WO2019090874A1 - 一种用于货物取送的单车调度方法 - Google Patents

一种用于货物取送的单车调度方法 Download PDF

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WO2019090874A1
WO2019090874A1 PCT/CN2017/114561 CN2017114561W WO2019090874A1 WO 2019090874 A1 WO2019090874 A1 WO 2019090874A1 CN 2017114561 W CN2017114561 W CN 2017114561W WO 2019090874 A1 WO2019090874 A1 WO 2019090874A1
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task
constraint
delivery
shipment
vehicle
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French (fr)
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牟峰
夏梅宸
刘兴伟
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西华大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping

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  • the invention relates to the field of intelligent dispatching technology, in particular to a bicycle scheduling method for goods picking and delivery.
  • Cargo delivery exists in many areas of the social and economic system, such as urban logistics distribution systems, sorting systems, container automated packing systems, and railway station pick-and-place systems.
  • urban logistics distribution systems sorting systems
  • container automated packing systems container automated packing systems
  • railway station pick-and-place systems With the advancement and development of technology, it will be oriented to many new application areas, such as drone distribution scheduling, unmanned distribution car scheduling and unmanned taxi scheduling.
  • drone distribution scheduling unmanned distribution car scheduling and unmanned taxi scheduling.
  • Vehicle scheduling methods are mainly divided into precise methods and heuristic methods.
  • the precise method is mainly used to find the analytical solution of the problem, including the branch pricing method, the column generation method, etc., which is characterized by converting the problem into a linear programming problem.
  • the heuristic method is a method based on intuitive experience. It can obtain satisfactory scheduling results on the basis of acceptable cost. Although it can not determine the degree of deviation between it and the optimal scheduling, it can better solve the accurate method.
  • the evolutionary mechanism of the ant colony algorithm is to simulate the ant foraging behavior
  • the evolutionary mechanism of the genetic algorithm is to simulate the gene crossover and mutation. More depends on the coding of the actual application scenario, and the design of a specific heuristic based on this coding method and system characteristics, in other words, it mainly depends on the heuristic method constructed for the feasible scheduling results.
  • an object of the present invention is to provide a bicycle scheduling method for cargo pick-up that can achieve the purpose of real-time and portability, thereby satisfying the use requirements in practical application scenarios and more efficient.
  • the technical solutions are as follows:
  • a bicycle scheduling method for cargo picking comprising the following steps:
  • Step 1 Obtain system characteristic data of the vehicle dispatching vehicle dispatching application scenario, including geographic information data, delivery vehicle information data, and shipment information data;
  • Step 2 Pre-process the system feature data of the vehicle dispatching vehicle dispatching application scenario, create a pick-up task, take the task as a basic unit, associate it with the system feature data, and then retrieve the system characteristic data of the problem. Processed in a form that meets the requirements for building a composite structure;
  • Step 3 Analyze the system characteristics of the vehicle dispatching application scenario, and create a constraint for constructing the combined structure and a method for constructing the combined structure according to the system characteristics, and classify according to the influence form of the combined structure, including Local combined structural constraints, global combined structural constraints, and super-combined structural constraints;
  • Step 4 Take the task as the basic structural unit of the combination structure of the delivery plan, adopt the way of constructing the feasible combination structure; the construction method of the feasible combination structure is based on the constraint condition of the combination structure and its classification, and randomly construct a sorting structure.
  • the sorting structure construction method based on the sorting structure constraint is used to locally adjust the current sorting structure to satisfy the constraint.
  • each grouping is constructed one by one by using the grouping structure constraint, and the whole structure constraint is detected after each group is created.
  • Step 5 Put step 4 in the meta heuristic method, iteratively evolve the scheme, and finally stop iteration according to the termination rule, and output the optimization scheme.
  • the geographic information data includes a warehouse location, a customer location, a customer location partition, a connection path between the locations, and an average travel speed information of each path arc;
  • the distributed vehicle information data includes the number of executable mission vehicles, and the vehicle The task time range information and model information can be executed;
  • the shipment information data includes the shipment volume of the shipment, the pick-up start point and the end position of the shipment, the time window of the shipment at its start and end positions, and the customer's place. The time of intermediate processing.
  • step 2 specifically includes:
  • Step 21 Assign an index to each location involved in the cargo dispatch vehicle scheduling application scenario, assign the index of the warehouse to 0, assign the customer index from 1; and assign an index to each shipment included in the delivery problem. ; assign an index to each vehicle that performs the pick-up task;
  • Step 22 Create a delivery task for each shipment according to the corresponding starting and ending location data of the shipment, including the delivery type sent from the warehouse to the customer, and retrieve the retrieval type of the warehouse from the customer.
  • Step 23 Create a known data variable that satisfies the construction of the combined structure based on the feature data in the cargo dispatch vehicle scheduling application scenario and the created pick-up task.
  • step 23 specifically includes:
  • Step 231 Create a retrieval task and an associated variable c i of the delivery item; wherein i is an index of the delivery task, and c i is a shipment corresponding to the delivery task i;
  • Step 232 Create associated tasks o i and d i of the fetch task and its start and end positions; where o i is the starting position index of the fetch task i, and d i is the end position index of the fetch task i;
  • Step 234 Create a time window variable of the fetch task at the start and end positions with among them, with The earliest and latest time at which the task i can perform the task at its starting position o i , with The earliest and latest time at which the task i can perform the task at its end position d i ; or Pick up task, there is or For the latest time not involved or Pick up task, there is or Where M is a sufficiently large positive number;
  • Step 235 Create a collection of each type of delivery task, including a delivery type task set N - , a pickup type task set N + , a last item delivery task set N E of all shipments, and a transfer delivery task set And transfer pickup task collection
  • the delivery type task set N - is a collection of all delivery tasks and transfer delivery tasks
  • the pickup type task set N + is a collection of all the pickup tasks and the transfer pickup tasks;
  • Step 236 Create an associated variable w i of the delivery task and its transportation volume; wherein, w i is the transportation volume of the delivery item taken by the delivery task i;
  • Step 238 Create an associated variable T i of the intermediate processing time of the delivery task and its corresponding shipment at the customer, where i is an index of the delivery task, and T i is the delivery corresponding to the delivery task i at the customer.
  • Intermediate processing time i is an index of the delivery task, and T i is the delivery corresponding to the delivery task i at the customer.
  • Step 239 Create an associated variable Q of the vehicle performing the fetching task and its maximum load, where Q is the maximum load of the vehicle.
  • step 3 specifically includes:
  • Step 31 Create a local composite structure constraint, including a sort structure constraint and a group structure constraint, and the constraint can be satisfied only by changing a partial combination structure under the condition that other local combination structures are fixed;
  • Step 32 Create an overall combination structure constraint that needs to be changed to meet a plurality of partial combination structures; establish an upper bound constraint of the shipment pick-up time window, and classify it as an overall combined structure constraint, and the upper bound of the shipment pick-up time window It is required that the execution time of each delivery task does not exceed the upper bound of the shipment delivery time window;
  • the composite structure construction method based on the upper bound of the shipment delivery time window is specifically: the maximum grouping range according to external input [k + , k - ], that is, the execution sequence number of the first and last two retrieval tasks in the current group under the current sorting structure, and the task is taken from the first item Initially, the retrieval task i r in the current group is retrieved in ascending order of the sequence number in the sorting structure;
  • Step 33 Create a super-combination structural constraint that guarantees the uniqueness of the delivery scheme under the fixed combination structure, including the lower bound of the shipment pick-up time window, the processing completion time constraint of the shipment, and the departure customer time constraint.
  • step 31 specifically includes:
  • Step 311 Create a sorting structure constraint by: a task that objectively has a priority relationship between the picking and receiving tasks of the same shipment, constrains the sorting structure of the picking task, and establishes a delivery priority constraint of the same shipment. And classify it into the sorting structure constraint; the fetching priority constraint of the same shipment requires that in the sorting structure of the fetching task, the execution order of the fetching task of the same shipment satisfies the sending task>transfer picking task> The transfer task task> the pick-up task; the sorting structure construction method based on the fetch priority order constraint of the same shipment is specifically: when a fetch task of a certain shipment c i exists in a fetch task sequence does not satisfy the priority order constraint At the same time, the local adjustment of the sorting structure is performed by means of sequential interchange, so that the fetching task sequence satisfies the fetching priority constraint of the same shipment;
  • Step 312 Define a fetch task group as a set of fetching tasks performed by the vehicle from the warehouse and back to the warehouse, and create a grouping structure constraint that can be satisfied only by changing the packet structure of the fetching task.
  • step 312 specifically includes:
  • Step 3121 Establish different group constraint of different partitioning and picking task, and classify it into a grouping structure constraint; different grouping and forwarding tasks have different group constraints required in the group of the sending task, when the two adjacent orders are taken When there is no direct path between the customer locations corresponding to the sending task, the two receiving tasks are sequentially assigned to the adjacent two groups; the grouping structure construction method based on the different group constraints of the different partitioning and sending tasks is: Construct a group under the fixed sorting structure, according to the maximum grouping range [k + , k - ] of the external input, that is, the execution sequence number of the first and last two items in the current group under the current sorting structure, from the first item Send task Initially, the position of the direct client corresponding to the two consecutive delivery tasks i r and i r+1 in the current group is retrieved in ascending order of the sequence number in the sorting structure.
  • i r is taken as the last item of the current group, and the sequence number of the last item of the current group is updated to r, and the execution sequence number of the last item in the current group is returned.
  • i r is the index of the f-th task in the current fixed sort structure
  • Step 3122 Establish vehicle capability constraints and classify them into group structure constraints; vehicle capability constraints require that the vehicle load does not exceed its maximum load during the execution of each group's fetching task under a fixed ordering structure.
  • step 3122 specifically includes:
  • Step 31221 Establish a vehicle origination capability constraint; the vehicle origination capability constraint requires that the total number of shipments sent by the delivery type task in the warehouse and the destination is not in the delivery task group under a fixed order structure. Greater than the maximum load of the task execution vehicle; the group structure construction method based on the vehicle origin capability constraint is: for constructing a group under a fixed sorting structure, according to the maximum grouping range [k + , k - ] of the external input, That is, the execution sequence number of the first and last two retrieval tasks in the current group under the current sorting structure, and the task is taken from the first item.
  • the retrieval task i r in the current group is retrieved in ascending order according to the sequence number in the sorting structure, and the shipment amount of the shipments of the delivery type task whose origin is the warehouse and the destination is the customer location is accumulated, if it appears And Then the task i k is taken as the last item of the current group, and will As the starting load of the vehicle, the execution sequence number of the last item of the current grouping task i k is updated to k, where k+1 ⁇ k - , i r is the r item in the current fixed sorting structure
  • the index of the task, ⁇ i is a symbol variable. When the task i is a delivery type task, ⁇ i takes a value of -1; for a pickup type task, ⁇ i takes a value of 1;
  • Step 31222 Establish a vehicle in-transit capability constraint; the vehicle in-transit capability constraint requires that in the group of the delivery task, the load after the vehicle performs each retrieval task is not greater than the maximum load of the vehicle; and the group structure based on the vehicle's in-transit capability constraint is constructed.
  • the method is: constructing a group under a fixed sorting structure, according to the maximum grouping range [k + , k - ], that is, the execution sequence number of the first and last two items in the current group under the current sorting structure, Pick up the task from the first item Initially, the retrieval task i r in the current group is retrieved in ascending order of the sequence number in the sorting structure, using the formula Calculate the load after the current fetch task i k is executed.
  • ⁇ i is a symbol variable, and when the task i is a delivery type task, ⁇ i takes the value -1 ; for the pickup type task, ⁇ i takes a value of 1; The value of the value, update the vehicle originating load q 0 and the execution order number k - of the last item of the current group.
  • step 31222 further includes:
  • Step 312221 If q ik >Q, go to step 312223, otherwise go to the next step;
  • Step 312223 retrieve the fetch task i r corresponding to the execution sequence r in descending order within the range (k, k - ], and if there is no delivery type task i r whose starting point is the warehouse and the end point is the customer location, then the fetch will be sent.
  • the task i k-1 is the last item of the current group, and updates the execution sequence number of the last item of the current group to k-1, and returns the execution sequence number k of the last item in the current group. -1;
  • Step 312224 retrieve the fetching task i r corresponding to the execution order r in the descending order of the range (k, k - ], and the first starting point is the transport corresponding to the delivery type task i r of the warehouse and the end point being the customer position.
  • the amount is deducted from q 0 , that is, The execution sequence number k - of the last item of the current packet is updated to r-1; q ik is calculated, and step 312221 is performed.
  • processing method of the fetching task i r in the step 32 includes:
  • Step 321 If or Go to step 323;
  • Step 324 Exchange the order of the fetch tasks i r ' and i r in the current sorting structure
  • Step 325 If the current order structure is in the shipment with The delivery task does not satisfy the delivery priority constraint of the same shipment, and the localization adjustment of the execution order of the delivery task is performed by using a ranking structure construction method based on the delivery priority of the same shipment;
  • step 33 specifically includes:
  • Step 331 Establish a lower bound of the shipment pick-up time window and classify it as a super-combination structure constraint; a lower bound constraint of the shipment pick-up time window requires the start time of the vehicle to reach the pick-up task i When the vehicle has to wait until the time After that, the fetching task i is started, when the vehicle reaches the end time of the fetching task i When the vehicle has to wait until time After that, the execution of the retrieval task i is started;
  • Step 332 Establish a mid-machine processing completion time constraint and classify it into a super-combination structure constraint; the shipment intermediate processing completion time constraint requires that the delivery type task i delivers the shipment c i to the customer l i , After the intermediate processing of the time T i is taken out by the pickup type task j and sent to another location, if the vehicle reaches the start time of the pickup type task j Vehicles have to wait until time After that, the task j is started;
  • Step 333 establishing customer departure time constraints, and classified to a combination of ultra-structural constraints; departure time constraints customer requirements for submittal type task i, c i when the shipment is sent to the customer L i, of the vehicle from the client
  • ta i and tl i are the time when the vehicle arrives and leaves l i , respectively.
  • Vehicle may be the earliest time of the client into the shipment at the l i, th i intermediate processing completion time of shipment of c i.
  • step 4 specifically includes:
  • Step 41 randomly generate an ordered sequence seq that takes the task as a basic unit
  • Step 42 Perform a local adjustment of the execution order of the fetching operation in the sequence seq using the sorting structure construction method based on the fetching priority constraint of the same shipment, and the adjusted sequence seq satisfies the fetching priority constraint of the same shipment;
  • Step 43 Construct each packet in turn and make local adjustments based on the overall combined structure constraints.
  • step 43 specifically includes:
  • Step 432 According to with The determined maximum packet range, using the packet structure construction method based on the different group constraints of the different partition fetch task, updating the sequence number of the last item fetching task of the group g in the current sequence seq in the sequence seq
  • Step 433 According to with The determined maximum packet range, using the packet structure construction method based on the vehicle origin capability constraint to update the sequence number of the last item of the packet g in the sequence seq in the current sequence seq The sequence number of the last item fetching task of the group g in the sequence seq in the current sequence seq is updated using the packet structure construction method based on the vehicle in-transit capability constraint
  • Step 434 According to with Determine the maximum grouping range, use the combined structure construction method based on the upper bound constraint of the shipment pick-up time window to locally adjust the local combination structure of the group that does not satisfy the upper bound of the delivery time window of the shipment, and perform corresponding operations according to the returned result. .
  • step 434 specifically includes:
  • the invention has the beneficial effects that the present invention is directed to the development status and existing problems of the cargo pick-up vehicle dispatching method, and achieves the purpose of real-time and portability, thereby satisfying the use requirements in practical application scenarios, and greatly mentions the goods.
  • the efficiency of the delivery must be very practical.
  • FIG. 1 is a flow chart of a bicycle scheduling method for cargo pick-up according to the present invention.
  • FIG. 2 is a schematic diagram of a method for constructing a ranking structure based on a delivery priority order constraint of the same shipment according to the present invention.
  • FIG. 3 is a schematic diagram of a method for constructing a packet structure based on different group constraints of different partitioned fetch tasks according to the present invention.
  • FIG. 4 is a schematic diagram of a method for constructing a packet structure based on a vehicle origination capability constraint according to the present invention.
  • FIG. 5 is a schematic diagram of a method for constructing a packet structure based on a vehicle in-transit capability constraint according to the present invention.
  • Figure 6 is a schematic diagram of the implementation of the combined structure construction method based on the upper bound of the shipment pick-up time window
  • Figure 7.1 is a schematic diagram of an ordered sequence of randomly generated pick-and-drop tasks in the combined structure construction method of the cargo pick-up bicycle scheduling plan of the present invention.
  • Figure 7.2 is a schematic diagram of a method for constructing a ranking structure based on the same priority of the delivery of the same shipment in the combined structure construction method of the cargo delivery bicycle scheduling plan of the present invention.
  • Figure 7.3 is a schematic diagram of the grouping 0 of the task sequence seq in the combined structure construction method of the cargo pick-up bicycle scheduling plan of the present invention.
  • Figure 7.4 is a schematic diagram of the packet 1 of the task sequence seq in the combined structure construction method of the cargo pick-up bicycle scheduling plan of the present invention.
  • Figure 7.5 is a schematic diagram of the grouping 2 of the task sequence seq in the combined structure construction method of the cargo pick-up bicycle scheduling plan of the present invention.
  • Figure 7.6 is a schematic diagram of the final feasible combination structure in the combined structure construction method of the cargo pick-up bicycle scheduling plan of the present invention.
  • the invention discloses a bicycle scheduling method for goods picking, and the technical solution comprises the following steps:
  • Step 1 Obtain system characteristic data of the vehicle dispatching vehicle dispatching application scenario, including geographic information, delivery vehicle information, shipment information, and customer warehouse capacity information.
  • the geographic information data includes warehouse location, customer location, customer location partition, and location.
  • the connection path between each path and the average travel speed information of each path arc segment, the distribution vehicle information data includes the number of executable task vehicles, the executable task time range information of the vehicle, the vehicle type information, the shipment information data including the shipment quantity of the shipment, and the goods.
  • Step 2 Pre-processing the system feature data of the vehicle dispatching application scenario, creating a pick-up task, taking the task as a basic unit, associating it with the system feature data, and then taking the system characteristic data of the problem
  • the processing is in a form that conforms to the requirements of the combined structure construction.
  • Step 21 Assign an index to each location involved in the vehicle delivery vehicle scheduling application scenario, assign the index of the warehouse to 0, assign the index of the customer from 1; and assign an index to each shipment included in the delivery problem. ; assign an index to each vehicle that performs the pick-up task;
  • Step 22 Create a delivery task for each shipment according to the data of the delivery start point and the end position corresponding to the shipment, including the delivery type sent from the warehouse to the customer, and retrieve the retrieval type of the warehouse from the customer.
  • the type of transfer that the customer takes out and sends to another customer where the transfer task is split into a transfer pick-up task and a transfer send task, and the transfer pick-up task refers to the transfer from the starting point customer in the transfer task Shipment, transfer delivery refers to the task of sending the goods taken from the originating customer to the destination customer; assigning a unique index to each delivery task;
  • Step 23 Create a known data variable that meets the structure of the combined structure based on the feature data in the vehicle dispatching application scenario and the created pick-up task, including:
  • Step 231 Create a delivery task and an associated variable c i of the delivery item, where i is an index of the delivery task, and c i is a shipment corresponding to the delivery task i;
  • Step 232 Create a retrieval task and associated variables o i and d i of the starting point and the ending position, where i is an index of the fetching task, o i is a starting position index of the fetching task i, and d i is a fetching task The end position index of i;
  • Step 234 Create a time window variable of the fetching task at the start and end positions with Where i is the index of the fetching task, with The earliest and latest time at which the task i can perform the task at its starting position o i , with The earliest and latest time at which the task i can perform the task at its end position d i ; or Pick up task, there is or For the latest time not involved or Pick up task, there is or Where M is a sufficiently large positive number;
  • Step 235 Create a collection of each type of delivery task, including a delivery type task set N - , a pickup type task set N + , a last item delivery task set N E of all shipments, and a transfer delivery task set. And transfer pickup task collection Etc., where the delivery type task set N - is a collection of all delivery tasks and transfer delivery tasks, the pickup type task set N + is a collection of all pickup tasks and transfer pickup tasks;
  • Step 236 Create an associated variable w i of the fetching task and its traffic volume, where i is an index of the fetching task, and w i is a traffic volume of the delivery piece taken by the fetching task i;
  • Step 238 In the vehicle dispatching vehicle dispatching application scenario, there is a case where the shipment is sent to the customer for intermediate processing, and then taken out to another location, thereby creating a delivery task and its corresponding shipment at the customer.
  • the associated variable T i of the intermediate processing time where i is the index of the fetching task, and T i is the intermediate processing time of the shipment corresponding to the fetching task i at the customer;
  • Step 239 Create a vehicle that performs the fetching task and an associated variable Q of the maximum load thereof, where Q is the maximum load of the vehicle.
  • Step 3 Analyze the system characteristics of the vehicle dispatching application scenario, and create a constraint for constructing the combined structure and a method for constructing the combined structure according to the system characteristics, and classify according to their influence forms on the combined structure, including Local composite structural constraints, global composite structural constraints, and super-combined structural constraints.
  • Step 31 Creating a local combination structure constraint, including a sort structure constraint and a group structure constraint, and the constraint that can be satisfied only by changing a local combination structure under the condition that other local combination structures are fixed, including:
  • Step 311 The sorting structure is a structured expression of the execution order of the fetching task, which can be expressed by an ordered sequence composed of a fetching task, and the sorting structure constraint is a constraint that can be satisfied only by changing the sorting structure of the fetching task.
  • Create a sort structure constraint including: when the shipment in the pick-up question has to be fetched multiple times, since the shipment can only be picked up from the location and sent to another location after the shipment is sent to a location, There is an objective prioritized relationship between the delivery tasks of the same shipment. To satisfy this prioritization, only the ordering structure of the delivery task must be constrained.
  • the delivery priority constraint of the same shipment is established, and It is classified into the sorting structure constraint;
  • the fetching priority constraint of the same shipment requires that in the sorting structure of the fetching task, the execution order of the fetching task of the same shipment satisfies the sending task>transfer picking task>transfer sending piece Task>Retrieve task, where the symbol> is the symbol of the priority of the same shipment immediately adjacent to the delivery task, indicating that the former takes precedence over the latter in the execution order; based on the same shipment
  • the interchangeable sequential manner local adjustment structure sorted sorted, so to take delivery The task sequence satisfies the delivery priority constraint of the same shipment;
  • Step 312 Define a fetch task group as a set of fetching tasks performed by the vehicle in the process of departing from the warehouse and returning to the warehouse.
  • the grouping structure is a grouped number and a structured expression of the fetching task included in each group, creating only The grouping structure constraints that can be satisfied by changing the packet structure of the fetch task, including:
  • Step 3121 The partition management is performed on the customer according to the actual needs of the delivery system, so that the vehicle performing the delivery task does not perform the same group delivery task across the area, that is, the delivery tasks of different partition customers are not in the same group, and are sent in the same group.
  • the problem there is no direct path between customers in different partitions. For this reason, different group constraints of different partitions are taken and classified into group structure constraints; different grouping tasks are required for different group constraints.
  • the two retrieval tasks are sequentially assigned to the adjacent two groups; based on the different partitions
  • a packet structure construction method for fetching different group constraints of a task is used to construct a group under a fixed sorting structure, according to the maximum grouping range [k + , k - ] of external input, that is, the first and last two in the current group
  • the execution sequence number of the task under the current sorting structure, and the task is taken from the first item.
  • the position of the direct client corresponding to the two consecutive delivery tasks i r and i r+1 in the current group is retrieved in ascending order of the sequence number in the sorting structure.
  • i r is taken as the last item of the current group, and the sequence number of the last item of the current group is updated to r, and the execution sequence number of the last item in the current group is returned.
  • i r is the index of the f-th task in the current fixed sort structure
  • Step 3122 The vehicle that performs the fetching task has a limit of the maximum loading amount.
  • the delivery item to be fetched is sufficient, it cannot complete all the fetching tasks at one time due to the limitation of the loading capacity of the vehicle, so the fetching task must be assigned to different groupings. And complete the fetching task of each group one by one.
  • the vehicle capability constraint is established and classified into the group structure constraint; the vehicle capability constraint requires that each group's fetching task process be performed under a fixed ordering structure.
  • the vehicle load does not exceed its maximum load, including:
  • Step 31221 The initial load of the vehicle must satisfy the vehicle capability constraint.
  • the vehicle origination capability constraint is established; the vehicle origination capability constraint is required to be in the delivery task group under a fixed ordering structure, and the starting point is the warehouse and the destination point is The total amount of shipments sent by the customer's delivery type task is not greater than the maximum load of the task execution vehicle; the group structure construction method based on the vehicle origination capability constraint is used to construct a group under a fixed sorting structure, according to the external The maximum grouping range [k + , k - ] entered, that is, the execution sequence number of the first and last two items in the current group under the current sorting structure, and the task is taken from the first item.
  • the retrieval task i r in the current group is retrieved in ascending order according to the sequence number in the sorting structure, and the shipment amount of the shipments of the delivery type task whose origin is the warehouse and the destination is the customer location is accumulated, if it appears And Then the task i k is taken as the last item of the current group, and will As the starting load of the vehicle, the execution sequence number of the last item of the current grouping task i k is updated to k, where k+1 ⁇ k - , i r is the r item in the current fixed sorting structure
  • the index of the task, ⁇ i is a symbol variable. When the task i is a delivery type task, ⁇ i takes a value of -1, and for a pickup type task, ⁇ i takes a value of 1;
  • Step 31222 Since the vehicle load is reduced after the delivery type task is performed, the vehicle load increases after the retrieval type task is performed, and the above two types of tasks can occur simultaneously in one group, so a group delivery task process is performed.
  • the load of the vehicle is dynamically changed. At this time, the load of the vehicle is required to satisfy the vehicle capacity constraint after performing any one of the current dispatching tasks. To this end, the vehicle's in-transit capability constraint is established; the vehicle's in-transit capability constraint is required to be taken in the task.
  • the load after the vehicle performs each feeding task is not greater than the maximum loading of the vehicle; the grouping structure construction method based on the vehicle in-transit capability constraint is used to construct a group under a fixed sorting structure according to the maximum grouping range [ k + , k - ], that is, the execution sequence number of the first and last two retrieval tasks in the current group under the current sorting structure, and the task is taken from the first item Initially, the retrieval task i r in the current group is retrieved in ascending order of the sequence number in the sorting structure, using the formula Calculate the load after the current fetch task i k is executed.
  • ⁇ i is the index of the r- th task in the current fixed-sort structure
  • ⁇ i is a symbol variable
  • ⁇ i is -1 when the task i is sent as the task of the delivery type
  • ⁇ i takes a value of 1, according to The value of the value, update the vehicle originating load q 0 and the execution order number k - of the last item of the current grouping, including:
  • Step 312223 retrieve the fetch task i r corresponding to the execution sequence r in the descending order within the range (k, k - ). If there is no delivery type task i r whose starting point is the warehouse and the end point is the customer location, then the fetching task i r will be sent.
  • the task i k-1 is the last item of the current group, and updates the execution sequence number of the last item of the current group to k-1, and returns the execution sequence number k of the last item in the current group. -1;
  • Step 312224 in the range (k, k - ), retrieve the retrieval task i r corresponding to the execution sequence r in descending order, and transport the first occurrence starting point to the delivery type task i r of the warehouse and the destination location
  • the amount is deducted from q 0 , that is, The order of execution of the task to take the last item of the current packet send sequence number k - is updated to r-1; calculated Go to step 312221;
  • Step 32 Create an overall combined structure constraint that can be changed by changing a plurality of local combination structures; the latest time when the task is fetched and the task i is limited to start the task And the latest time at the end of the mission
  • the starting time ts i is greater than Or when the vehicle reaches transferring a task i is greater than the end time TE i
  • the upper bound of the shipment picking time window is established and classified into the overall combined structure constraint; the shipment picking time window
  • the upper bound constraint requires that the execution time of each item of the delivery does not exceed the upper bound of the delivery time window; the combined structure construction method based on the upper bound of the shipment pick-up time window is used to satisfy the delivery of the shipment in the group.
  • the local combination structure of the upper bound of the time window is locally adjusted according to the maximum grouping range [k + , k - ] of the external input, that is, the execution sequence number of the first and last two retrieval tasks in the current group under the current sorting structure, Pick up the task from the first item Initially, the retrieval task i r in the current group is retrieved in ascending order of the sequence number in the sorting structure, and the processing method for the retrieval task i r includes:
  • Step 321 if or Go to step 333;
  • Step 324 exchanging the order of the fetch tasks i r ' and i r in the current sorting structure
  • Step 325 if the current ordering structure in the shipment with If the picking task does not satisfy the fetching priority constraint of the same shipment, the ordering structure construction method based on the fetching priority constraint of the same shipment is used to perform local adjustment of the fetching task execution order;
  • Step 33 Create a super-combination structure constraint that guarantees the uniqueness of the fetching scheme under the fixed combination structure, which is an agreement on the execution manner of the fetching task on the basis of the combined structure, including the lower bound constraint of the delivery time window of the shipment.
  • Intermediate processing completion time constraints and departure time constraints, etc. including:
  • Step 331 The earliest time when the pick-up task i is used to limit the start of the task in the vehicle dispatching application scheduling scenario And the earliest time at the end of the mission In combination with the objective reality, the vehicle is allowed to arrive at the corresponding place in advance to wait.
  • the lower bound of the shipment pick-up time window is established and classified as a super-combination structure constraint; the lower bound constraint of the shipment pick-up time window is required when the vehicle arrives Start time to send task i When the vehicle has to wait until the time After that, the fetching task i is started, when the vehicle reaches the end time of the fetching task i When the vehicle has to wait until the time After that, the execution of the retrieval task i is started;
  • Step 332 The delivery problem is that the shipment is sent to the customer for intermediate processing operations such as packaging, and the intermediate processed shipment is taken out by the vehicle and sent to another location, and the vehicle is allowed to arrive in advance according to the objective reality. Waiting for the corresponding location, for this purpose, establish the intermediate processing completion time constraint of the shipment and classify it into the super-combination structure constraint; the intermediate processing completion time constraint of the shipment requires that the delivery type task i send the shipment c i to the customer At l i , after intermediate processing with a duration of T i , it is taken out by the pickup type task j and sent to another location, if the vehicle reaches the start time of the pickup type task j The vehicle has to wait until the time After that, the task j is started;
  • Step 333 in order to make the execution of the pick-up task as efficient as possible, the vehicle is required to leave the current customer as early as possible to perform the next pick-up task.
  • the departure time constraint is established and classified as a super-combination. structural constraint; departure time constraints customer requirements for submittal type task i, c i when the shipment is sent to the customer L i, the time of the vehicle away from the customer for the l i
  • the time the vehicle leaves from the customer l i is
  • ta i and tl i are the time when the vehicle arrives and leaves l i , respectively.
  • Vehicle may be the earliest time of the client into the shipment at the l i, th i intermediate processing completion time of shipment of c i.
  • Step 4 taking the task as the basic structural unit of the fetching scheme combination structure, because of the existence of the super-combination structure constraint, each fetching scheme uniquely corresponds to one combined structure, so when the combined structure is determined, the fetching scheme is also determined. Therefore, the construction of feasible combination structure is adopted to replace the construction of feasible scheme.
  • the construction method of feasible combination structure is based on the constraint condition and classification of composite structure, constructing a sorting structure randomly, and using the sorting structure based on sorting structure constraint The current sorting structure is locally adjusted to satisfy the constraint.
  • each grouping is constructed one by one by using the grouping structure constraint.
  • the overall structure constraint is used for detection and local combination structure adjustment, if the combined structure is constructed. In the process, there is a partial adjustment of the structure that does not satisfy the overall structural constraints and cannot be based on the overall structural constraints. Then, the construction method of the feasible combined structure is repeated until a complete and feasible combined structure is obtained. Specifically include:
  • Step 41 randomly generating an ordered sequence seq that takes the task as a basic unit
  • Step 42 Perform a local adjustment of the execution order of the fetching operation in the sequence seq by using a sorting structure construction method based on the fetching priority constraint of the same shipment, and the adjusted sequence seq satisfies the fetching priority constraint of the same shipment;
  • Step 43 Construct each group in turn and perform local adjustment based on the overall combined structure constraint, including:
  • Step 432 according to with The determined maximum packet range, using the packet structure construction method based on the different group constraints of the different partition fetch task, updating the sequence number of the last item fetching task of the group g in the current sequence seq in the sequence seq
  • Step 433 according to with The determined maximum packet range, using the packet structure construction method based on the vehicle origin capability constraint to update the sequence number of the last item of the packet g in the sequence seq in the current sequence seq
  • the sequence number of the last item fetching task of the group g in the sequence seq in the current sequence seq is updated using the packet structure construction method based on the vehicle in-transit capability constraint
  • Step 434 uses the combined structure construction method based on the upper bound constraint of the shipment pick-up time window to locally adjust the local combination structure of the group that does not satisfy the upper bound of the delivery time window of the shipment, and perform corresponding operations according to the returned result.
  • Step 5 Step 4 is placed on a meta heuristic method such as an ant colony algorithm, and the scheme is iteratively evolved, and finally the iteration is stopped according to the termination rule, and the optimization scheme is output.
  • a meta heuristic method such as an ant colony algorithm
  • the bicycle scheduling method for cargo picking, the step 4 further includes:
  • Step 1 Obtain system characteristic data in the vehicle dispatching application scenario, including geographic information, delivery vehicle information, shipment information, and customer warehouse capacity information.
  • the geographic information data includes warehouse location, customer location, customer location partition, and location.
  • the connection route, the average travel speed information of each path arc, and the like, the distribution vehicle information data includes the number of executable task vehicles, the executable task time range information of the vehicle, the vehicle type information, and the like, and the shipment information data includes the shipment amount of the shipment. , the corresponding starting point and ending position of the shipment, the time window of the shipment at its starting point and ending position, the time of intermediate processing at the customer, etc.;
  • Step 2 Pre-processing the system feature data in the vehicle dispatching application scenario, creating a pick-up task, taking the task as a basic unit, associating it with the system feature data, and then taking the system characteristic data of the problem Processing as In accordance with the form of the construction requirements of the combined structure;
  • the customer location, the customer location partition and the connection path information between the locations, the path length td od between the locations is calculated, and the average traveling speed information of each arc segment is combined with the average traveling speed information of each arc segment to calculate the distribution vehicle in each path.
  • the travel time t od on the above , the data in the embodiment is shown in Table 1; the distribution vehicle information is arranged, and the embodiment is a bicycle problem.
  • the maximum load Q of the vehicle is assumed to be 15 unit equivalents, and the executable task time range is [0, ⁇ ); performing data preprocessing, creating each fetching task i, fetching the task and its associated variable c i , taking the task and its associated variables o i and d i , Send the task and its associated variable l i directly at the location of the customer, and take the time window variable of the task at the start and end positions with
  • the associated variable w i of the task and its transportation volume, the associated variable T i of the intermediate processing time of the delivery task and its corresponding shipment at the customer, the data in the embodiment is shown in Table 2; Collection, including the delivery type task set N - , the pickup type task set N + , the last item of all shipments to the task set N E , the transfer delivery task set And transfer pickup task collection
  • Table 3 Collection
  • Step 3 Analyze and analyze the system characteristics in the vehicle dispatching application scenario, and create constraints for constructing the combined structure and methods for constructing the combined structure according to the system characteristics, and classify according to their influence forms on the combined structure. Including local combined structural constraints, integral combined structural constraints and super-combined structural constraints;
  • Empty packet identifier Indicates that the packet identifier can be inserted here; in Figure 2, seq 0 is a randomly generated sequence of fetching tasks, where fetch tasks 2 and 3 do not satisfy the fetch priority constraint of the same shipment, so The order structure construction method based on the fetching priority order constraint of the same shipment performs the order of execution of the fetching task, and obtains the ordered sequence seq of the fetching task.
  • the constraint is decomposed into the vehicle origin capability constraint and the vehicle in-transit capability constraint; the vehicle origination capability constraint requires the delivery task group in a fixed sorting structure, and the starting point is the warehouse and the destination is the customer's delivery type task. The total amount is not greater than the maximum load of the task execution vehicle.
  • a packet structure construction method based on the vehicle origin capability constraint is constructed. As shown in FIG. 4, the maximum grouping range [k + according to external input in seq 0 is shown.
  • the task i 5 1 will be taken as the last item of the current group, and will be As the starting load of the vehicle, and updating the execution sequence number k - of the last item of the current packet to 5, at this time, a new packet identifier as shown in the sequence seq is obtained;
  • the load after the vehicle performs each picking task is not greater than the maximum load of the vehicle.
  • a packet structure construction method based on the vehicle's in-transit capability constraint is constructed, as shown in FIG. 5, in seq 0 .
  • the execution sequence number k - is updated to 4, at which point a new packet identifier as shown by the sequence seq is obtained.
  • Table 4 shows part of the vehicle time information of seq 0 in Figure 6.
  • Step 4 taking the task as the basic structural unit of the fetching scheme combination structure, because of the existence of the super-combination structure constraint, each fetching scheme uniquely corresponds to one combined structure, so when the combined structure is determined, the fetching scheme is also determined. Therefore, the construction of feasible combination structure is used to replace the construction of feasible scheme.
  • the construction method of feasible combination structure is based on the constraint condition and classification of composite structure, constructing a sorting structure randomly, and constructing the sorting structure based on sorting structure constraints. The method locally adjusts the current sorting structure to satisfy the constraint.
  • each group is constructed one by one by using the grouping structure constraint. After each group is created, the overall structure constraint is used for detection and local combination structure adjustment.
  • each group is constructed from the first group 0; the process of constructing each group is a group structure construction method based on different group constraints of different partitions to receive tasks, based on vehicle origin capability constraints
  • the group structure construction method, the group structure construction method based on the vehicle's in-transit capability constraint, and the combined structure construction method based on the upper bound of the shipment pick-up time window; assuming the initial standby time of the vehicle is 8:30, initializing the current grouping number g 0 , initializing the execution sequence number of the current packet first item and the last item retrieval task in the sequence seq with Construct a packet 0 of sequence seq, adding a packet identifier for packet 0 As shown in Figure 7.3; group 1 of the sequence seq is constructed, and the sequence seq adds a packet identifier for packet 1.
  • the group 2 of the sequence seq is constructed, and when the combined structure construction method based on the upper bound of the shipment fetch time window is applied, the execution of the fetch task 4 and the fetch task 3 in the current task sequence is performed. Subsequent interchange, get a new fetch task sequence seq and group identifier, as shown in Figure 7.5, according to the return value of the combined structure construction method based on the upper bound of the shipment fetch time window, the current grouping number g is taken The value is updated to 0, and the execution sequence number of the current packet first item and the last item retrieval task in the sequence seq is updated accordingly. with The current structure of the current task sequence seq is constructed, and finally a feasible task is obtained, as shown in Figure 7.6. The time information is shown in Table 5.
  • Step 5 Step 4 is placed on a meta heuristic method such as an ant colony algorithm, and the scheme is iteratively evolved, and finally the iteration is stopped according to the termination rule, and the optimization scheme is output.
  • a meta heuristic method such as an ant colony algorithm
  • Initializing related parameters in the meta heuristic method for example, in the ant colony algorithm, setting the pheromone matrix as the node for the fetching task and initializing the pheromone of each path arc into the same positive real number; applying step 4, where randomly generated
  • the ordered sequence of the fetching task can be performed according to the specific meta heuristic method adopted, such as the state transfer formula for each artificial ant in the ant colony algorithm.
  • ⁇ ij is the pheromone measure between the (i, j) arcs
  • ⁇ k (j) is provided by node j
  • Heuristic information (visualization information)
  • k(j) is the car group number corresponding to node j
  • ⁇ is the pheromone intensity factor
  • ⁇ is the heuristic information factor
  • parameter update is performed according to the parameter update mechanism of the meta heuristic method.

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Abstract

本发明公开一种用于货物取送的单车调度方法，获取货物取送车辆调度应用场景的系统特征数据，创建取送任务，将取送问题的系统特征数据处理为符合组合结构构建要求的形式；创建用于构建组合结构的约束及其构建组合结构的方法，并根据其对组合结构的影响形式进行归类，随机地构造一个排序结构，利用基于排序结构约束的排序结构构建方法对当前排序结构进行局部调整使其满足约束，在排序结构基础上利用分组结构约束逐一构建每个分组，并利用整体结构约束进行检测和局部的组合结构调整；将其置于元启发式方法，对方案进行迭代进化，根据终止规则停止迭代，输出优化的调度计划。本发明达到实时性和可移植性兼备的目的，满足在实际应用场景中的使用需求。

Description

一种用于货物取送的单车调度方法 技术领域
本发明涉及智能调度技术领域，具体为一种用于货物取送的单车调度方法。
背景技术
货物取送存在于社会经济系统的诸多领域，例如，城市物流配送系统、分拣系统、货柜自动化装箱系统和铁路车站取送调车系统等。特别地，随着科技的进步和发展，它将面向众多新的应用领域，如无人机配送调度、无人配送小车调度和无人驾驶出租车调度等。尽管该问题由来已久，且已有诸多较为成熟的方法，但大多倾向于理论研究，当具体到应用层面时，由于使用场景的特征各异，使得方法在效率或可移植性上受到诸多制约，进而使得货物取送的车辆调度方法被持续关注。
车辆调度方法主要分为精确方法和启发式方法。精确方法主要用于寻求问题的解析解，包括分枝定价法、列生成方法等，其特点在于将问题转换为线性规划问题进行求解。然而，当具体到应用场景时，往往呈现出大量的非线性特征，尽管通常能将非线性约束转化为线性约束，但这将不可避免地引入新的决策变量，进而增大搜索空间，使得调度方法的效率受到制约。启发式方法是一种基于直观经验的方法，它能在可接受的开销基础上得到满意的调度结果，尽管不能确定它与最优调度之间的偏离程度，但它可较好地解决精确方法中由于线性转换所产生的搜索空间过大问题，同时，更容易将人的直观经验和知识植入方法中，使得方法更为合理，此类方法包括蚁群算法、遗传算法、模拟退火算法和神经网络算法等，可见，启发式方法是一种更倾向于实时性的方法。尽管启发式方法在效率方面往往更具优势，但它无法避免对具体特征的依赖，这些特征由应用场景所决定，不同场景中系统特征的微小区别往往使得调度方法截然不同，这使得启发式方法的可扩展性受到制约，进而限制其适用范围。实践表明，启发式方法效率的高低并非完全取决于元启发式方法的迭代或进化机制，例如蚁群算法的进化机制为模拟蚂蚁觅食行为，遗传算法的进化机制为模拟基因交叉和变异，而更多取决于对实际应用场景的编码方式，以及基于这种编码方式和系统特征的特定启发式方法的设计，换言之，它主要取决于针对可行调度结果构建的启发式方法。
发明内容
针对上述问，本发明的目的在于提供能达到实时性和可移植性兼备的目的，从而满足在实际应用场景中的使用需求，且更为高效的用于货物取送的单车调度方法。技术方案如下：
一种用于货物取送的单车调度方法，包括以下步骤：
步骤1：获取货物取送车辆调度应用场景的系统特征数据，包括地理信息数据、配送车辆信息数据、货件信息数据；
步骤2：对货物取送车辆调度应用场景的系统特征数据进行预处理，创建取送任务，以取送任务为基本单元，将其与系统特征数据进行关联，进而将取送问题的系统特征数据处理为符合组合结构构建要求的形式；
步骤3：对货物取送车辆调度应用场景的系统特征进行分析，根据系统特征创建用于构建组合结构的约束及其构建组合结构的方法，并根据其对组合结构的影响形式进行归类，包括局部组合结构约束、整体组合结构约束和超组合结构约束；
步骤4：以取送任务作为取送方案组合结构的基本构造单位，采用构造可行组合结构的方式；可行组合结构的构造方法为基于组合结构的约束条件及其分类，随机地构造一个排序结构，利用基于排序结构约束的排序结构构建方法对当前排序结构进行局部调整使其满足约束，在排序结构基础上利用分组结构约束逐一构建每个分组，在每个分组创建完成后利用整体结构约束进行检测和局部的组合结构调整，若组合结构构造过程中存在不满足整体结构约束，并且不能基于整体结构约束的组合结构构建方法进行结构的局部调整，则重复执行可行组合结构的构造方法，直至得到一个完整的、可行的组合结构；
步骤5：将步骤4置于元启发式方法，对方案进行迭代进化，最终根据终止规则停止迭代，输出优化方案。
进一步的，所述地理信息数据包括仓库位置、客户位置、客户位置分区、各位置之间的连接路径、各路径弧段的平均行驶速度信息；配送车辆信息数据包括可执行任务车辆数、车辆的可执行任务时间范围信息、车型信息；货件信息数据包括货件的运输量、货件对应的取送起点和终点位置、货件在其起点和终点位置上的时间窗，以及在客户处进行中间加工的时间。
更进一步的，所述步骤2具体包括：
步骤21：对货物取送车辆调度应用场景中所涉及的每个位置赋予索引，将仓库的索引赋值为0，客户的索引从1开始赋值；对取送问题中包含的每个货件赋予索引；对执行取送任务的每个车辆赋予索引；
步骤22：根据货件所对应的取送起点和终点位置数据为每个货件创建取送任务，包括从仓库送至客户处的送件类型，从客户处取回仓库的取件类型，从客户处取出并送往另一客户处的调移类型；其中调移任务被拆分为调移取件任务和调移送件任务；为每项取送任务赋予唯一的索引；
步骤23：基于货物取送车辆调度应用场景中的特征数据和已创建的取送任务，创建满足组合结构构建的已知数据变量。
更进一步的，所述步骤23具体包括：
步骤231：创建取送任务及其取送货件的关联变量ci；其中，i为取送任务的索引，ci为取送任务i对应的货件；
步骤232：创建取送任务及其起点和终点位置的关联变量oi和di；其中，oi为取送任务i的起点位置索引，di为取送任务i的终点位置索引；
步骤233：创建取送任务及其直接关联客户所在位置的关联变量li；其中，li为取送任务i的直接关联客户的索引；对于取件类型任务i，有li＝oi；对于取件类型任务i，有li＝di；
步骤234：创建取送任务在起点和终点位置上的时间窗变量
Figure PCTCN2017114561-appb-000001
和
Figure PCTCN2017114561-appb-000002
其中，
Figure PCTCN2017114561-appb-000003
和
Figure PCTCN2017114561-appb-000004
分别为取送任务i在其起点位置oi上可执行任务的最早和最晚时间，
Figure PCTCN2017114561-appb-000005
和
Figure PCTCN2017114561-appb-000006
分别为取送任务i在其终点位置di上可执行任务的最早和最晚时间；对于不涉及最早时间
Figure PCTCN2017114561-appb-000007
或
Figure PCTCN2017114561-appb-000008
的取送任务，有
Figure PCTCN2017114561-appb-000009
或
Figure PCTCN2017114561-appb-000010
对于不涉及最晚时间
Figure PCTCN2017114561-appb-000011
或
Figure PCTCN2017114561-appb-000012
的取送任务，有
Figure PCTCN2017114561-appb-000013
或
Figure PCTCN2017114561-appb-000014
其中M为一个充分大的正数；
步骤235：创建各类型取送任务的集合，包括送件类型任务集合N-、取件类型任务集合N+，所有货件最后一项取送任务集合NE，调移送件任务集合
Figure PCTCN2017114561-appb-000015
和调移取件任务集合
Figure PCTCN2017114561-appb-000016
其中，送件类型任务集合N-为所有送件任务和调移送件任务的集合，取件类型任务集合N+为所有取件任务和调移取件任务的集合；
步骤236：创建取送任务及其运输量的关联变量wi；其中，wi为取送任务i所取送货件的运输量；
步骤237：创建包括仓库与客户，和客户与客户位置之间的距离变量tdll′，及行驶时间变量tll′；其中，l和l′分别为行驶路径弧段的起点和终点位置的索引，特别地，若两个位置之间无直接通路，则有tdll′＝M和tll′＝M，M为一个充分大的正数；
步骤238：创建取送任务及其对应货件在客户处的中间加工时间的关联变量Ti，其中，i为取送任务的索引，Ti为取送任务i所对应货件在客户处的中间加工时间；
步骤239：创建执行取送任务的车辆及其最大装载量的关联变量Q，其中，Q为车辆的最大装载量。
更进一步的，所述步骤3具体包括：
步骤31：创建局部组合结构约束，包括排序结构约束和分组结构约束，该约束在其他局部组合结构固定的条件下仅通过改变一种局部组合结构便可满足；
步骤32：创建需改变多种局部组合结构才能满足的整体组合结构约束；建立货件取送时间窗上界约束，并将其归类于整体组合结构约束，货件取送时间窗上界约束要求每项取送任务的执行时间都不超过货件取送时间窗上界；基于货件取送时间窗上界约束的组合结构构建方法具体为：根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000017
开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir；
步骤33：创建保证在固定组合结构下取送方案唯一性的超组合结构约束，包括货件取送时间窗下界约束，货件中间加工完成时间约束和离开客户时间约束。
更进一步的，所述步骤31具体包括：
步骤311：创建排序结构约束，方法为：对同一货件的取送任务之间客观上存在优先序关系的任务，对取送任务的排序结构加以约束，建立同一货件的取送优先序约束，并将其归类于排序结构约束；同一货件的取送优先序约束要求在取送任务的排序结构中，同一货件的取送任务执行顺序满足送件任务＞调移取件任务＞调移送件任务＞取件任务；基于同一货件的取送优先序约束的排序结构构建方法具体为：当一个取送任务序列中存在某个货件ci的取送任务不满足优先序约束时，采用顺序互换的方式进行排序结构的局部调整，使取送任务序列满足同一货件的取送优先序约束；
步骤312：将一个取送任务分组定义为车辆从仓库出发并回到仓库的过程中所执行的取送任务集合，创建仅通过改变取送任务的分组结构就能被满足的分组结构约束。
更进一步的，所述步骤312具体包括：
步骤3121：建立相异分区取送任务不同组约束，并将其归类于分组结构约束；相异分区取送任务不同组约束要求在取送任务的分组中，当顺序相邻的两项取送任务所对应的客户位置之间无直接通路时，将这两项取送任务依次分配至相邻的两个分组；基于相异分区取送任务不同组约束的分组结构构建方法为：在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000018
开始，依排序结构中顺序序号的升序检索当前分组中顺序相邻的两项取送任务ir和ir+1所对应的直接客户的位置
Figure PCTCN2017114561-appb-000019
和
Figure PCTCN2017114561-appb-000020
之间是否存在直接通路，其中r≥k+且 r+1≤k-，如果不存在直接通路，即
Figure PCTCN2017114561-appb-000021
则将取送任务ir作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为r，返回当前分组中末项取送任务的执行顺序序号r，其中，ir为在当前固定排序结构下第r项取送任务的索引；
步骤3122：建立车辆能力约束，并将其归类于分组结构约束；车辆能力约束要求在一个固定排序结构下执行每个分组的取送任务过程中，车辆负载不超过其最大装载量。
更进一步的，所述步骤3122具体包括：
步骤31221：建立车辆始发能力约束；车辆始发能力约束要求在一个固定排序结构下的取送任务分组中，起点为仓库和终点为客户的送件类型任务所运送货件的总量不大于任务执行车辆的最大装载量；基于车辆始发能力约束的分组结构构建方法为：用于在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000022
开始，依排序结构中顺序序号升序检索当前分组中的取送任务ir，对起点为仓库和终点为客户位置的送件类型任务所运送货件的运输量进行累加，若出现
Figure PCTCN2017114561-appb-000023
且
Figure PCTCN2017114561-appb-000024
则将取送任务ik作为当前分组的最后一项取送任务，将
Figure PCTCN2017114561-appb-000025
作为车辆始发负载量，并将当前分组的末项取送任务ik的执行顺序序号更新为k，其中，k+1≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时，σi取值为-1；为取件类型任务时，σi取值为1；
步骤31222、建立车辆在途能力约束；车辆在途能力约束要求在取送任务的分组中，车辆执行每项取送任务后的负载量不大于车辆的最大装载量；基于车辆在途能力约束的分组结构构建方法为：用于在一个固定的排序结构下构建一个分组，根据最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000026
开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，利用公式
Figure PCTCN2017114561-appb-000027
计算得到当前取送任务ik执行完毕后的负载量
Figure PCTCN2017114561-appb-000028
其中，k≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时，σi取值为-1；为取件类型任务时，σi取值为1；根据
Figure PCTCN2017114561-appb-000029
的取值情况，更新车辆始发负载量 q0和当前分组的末项取送任务的执行顺序序号k-。
更进一步的，所述步骤31222还包括：
步骤312221：如果qik＞Q，转步骤312223，否则进入下一步；
步骤312222：令k＝k+1，如果k≤k-，则计算qik，转步骤312221；否则，将取送任务
Figure PCTCN2017114561-appb-000030
作为当前分组的最后一项取送任务，返回当前分组中末项取送任务的执行顺序序号k-；
步骤312223：在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，如果不存在起点为仓库和终点为客户位置的送件类型任务ir，则将取送任务ik-1作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为k-1，返回当前分组中末项取送任务的执行顺序序号k-1；
步骤312224：在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，将首个出现的起点为仓库和终点为客户位置的送件类型任务ir所对应运输量从q0中扣除，即有
Figure PCTCN2017114561-appb-000031
将当前分组的末项取送任务的执行顺序序号k-更新为r-1；计算qik，转步骤312221。
更进一步的，所述步骤32中对取送任务ir的处理方法包括：
步骤321：如果
Figure PCTCN2017114561-appb-000032
或
Figure PCTCN2017114561-appb-000033
转步骤323；
步骤322：令r＝r+1，如果r≤k-，转步骤321；否则，将取送任务
Figure PCTCN2017114561-appb-000034
作为当前分组的最后一项取送任务，返回局部调整成功指示器LT＝true和分组序号指示器g＝-1，此处g取值为-1表示不需对已构建的排序结构和分组结构进行局部调整；
步骤323：在范围[0，r)内随机选择一个执行顺序r′，要求取送任务ir′和ir所对应的货件不同
Figure PCTCN2017114561-appb-000035
当
Figure PCTCN2017114561-appb-000036
时有
Figure PCTCN2017114561-appb-000037
当
Figure PCTCN2017114561-appb-000038
时有
Figure PCTCN2017114561-appb-000039
如果不存在这样的取送任务ir′，则不再基于当前已构建的局部组合结构继续进行后续操作，返回局部调整成功指示器LT＝false和分组序号指示器g＝-1；
步骤324：交换取送任务ir′和ir在当前排序结构中的顺序；
步骤325：若当前排序结构中货件
Figure PCTCN2017114561-appb-000040
和
Figure PCTCN2017114561-appb-000041
的取送任务不满足同一货件的取送优先序约束， 则采用基于同一货件的取送优先序约束的排序结构构建方法进行取送任务执行顺序的局部调整；
步骤326：返回局部调整成功指示器LT＝true和分组序号指示器g＝gr′，其中，gr′为执行顺序为r′的取送任务当前所在分组的分组序号。
更进一步的，所述步骤33具体包括：
步骤331：建立货件取送时间窗下界约束，并将其归类于超组合结构约束；货件取送时间窗下界约束要求当车辆到达取送任务i的起点时间
Figure PCTCN2017114561-appb-000042
时车辆需等待至时间
Figure PCTCN2017114561-appb-000043
之后开始执行取送任务i，当车辆到达取送任务i的终点时间
Figure PCTCN2017114561-appb-000044
时，车辆需等待至时间
Figure PCTCN2017114561-appb-000045
之后开始执行取送任务i；
步骤332：建立货件中间加工完成时间约束，并将其归类于超组合结构约束；货件中间加工完成时间约束要求当送件类型任务i将货件ci送至客户处li，经时长为Ti的中间加工后，由取件类型任务j取出送至另一地点，若车辆到达取件类型任务j起点的时间
Figure PCTCN2017114561-appb-000046
车辆需等待至时间
Figure PCTCN2017114561-appb-000047
之后开始执行任务j；
步骤333：建立离开客户时间约束，并将其归类于超组合结构约束；离开客户时间约束要求，对于送件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000048
对于取件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000049
其中，tai和tli分别为车辆到达和离开li的时间，
Figure PCTCN2017114561-appb-000050
为车辆可将货件送入客户处li的最早时间，thi为货件ci的中间加工完毕时间。
更进一步的，所述步骤4具体包括：
步骤41：随机生成一个以取送任务为基本单元的有序序列seq；
步骤42：使用基于同一货件的取送优先序约束的排序结构构建方法对序列seq中取送作业进行执行顺序的局部调整，调整后的序列seq满足同一货件的取送优先序约束；
步骤43：依次构造每个分组并基于整体组合结构约束进行局部调整。
更进一步的，所述步骤43具体包括：
步骤431：初始化当前分组序号g＝0，初始化当前分组首项和末项取送任务在序列seq中 的执行顺序序号
Figure PCTCN2017114561-appb-000051
和
Figure PCTCN2017114561-appb-000052
其中，n为取送任务总数；
步骤432：根据
Figure PCTCN2017114561-appb-000053
和
Figure PCTCN2017114561-appb-000054
确定的最大分组范围，使用基于相异分区取送任务不同组约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000055
步骤433：根据
Figure PCTCN2017114561-appb-000056
和
Figure PCTCN2017114561-appb-000057
确定的最大分组范围，使用基于车辆始发能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000058
使用基于车辆在途能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000059
步骤434：根据
Figure PCTCN2017114561-appb-000060
和
Figure PCTCN2017114561-appb-000061
确定的最大分组范围，使用基于货件取送时间窗上界约束的组合结构构建方法对分组中不满足货件取送时间窗上界约束的局部组合结构进行局部调整，根据返回结果进行相应操作。
更进一步的，所述步骤434具体包括：
步骤4341：如果局部调整成功指示器LTg＝false，则转步骤41；
步骤4342：如果基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器g′的取值为-1，则令g＝g+1、
Figure PCTCN2017114561-appb-000062
和
Figure PCTCN2017114561-appb-000063
转步骤432；
步骤4343：对于基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器的取值g′，如果g′≤1，则令g＝0、
Figure PCTCN2017114561-appb-000064
和
Figure PCTCN2017114561-appb-000065
否则，令g＝g′-1、
Figure PCTCN2017114561-appb-000066
和
Figure PCTCN2017114561-appb-000067
转步骤432。
本发明的有益效果是：本发明针对货物取送车辆调度方法的发展现状、存在的问题，达到实时性和可移植性兼备的目的，从而满足在实际应用场景中的使用需求，大大提到了货物的取送效率，就要很大的实用性。
附图说明
图1为本发明用于货物取送的单车调度方法的流程图。
图2为本发明基于同一货件的取送优先序约束的排序结构构建方法实施示意图。
图3为本发明基于相异分区取送任务不同组约束的分组结构构建方法实施示意图。
图4为本发明基于车辆始发能力约束的分组结构构建方法实施示意图。
图5为本发明基于车辆始在途能力约束的分组结构构建方法实施示意图。
图6基于货件取送时间窗上界约束的组合结构构建方法实施示意图
图7.1为本发明货物取送单车调度计划的组合结构构造方法中随机生成的取送任务的有序序列示意图。
图7.2本发明货物取送单车调度计划的组合结构构造方法中施以基于同一货件的取送优先序约束的排序结构构建方法示意图。
图7.3本发明货物取送单车调度计划的组合结构构造方法中取送任务序列seq的分组0示意图。
图7.4本发明货物取送单车调度计划的组合结构构造方法中取送任务序列seq的分组1示意图。
图7.5本发明货物取送单车调度计划的组合结构构造方法中取送任务序列seq的分组2示意图。
图7.6本发明货物取送单车调度计划的组合结构构造方法中最终可行组合结构示意图。
具体实施方式
下面结合附图和具体实施例对本发明做进一步详细说明。
本发明公开了一种用于货物取送的单车调度方法，其技术方案包括如下步骤：
步骤1、获取货物取送车辆调度应用场景的系统特征数据，包括地理信息、配送车辆信息、货件信息和客户仓容量信息，地理信息数据包括仓库位置、客户位置、客户位置分区、各位置之间的连接路径、各路径弧段的平均行驶速度信息，配送车辆信息数据包括可执行任务车辆数、车辆的可执行任务时间范围信息、车型信息，货件信息数据包括货件的运输量、货件对应的取送起点和终点位置、货件在其起点和终点位置上的时间窗，在客户处进行中间加工的时间。
步骤2、对货物取送车辆调度应用场景的系统特征数据进行预处理，创建取送任务，以取送任务为基本单元，将其与系统特征数据进行关联，进而将取送问题的系统特征数据处理为符合组合结构构建要求的形式。
步骤21、对货物取送车辆调度应用场景中所涉及的每个位置赋予索引，将仓库的索引赋值为0，客户的索引从1开始赋值；对取送问题中包含的每个货件赋予索引；对执行取送任务的每个车辆赋予索引；
步骤22、根据货件所对应的取送起点和终点位置数据为每个货件创建取送任务，包括从仓库送至客户处的送件类型，从客户处取回仓库的取件类型，从客户处取出并送往另一客户处的调移类型，其中，调移任务被拆分为调移取件任务和调移送件任务，调移取件任务指调移任务中从起点客户取出货件，调移送件指将从起点客户取出的货件送至终点客户的任务；为每项取送任务赋予唯一的索引；
步骤23、基于货物取送车辆调度应用场景中的特征数据和已创建的取送任务，创建满足组合结构构建的已知数据变量，包括：
步骤231、创建取送任务及其取送货件的关联变量ci，其中，i为取送任务的索引，ci为取送任务i对应的货件；
步骤232、创建取送任务及其起点和终点位置的关联变量oi和di，其中，i为取送任务的索引，oi为取送任务i的起点位置索引，di为取送任务i的终点位置索引；
步骤233、创建取送任务及其直接关联客户所在位置的关联变量li，其中，i为取送任务的索引，li为取送任务i的直接关联客户的索引；对于取件类型任务i，有li＝oi；对于取件类型任务i，有li＝di；
步骤234、创建取送任务在起点和终点位置上的时间窗变量
Figure PCTCN2017114561-appb-000068
和
Figure PCTCN2017114561-appb-000069
其中，i为取送任务的索引，
Figure PCTCN2017114561-appb-000070
和
Figure PCTCN2017114561-appb-000071
分别为取送任务i在其起点位置oi上可执行任务的最早和最晚时间，
Figure PCTCN2017114561-appb-000072
和
Figure PCTCN2017114561-appb-000073
分别为取送任务i在其终点位置di上可执行任务的最早和最晚时间；对于不涉及最早时间
Figure PCTCN2017114561-appb-000074
或
Figure PCTCN2017114561-appb-000075
的取送任务，有
Figure PCTCN2017114561-appb-000076
或
Figure PCTCN2017114561-appb-000077
对于不涉及最晚时间
Figure PCTCN2017114561-appb-000078
或
Figure PCTCN2017114561-appb-000079
的取送任务，有
Figure PCTCN2017114561-appb-000080
或
Figure PCTCN2017114561-appb-000081
其中M为一个充分大的正数；
步骤235、创建各类型取送任务的集合，包括送件类型任务集合N-、取件类型任务集合N+，所有货件最后一项取送任务集合NE，调移送件任务集合
Figure PCTCN2017114561-appb-000082
和调移取件任务集合
Figure PCTCN2017114561-appb-000083
等，其中，送件类型任务集合N-为所有送件任务和调移送件任务的集合，取件类型任务集合N+为所有取件任务和调移取件任务的集合；
步骤236、创建取送任务及其运输量的关联变量wi，其中，i为取送任务的索引，wi为取送任务i所取送货件的运输量；
步骤237、创建包括仓库与客户，和客户与客户位置之间的距离变量tdll′，及行驶时间变量tll′；其中，l和l′分别为行驶路径弧段的起点和终点位置的索引，特别地，若两个位置之间无直接通路，则有tdll′＝M和tll′＝M，M为一个充分大的正数；；
步骤238、货物取送车辆调度应用场景中会存在货件被送至客户处进行中间加工，随后再 被取出送至另一地点的情况，因此创建取送任务及其对应货件在客户处的中间加工时间的关联变量Ti，其中，i为取送任务的索引，Ti为取送任务i所对应货件在客户处的中间加工时间；
步骤239、创建执行取送任务的车辆及其最大装载量的关联变量Q，其中，Q为车辆的最大装载量。
步骤3、对货物取送车辆调度应用场景的系统特征进行分析，根据系统特征创建用于构建组合结构的约束及其构建组合结构的方法，并根据它们对组合结构的影响形式进行归类，包括局部组合结构约束、整体组合结构约束和超组合结构约束。
步骤31、创建局部组合结构约束，包括排序结构约束和分组结构约束等在其他局部组合结构固定的条件下仅通过改变一种局部组合结构便可满足的约束，包括：
步骤311、排序结构是取送任务执行顺序的结构化表达，它可由一个取送任务构成的有序序列显示表达，排序结构约束是仅通过改变取送任务的排序结构就能被满足的约束，创建排序结构约束，包括：当取送问题中的货件须被多次取送时，由于只有当货件被送至某个地点后，才能从该地点取出货件送至其他地点，因此同一货件的取送任务之间客观上存在优先序关系，满足这种优先序关系只须对取送任务的排序结构加以约束，为此，建立同一货件的取送优先序约束，并将其归类于排序结构约束；同一货件的取送优先序约束要求在取送任务的排序结构中，同一货件的取送任务执行顺序满足送件任务＞调移取件任务＞调移送件任务＞取件任务，其中，符号＞是同一货件紧邻取送任务优先序关系符号，表示前者在执行顺序上优先于后者；基于同一货件的取送优先序约束的排序结构构建方法为当一个取送任务序列中存在某个货件ci的取送任务不满足优先序约束时，采用顺序互换的方式进行排序结构的局部调整，使取送任务序列满足同一货件的取送优先序约束；
步骤312、将一个取送任务分组定义为车辆从仓库出发并回到仓库的过程中所执行的取送任务集合，分组结构是分组数量以及各分组包含的取送任务的结构化表达，创建仅通过改变取送任务的分组结构就能被满足的分组结构约束，包括：
步骤3121、根据取送系统的实际需要常对客户进行分区管理，使得执行取送任务的车辆不跨区域执行同一组取送任务，即不同分区客户的取送任务不在同一分组中，在取送问题中表现为不同分区的客户之间不存在直接通路，为此，建立相异分区取送任务不同组约束，并将其归类于分组结构约束；相异分区取送任务不同组约束要求在取送任务的分组中，当顺序相邻的两项取送任务所对应的客户位置之间无直接通路时，将这两项取送任务依次分配至相邻的两个分组；基于相异分区取送任务不同组约束的分组结构构建方法用于在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取 送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000084
开始，依排序结构中顺序序号的升序检索当前分组中顺序相邻的两项取送任务ir和ir+1所对应的直接客户的位置
Figure PCTCN2017114561-appb-000085
和
Figure PCTCN2017114561-appb-000086
之间是否存在直接通路，其中r≥k+且r+1≤k-，如果不存在直接通路，即
Figure PCTCN2017114561-appb-000087
则将取送任务ir作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为r，返回当前分组中末项取送任务的执行顺序序号r，其中，ir为在当前固定排序结构下第r项取送任务的索引；
步骤3122、执行取送任务的车辆存在最大装载量的限制，当待取送货件足够多时，由于车辆装载能力限制使得它不能一次完成所有取送任务，因此须将取送任务分配到不同分组中，并逐一完成每个分组的取送任务，为此，建立车辆能力约束，并将其归类于分组结构约束；车辆能力约束要求在一个固定排序结构下执行每个分组的取送任务过程中，车辆负载不超过其最大装载量，包括：
步骤31221、车辆的始发负载量须满足车辆能力约束，为此，建立车辆始发能力约束；车辆始发能力约束要求在一个固定排序结构下的取送任务分组中，起点为仓库和终点为客户的送件类型任务所运送货件的总量不大于任务执行车辆的最大装载量；基于车辆始发能力约束的分组结构构建方法用于在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000088
开始，依排序结构中顺序序号升序检索当前分组中的取送任务ir，对起点为仓库和终点为客户位置的送件类型任务所运送货件的运输量进行累加，若出现
Figure PCTCN2017114561-appb-000089
且
Figure PCTCN2017114561-appb-000090
则将取送任务ik作为当前分组的最后一项取送任务，将
Figure PCTCN2017114561-appb-000091
作为车辆始发负载量，并将当前分组的末项取送任务ik的执行顺序序号更新为k，其中，k+1≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时σi取值为-1，为取件类型任务时σi取值为1；
步骤31222、由于执行送件类型任务后车辆负载量减少，执行取件类型任务后车辆负载量增加，而一个分组中可同时出现上述两种类型的任务，因此在执行一个分组的取送任务过程中车辆负载量动态变化，此时要求在执行当前分组中任何一项取送任务后车辆的负载都满足车辆能力约束，为此，建立车辆在途能力约束；车辆在途能力约束要求在取送任务的分组中，车辆执行每项取送任务后的负载量不大于车辆的最大装载量；基于车辆在途能力约束的分组 结构构建方法用于在一个固定的排序结构下构建一个分组，根据最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000092
开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，利用公式
Figure PCTCN2017114561-appb-000093
计算得到当前取送任务ik执行完毕后的负载量
Figure PCTCN2017114561-appb-000094
其中，k≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时σi取值为-1，为取件类型任务时σi取值为1，根据
Figure PCTCN2017114561-appb-000095
的取值情况，更新车辆始发负载量q0和当前分组的末项取送任务的执行顺序序号k-，包括：
步骤312221、如果
Figure PCTCN2017114561-appb-000096
转步骤312223；
步骤312222、令k＝k+1，如果k≤k-，则计算
Figure PCTCN2017114561-appb-000097
转步骤312221；否则，将取送任务
Figure PCTCN2017114561-appb-000098
作为当前分组的最后一项取送任务，返回当前分组中末项取送任务的执行顺序序号k-；
步骤312223、在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，如果不存在起点为仓库和终点为客户位置的送件类型任务ir，则将取送任务ik-1作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为k-1，返回当前分组中末项取送任务的执行顺序序号k-1；
步骤312224、在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，将首个出现的起点为仓库和终点为客户位置的送件类型任务ir所对应运输量从q0中扣除，即有
Figure PCTCN2017114561-appb-000099
将当前分组的末项取送任务的执行顺序序号k-更新为r-1；计算
Figure PCTCN2017114561-appb-000100
转步骤312221；
步骤32、创建须改变多种局部组合结构才能满足的整体组合结构约束；取送问题中常对取送任务i限定任务开始的最晚时间
Figure PCTCN2017114561-appb-000101
和任务结束的最晚时间
Figure PCTCN2017114561-appb-000102
当车辆到达取送任务i的起点时间tsi大于
Figure PCTCN2017114561-appb-000103
时，或当车辆到达取送任务i的终点时间tei大于
Figure PCTCN2017114561-appb-000104
时，取送任务i在时间上不满足相应时间窗的上界要求，为此，建立货件取送时间窗上界约束，并将其归类于整体组合结构约束；货件取送时间窗上界约束要求每项取送任务的执行时间都不超过货件取送时间窗上界；基于货件取送时间窗上界约束的组合结构构建方法用于对分组中不满足货件取送 时间窗上界约束的局部组合结构进行局部调整，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
Figure PCTCN2017114561-appb-000105
开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，对取送任务ir的处理方法包括：
步骤321、如果
Figure PCTCN2017114561-appb-000106
或
Figure PCTCN2017114561-appb-000107
转步骤333；
步骤322、令r＝r+1，如果r≤k-，转步骤331；否则，将取送任务
Figure PCTCN2017114561-appb-000108
作为当前分组的最后一项取送任务，返回局部调整成功指示器LT＝true和分组序号指示器g＝-1，此处g取值为-1表示不须对已构建的排序结构和分组结构进行局部调整；
步骤323、在范围[0，r)内随机选择一个执行顺序r′，要求取送任务ir′和ir所对应的货件不同
Figure PCTCN2017114561-appb-000109
当
Figure PCTCN2017114561-appb-000110
时有
Figure PCTCN2017114561-appb-000111
当
Figure PCTCN2017114561-appb-000112
时有
Figure PCTCN2017114561-appb-000113
如果不存在这样的取送任务ir′，则不再基于当前已构建的局部组合结构继续进行后续操作，返回局部调整成功指示器LT＝false和分组序号指示器g＝-1；
步骤324、交换取送任务ir′和ir在当前排序结构中的顺序；
步骤325、若当前排序结构中货件
Figure PCTCN2017114561-appb-000114
和
Figure PCTCN2017114561-appb-000115
的取送任务不满足同一货件的取送优先序约束，则采用基于同一货件的取送优先序约束的排序结构构建方法进行取送任务执行顺序的局部调整；
步骤326、返回局部调整成功指示器LT＝true和分组序号指示器g＝gr′，其中，gr′为执行顺序为r′的取送任务当前所在分组的分组序号。
步骤33、创建保证在固定组合结构下取送方案唯一性的超组合结构约束，它们是在组合结构的基础上对取送任务执行方式上的约定，包括货件取送时间窗下界约束，货件中间加工完成时间约束和离开客户时间约束等，包括：
步骤331、货物取送车辆调度应用场景中常对取送任务i限定任务开始的最早时间
Figure PCTCN2017114561-appb-000116
和任务结束的最早时间
Figure PCTCN2017114561-appb-000117
并结合客观实际允许车辆提前到达相应地点进行等待，为此，建立货件取送时间窗下界约束，并将其归类于超组合结构约束；货件取送时间窗下界约束要求当车辆到达取送任务i的起点时间
Figure PCTCN2017114561-appb-000118
时车辆须等待至时间
Figure PCTCN2017114561-appb-000119
之后开始执行取送任务i，当车辆到达取送任务i的终点时间
Figure PCTCN2017114561-appb-000120
时，车辆须等待至时间
Figure PCTCN2017114561-appb-000121
之后开始执行取送任务i；
步骤332、取送问题中存在货件被送至客户处进行诸如包装之类中间加工作业，中间加工完毕的货件由车辆择机取出并送至另一地点的情况，结合客观实际允许车辆提前到达相应地点进行等待，为此，建立货件中间加工完成时间约束，并将其归类于超组合结构约束；货件中间加工完成时间约束要求当送件类型任务i将货件ci送至客户处li，经时长为Ti的中间加工后，由取件类型任务j取出送至另一地点，若车辆到达取件类型任务j起点的时间
Figure PCTCN2017114561-appb-000122
车辆须等待至时间
Figure PCTCN2017114561-appb-000123
之后开始执行任务j；
步骤333、为了使得取送任务的执行尽可能地高效，要求车辆尽可能早地离开当前客户以执行下一项取送任务，为此，建立离开客户时间约束，并将其归类于超组合结构约束；离开客户时间约束要求，对于送件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000124
对于取件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000125
其中，tai和tli分别为车辆到达和离开li的时间，
Figure PCTCN2017114561-appb-000126
为车辆可将货件送入客户处li的最早时间，thi为货件ci的中间加工完毕时间。
步骤4、以取送任务作为取送方案组合结构的基本构造单位，由于超组合结构约束的存在，使得每个取送方案唯一对应一个组合结构，因此当组合结构确定时取送方案也确定，为此采用构造可行组合结构的方式替代可行方案的构造；可行组合结构的构造方法为基于组合结构的约束条件及其分类，随机地构造一个排序结构，利用基于排序结构约束的排序结构构建方法对当前排序结构进行局部调整使其满足约束，在排序结构基础上利用分组结构约束逐一构建每个分组，在每个分组创建完成后利用整体结构约束进行检测和局部的组合结构调整，若组合结构构造过程中存在不满足整体结构约束，并且不能基于整体结构约束的组合结构构建方法进行结构的局部调整，则重复执行可行组合结构的构造方法，直至得到一个完整的、可行的组合结构。具体包括：
步骤41、随机生成一个以取送任务为基本单元的有序序列seq；
步骤42、使用基于同一货件的取送优先序约束的排序结构构建方法对序列seq中取送作业进行执行顺序的局部调整，调整后的序列seq满足同一货件的取送优先序约束；
步骤43、依次构造每个分组并基于整体组合结构约束进行局部调整，包括：
步骤431、初始化当前分组序号g＝0，初始化当前分组首项和末项取送任务在序列seq中的执行顺序序号
Figure PCTCN2017114561-appb-000127
和
Figure PCTCN2017114561-appb-000128
其中，n为取送任务总数；
步骤432、根据
Figure PCTCN2017114561-appb-000129
和
Figure PCTCN2017114561-appb-000130
确定的最大分组范围，使用基于相异分区取送任务不同组约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000131
步骤433、根据
Figure PCTCN2017114561-appb-000132
和
Figure PCTCN2017114561-appb-000133
确定的最大分组范围，使用基于车辆始发能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000134
使用基于车辆在途能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
Figure PCTCN2017114561-appb-000135
步骤434、根据
Figure PCTCN2017114561-appb-000136
和
Figure PCTCN2017114561-appb-000137
确定的最大分组范围，使用基于货件取送时间窗上界约束的组合结构构建方法对分组中不满足货件取送时间窗上界约束的局部组合结构进行局部调整，根据返回结果进行相应操作，包括：
步骤4341、如果局部调整成功指示器LTg＝false，则转步骤41；
步骤4342、如果基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器g′的取值为-1，则令g＝g+1、
Figure PCTCN2017114561-appb-000138
和
Figure PCTCN2017114561-appb-000139
转步骤432；
步骤4343、对于基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器的取值g′，如果g′≤1，则令g＝0、
Figure PCTCN2017114561-appb-000140
和
Figure PCTCN2017114561-appb-000141
否则，令g＝g′-1、
Figure PCTCN2017114561-appb-000142
和
Figure PCTCN2017114561-appb-000143
转步骤432。
步骤5、将步骤4置于诸如蚁群算法的元启发式方法，对方案进行迭代进化，最终根据终止规则停止迭代，输出优化方案。
所述的一种用于货物取送的单车调度方法，所述步骤4还包括：
如附图1所示，本发明方法按照以下步骤进行：
步骤1、获取货物取送车辆调度应用场景中系统特征数据，包括地理信息、配送车辆信息、货件信息和客户仓容量信息等，地理信息数据包括仓库位置、客户位置、客户位置分区、各位置之间的连接路径、各路径弧段的平均行驶速度信息等，配送车辆信息数据包括可执行任务车辆数、车辆的可执行任务时间范围信息、车型信息等，货件信息数据包括货件运输量、货件对应的取送起点和终点位置、货件在其起点和终点位置上的时间窗，在客户处进行中间加工的时间等；
步骤2、对货物取送车辆调度应用场景中系统特征数据进行预处理，创建取送任务，以取送任务为基本单元，将其与系统特征数据进行关联，进而将取送问题的系统特征数据处理为 符合组合结构构建要求的形式；
根据仓库位置、客户位置、客户位置分区和各位置之间的连接路径信息，计算得到各位置之间的路径长度tdod，再结合各路径弧段的平均行驶速度信息计算得到配送车辆在各路径上的行驶时间tod，实施例中数据如表1所示；整理得到配送车辆信息，实施例为单车问题，车辆最大负载量Q假设为15个单位当量，可执行任务时间范围为[0，∞)；进行数据预处理，创建每个取送任务i，取送任务及其取送货件的关联变量ci，取送任务及其起点和终点位置的关联变量oi和di，取送任务及其直接关联客户所在位置的关联变量li，取送任务在起点和终点位置上的时间窗变量
Figure PCTCN2017114561-appb-000144
和
Figure PCTCN2017114561-appb-000145
取送任务及其运输量的关联变量wi，取送任务及其对应货件在客户处的中间加工时间的关联变量Ti，实施例中数据如表2所示；各类型取送任务的集合，包括送件类型任务集合N-、取件类型任务集合N+，所有货件最后一项取送任务集合NE，调移送件任务集合
Figure PCTCN2017114561-appb-000146
和调移取件任务集合
Figure PCTCN2017114561-appb-000147
实施例中数据如表3所示；
表1实施例中各位置之间的走行时间(min)
Figure PCTCN2017114561-appb-000148
表2实施例中取送任务及其关联变量数据信息
Figure PCTCN2017114561-appb-000149
表3实施例中各类型取送任务集合
Figure PCTCN2017114561-appb-000150
Figure PCTCN2017114561-appb-000151
步骤3、对货物取送车辆调度应用场景中系统特征进行分析分析，根据系统特征创建用于构建组合结构的约束及其构建组合结构的方法，并根据它们对组合结构的影响形式进行归类，包括局部组合结构约束、整体组合结构约束和超组合结构约束；
建立同一货件的取送优先序约束，并将其归类于排序结构约束，它要求同一货件的取送任务执行顺序满足送件任务＞调移取件任务＞调移送件任务＞取件任务，为此，构建基于同一货件的取送优先序约束的排序结构构建方法，如图2所示；图2中的符号含义为，△、□和○中的数字分别表示ci取值为0、1和2的取送任务序号，虚线下方数字为执行顺序序号，
Figure PCTCN2017114561-appb-000152
为分组标识符，两个
Figure PCTCN2017114561-appb-000153
之间的取送任务为同一分组的任务，
Figure PCTCN2017114561-appb-000154
为空分组标识符，
Figure PCTCN2017114561-appb-000155
表示可在此处插入分组标识符；图2中，seq0为随机生成的一个取送任务有序序列，其中，取送任务2和3不满足同一货件的取送优先序约束，因此采用基于同一货件的取送优先序约束的排序结构构建方法进行取送任务的执行顺序交换，得到取送任务的有序序列seq。
建立相异分区取送任务不同组约束，并将其归类于分组结构约束，它要求在取送任务的分组中，当顺序相邻的两项取送任务所对应的客户位置之间无直接通路时，将这两项取送任务依次分配至相邻的两个分组，为此，构建基于相异分区取送任务不同组约束的分组结构构建方法，如图3所示，在seq0中根据外部输入的最大分组范围[k+，k-]，若k+＝0，k-＝7，从首项取送任务i0＝2开始，依排序结构中顺序序号的升序检索当前分组中顺序相邻的两项取送任务ir和ir+1所对应直接客户的位置
Figure PCTCN2017114561-appb-000156
和
Figure PCTCN2017114561-appb-000157
之间是否存在直接通路，其中r≥0且r+1≤7，在检索过程中，取送任务i2＝5和i3＝0所对应的直接客户的位置l5＝2和l0＝3之间不存在直接通路，即有td2，3＝t2，3＝M，则将取送任务5作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号k-更新为2，返回当前分组中末项取送任务的执行顺序序号2，此时，得到如序列seq所示的一个新分组标识符。
建立车辆能力约束，并将其归类于分组结构约束，它要求要求在一个固定排序结构下执行每个分组的取送任务过程中，车辆负载不超过其最大装载量，进一步地，将车辆能力约束分解为车辆始发能力约束和车辆在途能力约束；车辆始发能力约束要求在一个固定排序结构下的取送任务分组中，起点为仓库和终点为客户的送件类型任务所运送货件的总量不大于任 务执行车辆的最大装载量，为此，构建基于车辆始发能力约束的分组结构构建方法，如图4所示，在seq0中根据外部输入的最大分组范围[k+，k-]，若k+＝3，k-＝7，从首项取送任务i3＝0开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，对起点为仓库和终点为客户位置的送件类型任务所运送货件的运输量进行累加，由于(1+σ0)w0＝18＜2Q＝30且(1+σ0)w0+0+0+(1+σ6)w6＝40＞2Q＝30，则将取送任务i5＝1作为当前分组的最后一项取送任务，将
Figure PCTCN2017114561-appb-000158
作为车辆始发负载量，并将当前分组的末项取送任务的执行顺序序号k-更新为5，此时，得到如序列seq所示的一个新分组标识符；车辆在途能力约束要求在取送任务的分组中，车辆执行每项取送任务后的负载量不大于车辆的最大装载量，为此，构建基于车辆在途能力约束的分组结构构建方法，如图5所示，在seq0中根据最大分组范围[k+，k-]，其中，k+＝3，k-＝5从首项取送任务i3开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，利用公式
Figure PCTCN2017114561-appb-000159
计算得到当前取送任务ik执行完毕后的负载量
Figure PCTCN2017114561-appb-000160
其中，k≤k-，并按如下步骤执行，首先令k＝k+计算q0＝q0+σ0w0＝0，由于
Figure PCTCN2017114561-appb-000161
因此令k＝k+1，又因为k＝4≤k-＝5，则计算
Figure PCTCN2017114561-appb-000162
因为
Figure PCTCN2017114561-appb-000163
因此令k＝k+1，又因为k＝5≤k-＝5，则计算
Figure PCTCN2017114561-appb-000164
因为
Figure PCTCN2017114561-appb-000165
所以在(k，k-]内按降序检索执行顺序r所对应的取送任务ir，由于此时k＝k-＝5，即区间(k，k-]不存在，故而不存在起点为仓库和终点为客户位置的送件类型任务ir，则将取送任务ik-1＝i4＝3作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号k-更新为4，此时，得到如序列seq所示的一个新分组标识符。
建立货件取送时间窗上界约束，并将其归类于整体组合结构约束，它要求每项取送任务的执行时间都不超过货件取送时间窗上界，为此，构建基于货件取送时间窗上界约束的组合结构构建方法，如图6所示，在seq0中根据外部输入的最大分组范围[k+，k-]，若k+＝0，k-＝2，从首项取送任务i0＝2开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，基于超组合结构约束中货件取送时间窗下界约束、货件中间加工完成时间约束和离开客户时间约束对车辆在取送任务执行方式上的约定，假设车辆在当前分组中待命起点时间为 8:30，货件中间加工完成时间、车辆到达和离开各位置的时间如表4所示，由于
Figure PCTCN2017114561-appb-000166
所以在范围[0，2)内随机选择一个执行顺序r′，要求取送任务ir′和i2＝３所对应的货件不同
Figure PCTCN2017114561-appb-000167
且有
Figure PCTCN2017114561-appb-000168
此时仅有r′＝1对应的取送任务i1＝4满足随机选择条件，因此，交换取送任务i1＝4和i2＝3的执行顺序，得到，返回局部调整成功指示器LT＝true和须重新确定分组结构的起始分组序号g＝gr′＝0，此时，得到如序列seq所示的一个新序列，以及更新后的分组标识符。
表4图6中seq0的部分车辆时间信息
Figure PCTCN2017114561-appb-000169
建立货件取送时间窗下界约束，并将其归类于超组合结构约束，它要求当车辆到达取送任务i的起点时间
Figure PCTCN2017114561-appb-000170
时车辆须等待至时间
Figure PCTCN2017114561-appb-000171
之后开始执行取送任务i，当车辆到达取送任务i的终点时间
Figure PCTCN2017114561-appb-000172
时，车辆须等待至时间
Figure PCTCN2017114561-appb-000173
之后开始执行取送任务i；
建立货件中间加工完成时间约束，并将其归类于超组合结构约束，它要求当送件类型任务i将货件ci送至客户处li，经时长为Ti的中间加工后，由取件类型任务j取出送至另一地点，若车辆到达取件类型任务j起点的时间
Figure PCTCN2017114561-appb-000174
车辆须等待至时间thj之后开始执行任务j；
建立离开客户时间约束，并将其归类于超组合结构约束，它要求对于送件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000175
对于取件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
Figure PCTCN2017114561-appb-000176
其中，tai和tli分别为车辆到达和离开li的时间，
Figure PCTCN2017114561-appb-000177
为车辆可将货件送入客户处li的最早时间，thi为货件ci的中间加工完毕时间；
步骤4、以取送任务作为取送方案组合结构的基本构造单位，由于超组合结构约束的存在，使得每个取送方案唯一对应一个组合结构，因此当组合结构确定时取送方案也确定，为此采用构造可行组合结构的方式替代可行方案的构造；可行组合结构的构造方法为基于组合结构的约束条件及其分类，随机地构造一个排序结构，利用基于排序结构约束的排序结构构建方 法对当前排序结构进行局部调整使其满足约束，在排序结构基础上利用分组结构约束逐一构建每个分组，在每个分组创建完成后利用整体结构约束进行检测和局部的组合结构调整，若组合结构构造过程中存在不满足整体结构约束，并且不能基于整体结构约束的组合结构构建方法进行结构的局部调整，则重复执行可行组合结构的构造方法，直至得到一个完整的、可行的组合结构，如图7所示；
随机生成一个以取送任务为基本单元的有序序列seq，如图7.1所示；
使用基于同一货件的取送优先序约束的排序结构构建方法对序列seq中取送作业进行执行顺序的局部调整，调整后的序列seq满足同一货件的取送优先序约束，如图7.2所示；
基于已构建的排序结构，从首个分组0开始构造每个分组；构造每个分组的过程为依次施以基于相异分区取送任务不同组约束的分组结构构建方法、基于车辆始发能力约束的分组结构构建方法、基于车辆在途能力约束的分组结构构建方法和基于货件取送时间窗上界约束的组合结构构建方法；假设车辆初始待命时间为8:30，初始化当前分组序号g＝0，初始化当前分组首项和末项取送任务在序列seq中的执行顺序序号
Figure PCTCN2017114561-appb-000178
和
Figure PCTCN2017114561-appb-000179
构建序列seq的分组0，序列seq新增一个关于分组0的分组标识符
Figure PCTCN2017114561-appb-000180
如图7.3所示；构建序列seq的分组1，序列seq新增一个关于分组1的分组标识符
Figure PCTCN2017114561-appb-000181
如图7.4所示；构建序列seq的分组2，在施以基于货件取送时间窗上界约束的组合结构构建方法时，将取送任务4和取送任务3在当前任务序列中的执行顺序互换，得到一个新取送任务序列seq和分组标识符，如图7.5所示，根据基于货件取送时间窗上界约束的组合结构构建方法的返回值，将当前分组序号g的取值更新为0，相应地更新当前分组首项和末项取送任务在序列seq中的执行顺序序号
Figure PCTCN2017114561-appb-000182
和
Figure PCTCN2017114561-appb-000183
对当前取送任务序列seq进行分组结构构建，最终得到一个可行的取送任务方案，如图7.6所示，其中各时间信息如表5所示。
表5图7.6中最终方案的时间信息
Figure PCTCN2017114561-appb-000184
Figure PCTCN2017114561-appb-000185
步骤5、将步骤4置于诸如蚁群算法的元启发式方法，对方案进行迭代进化，最终根据终止规则停止迭代，输出优化方案。
初始化元启发式方法中相关参数，如蚁群算法中，以取送任务为节点设置信息素矩阵并将各路径弧段的信息素初始化为相同的正实数；施以步骤4，其中，随机生成取送任务的有序序列可根据采用的具体元启发式方法执行，如蚁群算法中每只人工蚂蚁按照状态转移公式
Figure PCTCN2017114561-appb-000186
选择下一访问客户节点j，直至得到一个完整的取送任务有序序列，式中τij为(i，j)弧段之间的信息素测度，ηk(j)是节点j所提供的启发式信息(可视化信息)，k(j)为节点j对应的车组序号，α为信息素强度因子，β为启发式信息因子；根据元启发式方法的参数更新机制对其进行参数更新，如蚁群算法中利用τij(t+1)＝(1-ρt)τij(t)+Δτij(t)对信息素矩阵进行更新，式中ρt∈(0，1]为当前迭代周期t的信息素衰减系数，Δτij(t)为当前迭代周期t中路径弧段(i，j)上应累积的信息量；元启发式方法保持方案的迭代进化，直至满足终止条件，输出货物取送的单车调度计划。

Claims (14)

  1. 一种用于货物取送的单车调度方法，其特征在于，包括以下步骤：
    步骤1：获取货物取送车辆调度应用场景的系统特征数据，包括地理信息数据、配送车辆信息数据、货件信息数据；
    步骤2：对货物取送车辆调度应用场景的系统特征数据进行预处理，创建取送任务，以取送任务为基本单元，将其与系统特征数据进行关联，进而将取送问题的系统特征数据处理为符合组合结构构建要求的形式；
    步骤3：对货物取送车辆调度应用场景的系统特征进行分析，根据系统特征创建用于构建组合结构的约束及其构建组合结构的方法，并根据其对组合结构的影响形式进行归类，包括局部组合结构约束、整体组合结构约束和超组合结构约束；
    步骤4：以取送任务作为取送方案组合结构的基本构造单位，采用构造可行组合结构的方式；可行组合结构的构造方法为基于组合结构的约束条件及其分类，随机地构造一个排序结构，利用基于排序结构约束的排序结构构建方法对当前排序结构进行局部调整使其满足约束，在排序结构基础上利用分组结构约束逐一构建每个分组，在每个分组创建完成后利用整体结构约束进行检测和局部的组合结构调整，若组合结构构造过程中存在不满足整体结构约束，并且不能基于整体结构约束的组合结构构建方法进行结构的局部调整，则重复执行可行组合结构的构造方法，直至得到一个完整的、可行的组合结构；
    步骤5：将步骤4置于元启发式方法，对方案进行迭代进化，最终根据终止规则停止迭代，输出优化方案。
  2. 根据权利要求1所述的用于货物取送的单车调度方法，其特征在于，所述地理信息数据包括仓库位置、客户位置、客户位置分区、各位置之间的连接路径、各路径弧段的平均行驶速度信息；配送车辆信息数据包括可执行任务车辆数、车辆的可执行任务时间范围信息、车型信息；货件信息数据包括货件的运输量、货件对应的取送起点和终点位置、货件在其起点和终点位置上的时间窗，以及在客户处进行中间加工的时间。
  3. 根据权利要求1所述的用于货物取送的单车调度方法，其特征在于，所述步骤2具体包括：
    步骤21：对货物取送车辆调度应用场景中所涉及的每个位置赋予索引，将仓库的索引赋值为0，客户的索引从1开始赋值；对取送问题中包含的每个货件赋予索引；对执行取送任务的每个车辆赋予索引；
    步骤22：根据货件所对应的取送起点和终点位置数据为每个货件创建取送任务，包 括从仓库送至客户处的送件类型，从客户处取回仓库的取件类型，从客户处取出并送往另一客户处的调移类型；其中调移任务被拆分为调移取件任务和调移送件任务；为每项取送任务赋予唯一的索引；
    步骤23：基于货物取送车辆调度应用场景中的特征数据和已创建的取送任务，创建满足组合结构构建的已知数据变量。
  4. 根据权利要求3所述的用于货物取送的单车调度方法，其特征在于，所述步骤23具体包括：
    步骤231：创建取送任务及其取送货件的关联变量ci；其中，i为取送任务的索引，ci为取送任务i对应的货件；
    步骤232：创建取送任务及其起点和终点位置的关联变量oi和di；其中，oi为取送任务i的起点位置索引，di为取送任务i的终点位置索引；
    步骤233：创建取送任务及其直接关联客户所在位置的关联变量li；其中，li为取送任务i的直接关联客户的索引；对于取件类型任务i，有li＝oi；对于送件类型任务i，有li＝di；
    步骤234：创建取送任务在起点和终点位置上的时间窗变量
    Figure PCTCN2017114561-appb-100001
    和
    Figure PCTCN2017114561-appb-100002
    其中，
    Figure PCTCN2017114561-appb-100003
    和
    Figure PCTCN2017114561-appb-100004
    分别为取送任务i在其起点位置oi上可执行任务的最早和最晚时间，
    Figure PCTCN2017114561-appb-100005
    和
    Figure PCTCN2017114561-appb-100006
    分别为取送任务i在其终点位置di上可执行任务的最早和最晚时间；对于不涉及最早时间
    Figure PCTCN2017114561-appb-100007
    或
    Figure PCTCN2017114561-appb-100008
    的取送任务，有
    Figure PCTCN2017114561-appb-100009
    或
    Figure PCTCN2017114561-appb-100010
    对于不涉及最晚时间
    Figure PCTCN2017114561-appb-100011
    或
    Figure PCTCN2017114561-appb-100012
    的取送任务，有
    Figure PCTCN2017114561-appb-100013
    或
    Figure PCTCN2017114561-appb-100014
    其中M为一个充分大的正数；
    步骤235：创建各类型取送任务的集合，包括送件类型任务集合N-、取件类型任务集合N+，所有货件最后一项取送任务集合NE，调移送件任务集合
    Figure PCTCN2017114561-appb-100015
    和调移取件任务集合
    Figure PCTCN2017114561-appb-100016
    其中，送件类型任务集合N-为所有送件任务和调移送件任务的集合，取件类型任务集合N+为所有取件任务和调移取件任务的集合；
    步骤236：创建取送任务及其运输量的关联变量wi；其中，wi为取送任务i所取送货件的运输量；
    步骤237：创建包括仓库与客户，和客户与客户位置之间的距离变量tdll′，及行驶时间变量tll′；其中，l和l′分别为行驶路径弧段的起点和终点位置的索引，特别地，若两个位置之间无直接通路，则有tdll′＝M和tll′＝M，M为一个充分大的正数；
    步骤238：创建取送任务及其对应货件在客户处的中间加工时间的关联变量Ti，其中，i为取送任务的索引，Ti为取送任务i所对应货件在客户处的中间加工时间；
    步骤239：创建执行取送任务的车辆及其最大装载量的关联变量Q，其中，Q为车辆的最大装载量。
  5. 根据权利要求1所述的用于货物取送的单车调度方法，其特征在于，所述步骤3具体包括：
    步骤31：创建局部组合结构约束，包括排序结构约束和分组结构约束，该约束在其他局部组合结构固定的条件下仅通过改变一种局部组合结构便可满足；
    步骤32：创建需改变多种局部组合结构才能满足的整体组合结构约束；建立货件取送时间窗上界约束，并将其归类于整体组合结构约束，货件取送时间窗上界约束要求每项取送任务的执行时间都不超过货件取送时间窗上界；基于货件取送时间窗上界约束的组合结构构建方法具体为：根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
    Figure PCTCN2017114561-appb-100017
    开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir；
    步骤33：创建保证在固定组合结构下取送方案唯一性的超组合结构约束，包括货件取送时间窗下界约束，货件中间加工完成时间约束和离开客户时间约束。
  6. 根据权利要求5所述的用于货物取送的单车调度方法，其特征在于，所述步骤31具体包括：
    步骤311：创建排序结构约束，方法为：对同一货件的取送任务之间客观上存在优先序关系的任务，对取送任务的排序结构加以约束，建立同一货件的取送优先序约束，并将其归类于排序结构约束；同一货件的取送优先序约束要求在取送任务的排序结构中，同一货件的取送任务执行顺序满足送件任务＞调移取件任务＞调移送件任务＞取件任务；基于同一货件的取送优先序约束的排序结构构建方法具体为：当一个取送任务序列中存在某个货件ci的取送任务不满足优先序约束时，采用顺序互换的方式进行排序结构的局部调整，使取送任务序列满足同一货件的取送优先序约束；
    步骤312：将一个取送任务分组定义为车辆从仓库出发并回到仓库的过程中所执行的取送任务集合，创建仅通过改变取送任务的分组结构就能被满足的分组结构约束。
  7. 根据权利要求6所述的用于货物取送的单车调度方法，其特征在于，所述步骤312具体包括：
    步骤3121：建立相异分区取送任务不同组约束，并将其归类于分组结构约束；相异分区取送任务不同组约束要求在取送任务的分组中，当顺序相邻的两项取送任务所对应的客户位置之间无直接通路时，将这两项取送任务依次分配至相邻的两个分组；基于相异分区取送任务不同组约束的分组结构构建方法为：在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
    Figure PCTCN2017114561-appb-100018
    开始，依排序结构中顺序序号的升序检索当前分组中顺序相邻的两项取送任务ir和ir+1所对应的直接客户的位置
    Figure PCTCN2017114561-appb-100019
    和
    Figure PCTCN2017114561-appb-100020
    之间是否存在直接通路，其中r≥k+且r+1≤k-，如果不存在直接通路，即
    Figure PCTCN2017114561-appb-100021
    则将取送任务ir作为当前分组的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为r，返回当前分组中末项取送任务的执行顺序序号r，其中，ir为在当前固定排序结构下第r项取送任务的索引；
    步骤3122：建立车辆能力约束，并将其归类于分组结构约束；车辆能力约束要求在一个固定排序结构下执行每个分组的取送任务过程中，车辆负载不超过其最大装载量。
  8. 根据权利要求7所述的用于货物取送的单车调度方法，其特征在于，所述步骤3122具体包括：
    步骤31221：建立车辆始发能力约束；车辆始发能力约束要求在一个固定排序结构下的取送任务分组中，起点为仓库和终点为客户的送件类型任务所运送货件的总量不大于任务执行车辆的最大装载量；基于车辆始发能力约束的分组结构构建方法为：用于在一个固定的排序结构下构建一个分组，根据外部输入的最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
    Figure PCTCN2017114561-appb-100022
    开始，依排序结构中顺序序号升序检索当前分组中的取送任务ir，对起点为仓库和终点为客户位置的送件类型任务所运送货件的运输量进行累加，若 出现
    Figure PCTCN2017114561-appb-100023
    且
    Figure PCTCN2017114561-appb-100024
    则将取送任务ik作为当前分组的最后一项取送任务，将
    Figure PCTCN2017114561-appb-100025
    作为车辆始发负载量，并将当前分组的末项取送任务ik的执行顺序序号更新为k，其中，k+1≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时，σi取值为-1；为取件类型任务时，σi取值为1；
    步骤31222、建立车辆在途能力约束；车辆在途能力约束要求在取送任务的分组中，车辆执行每项取送任务后的负载量不大于车辆的最大装载量；基于车辆在途能力约束的分组结构构建方法为：用于在一个固定的排序结构下构建一个分组，根据最大分组范围[k+，k-]，即当前分组中首、末两项取送任务在当前排序结构下的执行顺序序号，从首项取送任务
    Figure PCTCN2017114561-appb-100026
    开始，依排序结构中顺序序号的升序检索当前分组中的取送任务ir，利用公式
    Figure PCTCN2017114561-appb-100027
    计算得到当前取送任务ik执行完毕后的负载量
    Figure PCTCN2017114561-appb-100028
    其中，k≤k-，ir为在当前固定排序结构下第r项取送任务的索引，σi为符号变量，当取送任务i为送件类型任务时，σi取值为-1；为取件类型任务时，σi取值为1；根据
    Figure PCTCN2017114561-appb-100029
    的取值情况，更新车辆始发负载量q0和当前分组的末项取送任务的执行顺序序号k-。
  9. 根据权利要求8所述的用于货物取送的单车调度方法，其特征在于，所述步骤31222还包括：
    步骤312221：如果qik>Q，转步骤312223，否则进入下一步；
    步骤312222：令k＝k+1，如果k≤k-，则计算qik，转步骤312221；否则，将取送任务
    Figure PCTCN2017114561-appb-100030
    作为当前分组的最后一项取送任务，返回当前分组中末项取送任务的执行顺序序号k-；
    步骤312223：在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，如果不存在起点为仓库和终点为客户位置的送件类型任务ir，则将取送任务ik-1作为当前分组 的最后一项取送任务，并将当前分组的末项取送任务的执行顺序序号更新为k-1，返回当前分组中末项取送任务的执行顺序序号k-1；
    步骤312224：在范围(k，k-]内按降序检索执行顺序r所对应的取送任务ir，将首个出现的起点为仓库和终点为客户位置的送件类型任务ir所对应运输量从q0中扣除，即有
    Figure PCTCN2017114561-appb-100031
    将当前分组的末项取送任务的执行顺序序号k-更新为r-1；计算qik，转步骤312221。
  10. 根据权利要求6所述的用于货物取送的单车调度方法，其特征在于，所述步骤32中对取送任务ir的处理方法包括：
    步骤321：如果
    Figure PCTCN2017114561-appb-100032
    或
    Figure PCTCN2017114561-appb-100033
    转步骤323；
    步骤322：令r＝r+1，如果r≤k-，转步骤321；否则，将取送任务
    Figure PCTCN2017114561-appb-100034
    作为当前分组的最后一项取送任务，返回局部调整成功指示器LT＝true和分组序号指示器g＝-1，此处g取值为-1表示不需对已构建的排序结构和分组结构进行局部调整；
    步骤323：在范围[0，r)内随机选择一个执行顺序r′，要求取送任务ir′和ir所对应的货件不同
    Figure PCTCN2017114561-appb-100035
    当
    Figure PCTCN2017114561-appb-100036
    时有
    Figure PCTCN2017114561-appb-100037
    当
    Figure PCTCN2017114561-appb-100038
    时有
    Figure PCTCN2017114561-appb-100039
    如果不存在这样的取送任务ir′，则不再基于当前已构建的局部组合结构继续进行后续操作，返回局部调整成功指示器LT＝false和分组序号指示器g＝-1；
    步骤324：交换取送任务ir′和ir在当前排序结构中的顺序；
    步骤325：若当前排序结构中货件
    Figure PCTCN2017114561-appb-100040
    和
    Figure PCTCN2017114561-appb-100041
    的取送任务不满足同一货件的取送优先序约束，则采用基于同一货件的取送优先序约束的排序结构构建方法进行取送任务执行顺序的局部调整；
    步骤326：返回局部调整成功指示器LT＝true和分组序号指示器g＝gr′，其中，gr′为执行顺序为r′的取送任务当前所在分组的分组序号。
  11. 根据权利要求5所述的用于货物取送的单车调度方法，其特征在于，所述步骤33具 体包括：
    步骤331：建立货件取送时间窗下界约束，并将其归类于超组合结构约束；货件取送时间窗下界约束要求当车辆到达取送任务i的起点时间
    Figure PCTCN2017114561-appb-100042
    时车辆需等待至时间
    Figure PCTCN2017114561-appb-100043
    之后开始执行取送任务i，当车辆到达取送任务i的终点时间
    Figure PCTCN2017114561-appb-100044
    时，车辆需等待至时间
    Figure PCTCN2017114561-appb-100045
    之后开始执行取送任务i；
    步骤332：建立货件中间加工完成时间约束，并将其归类于超组合结构约束；货件中间加工完成时间约束要求当送件类型任务i将货件ci送至客户处li，经时长为Ti的中间加工后，由取件类型任务j取出送至另一地点，若车辆到达取件类型任务j起点的时间
    Figure PCTCN2017114561-appb-100046
    车辆需等待至时间
    Figure PCTCN2017114561-appb-100047
    之后开始执行任务j；
    步骤333：建立离开客户时间约束，并将其归类于超组合结构约束；离开客户时间约束要求，对于送件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
    Figure PCTCN2017114561-appb-100048
    对于取件类型任务i，当货件ci被送入客户处li后，车辆从客户处li离开的时间为
    Figure PCTCN2017114561-appb-100049
    其中，tai和tli分别为车辆到达和离开li的时间，
    Figure PCTCN2017114561-appb-100050
    为车辆可将货件送入客户处li的最早时间，thi为货件ci的中间加工完毕时间。
  12. 根据权利要求8所述的用于货物取送的单车调度方法，其特征在于，所述步骤4具体包括：
    步骤41：随机生成一个以取送任务为基本单元的有序序列seq；
    步骤42：使用基于同一货件的取送优先序约束的排序结构构建方法对序列seq中取送作业进行执行顺序的局部调整，调整后的序列seq满足同一货件的取送优先序约束；
    步骤43：依次构造每个分组并基于整体组合结构约束进行局部调整。
  13. 根据权利要求12所述的用于货物取送的单车调度方法，其特征在于，所述步骤43具体包括：
    步骤431：初始化当前分组序号g＝0，初始化当前分组首项和末项取送任务在序列 seq中的执行顺序序号
    Figure PCTCN2017114561-appb-100051
    和
    Figure PCTCN2017114561-appb-100052
    其中，n为取送任务总数；
    步骤432：根据
    Figure PCTCN2017114561-appb-100053
    和
    Figure PCTCN2017114561-appb-100054
    确定的最大分组范围，使用基于相异分区取送任务不同组约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
    Figure PCTCN2017114561-appb-100055
    步骤433：根据
    Figure PCTCN2017114561-appb-100056
    和
    Figure PCTCN2017114561-appb-100057
    确定的最大分组范围，使用基于车辆始发能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
    Figure PCTCN2017114561-appb-100058
    使用基于车辆在途能力约束的分组结构构建方法更新序列seq中分组g的末项取送任务在当前序列seq中的顺序序号
    Figure PCTCN2017114561-appb-100059
    步骤434：根据
    Figure PCTCN2017114561-appb-100060
    和
    Figure PCTCN2017114561-appb-100061
    确定的最大分组范围，使用基于货件取送时间窗上界约束的组合结构构建方法对分组中不满足货件取送时间窗上界约束的局部组合结构进行局部调整，根据返回结果进行相应操作。
  14. 根据权利要求13所述的用于货物取送的单车调度方法，其特征在于，所述步骤434具体包括：
    步骤4341：如果局部调整成功指示器LTg＝false，则转步骤41；
    步骤4342：如果基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器g′的取值为-1，则令g＝g+1、
    Figure PCTCN2017114561-appb-100062
    和
    Figure PCTCN2017114561-appb-100063
    转步骤432；
    步骤4343：对于基于货件取送时间窗上界约束的组合结构构建方法返回的分组序号指示器的取值g′，如果g′≤1，则令g＝0、
    Figure PCTCN2017114561-appb-100064
    和
    Figure PCTCN2017114561-appb-100065
    否则，令g＝g′-1、
    Figure PCTCN2017114561-appb-100066
    和
    Figure PCTCN2017114561-appb-100067
    转步骤432。
PCT/CN2017/114561 2017-11-13 2017-12-05 一种用于货物取送的单车调度方法 WO2019090874A1 (zh)

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