CN114331220A - Passenger vehicle transport vehicle scheduling method and device based on order dynamic priority - Google Patents
Passenger vehicle transport vehicle scheduling method and device based on order dynamic priority Download PDFInfo
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Abstract
The invention provides a passenger vehicle transport vehicle dispatching method and a device based on order dynamic priority, comprising the following steps: acquiring a transport vehicle data set and an order data set; calculating the emergency degree of each order based on the current time, the latest delivery time of each order, the order adding time and the transportation time required for directly transporting the order from the order taking point to the corresponding order delivery point, calculating the priority of each order based on each emergency degree, and determining the order to be transported in the scheduling period based on each priority; adopting a greedy algorithm to obtain a vehicle dispatching initial solution set at the dispatching time; dividing a vehicle scheduling initial solution set into a plurality of clusters by taking the current position of each vehicle as a clustering center; establishing a transportation profit function at the scheduling moment, taking the transportation profit function as an objective function, and optimizing feasible solutions in various clusters by adopting a tabu search algorithm; and combining and recombining feasible solutions subjected to tabu optimization in each cluster to obtain the optimal solution for vehicle scheduling at the scheduling moment.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a passenger vehicle transport vehicle scheduling method and device based on order dynamic priority.
Background
In recent years, with the rapid increase of automobile production and sale in China, the automobile logistics industry is rapidly developed. In 2020, the whole logistics market scale of the Chinese passenger vehicle exceeds 10000 million yuan, wherein the transportation cost accounts for more than 40%, and the transportation of the domestic passenger vehicle still mainly takes highways as the main part at present, so if the transportation cost in the whole logistics of the passenger vehicle is reduced, the profit of enterprises is greatly increased.
The problem of dynamic picking and delivering of open heterogeneous fleet vehicles with time windowing and dynamic priority, multi-warehouse, aperiodic, and split requirements, which aims at maximizing enterprise profit within one cycle time, is NP-hard, a variant of the classical VRP with capacity constraint, which is currently studied by few scholars. Most of the existing researches are focused on the dynamic path planning problem and the goods taking and delivering problem with time window constraint, and the research considering the dynamic priority is very few; at present, the research aiming at the dynamic path planning problem, whether single-target or multi-target optimization, mostly focuses on optimizing the transportation cost, the vehicle waiting time and the driving time, the number of service vehicles, the service delay time, the customer satisfaction degree, the vehicle utilization rate and the like, and rarely considers the maximization of the operating profit of an enterprise side, so that the transportation profit of the enterprise cannot be improved, and the passenger vehicle resources are wasted. Therefore, how to increase the transportation profit of enterprises in a period of time, reduce the number of passenger vehicle transportation vehicles, and reduce carbon emission is an urgent technical problem to be solved.
Disclosure of Invention
In view of the above, the present invention provides a dynamic vehicle scheduling method and apparatus for passenger vehicle transportation based on dynamic priority of orders, so as to solve one or more problems in the prior art.
According to one aspect of the invention, the invention discloses a dynamic vehicle scheduling method for passenger vehicle transportation based on dynamic order priority, which comprises the following steps:
acquiring a transport vehicle data set and an order data set, wherein each piece of vehicle data in the vehicle data set comprises vehicle joining time, a vehicle current position, vehicle rated capacity, vehicle running speed and vehicle loaded capacity, and each piece of order data in the order data set comprises order joining time, latest delivery time, an order pick-up point and an order delivery point;
calculating the emergency degree of each order based on the current time, the latest delivery time of each order, the order adding time and the transportation time required for directly transporting the order from the order taking point to the corresponding order delivery point, calculating the priority of each order based on the emergency degree of each order, and determining the order to be transported in the scheduling period based on the priority of each order;
adopting a greedy algorithm to the order to be transported to obtain a vehicle dispatching initial solution set at the dispatching time;
dividing the vehicle scheduling initial solution set into a plurality of clusters by a clustering algorithm by taking the current position of each vehicle as a clustering center;
establishing a transportation profit function at the scheduling moment, taking the transportation profit function as an objective function, and optimizing feasible solutions in various clusters by adopting a tabu search algorithm;
and combining and recombining feasible solutions subjected to tabu optimization in each cluster to obtain the optimal solution for vehicle scheduling at the scheduling moment.
In some embodiments of the invention, the objective function is:
in some embodiments of the present invention, the orders in the order data set include orders scheduled and taken but not completed in the last scheduling cycle, orders not scheduled in the last scheduling cycle, and newly added orders, and the newly added orders include orders newly generated from the last scheduling cycle to the current scheduling cycle, orders scheduled but not taken in the last scheduling cycle, and orders with the remaining part not scheduled in the last scheduling cycle.
In some embodiments of the present invention, calculating the urgency of each order based on the current time, the latest arrival time of each order, the order joining time, and the transport time required to transport directly from the order pickup point to the corresponding order delivery point comprises:
respectively calculating the difference value between the current time and the order adding time of each order as the residence time of each order;
determining the distance between the order picking and delivering points of each order based on the order picking and delivering points of each order and the positions of the order delivering points, and respectively calculating the ratio of the distance between the order picking and delivering points of each order to the minimum driving speed of the vehicle to be used as the transportation time of each order;
calculating the difference value between the latest delivery time of each order and the order adding time, the transportation time and the current time;
and taking each calculated difference value as the emergency degree of the order.
In some embodiments of the invention, calculating the priority of each order based on its urgency includes:
determining the maximum urgency level based on the calculated urgency levels of the orders, and determining the maximum urgency level according to the formula
Calculating the priority of each order; wherein
The priority of the order m is referred to,
to maximum urgency, EmIndicating the urgency of the order.
In some embodiments of the present invention, determining orders to be transported in the scheduling period based on the priority of each order includes:
selecting
Corresponding order and will
The corresponding order is used as the order to be transported in the scheduling period.
In some embodiments of the present invention, in the tabu search algorithm, the generation method of the neighborhood includes one of the following:
removing and inserting a pick point and a delivery point of a first order in the first path to the same position of the second path;
exchanging the pick-up point of the first order and the pick-up point of the second order in the first path, and exchanging the delivery point of the first order and the delivery point of the second order;
exchanging the goods taking points and the goods delivering points of the two orders at the same positions in the first path and the second path respectively;
removing both the pick point and the delivery point of the first order in the first path;
inserting a goods taking point and a goods delivering point of the first order into any path respectively;
and moving the position of the pick point of the first order in the first path to be behind the delivery point of the first order.
In some embodiments of the present invention, the tabu table length of the tabu search algorithm is 100, and the number of feasible solutions within each class of clusters is 20.
According to another aspect of the present invention, a dynamic vehicle dispatching system for passenger vehicle transportation based on order dynamic priority is further disclosed, the system comprising a processor and a memory, the memory having stored therein computer instructions, the processor being configured to execute the computer instructions stored in the memory, the system implementing the steps of the method according to any one of the above embodiments when the computer instructions are executed by the processor.
According to yet another aspect of the invention, a computer-readable storage medium is also disclosed, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any of the embodiments above.
The dynamic vehicle dispatching method for passenger vehicle transportation based on the dynamic order priority considers the difference and the value of service objects, namely preferentially meets the order transportation requirement with higher emergency degree, can improve the service satisfaction and evaluation of customers to enterprises, and improves the competitiveness of the enterprises. In addition, for orders with low emergency degree, parts can be selected for scheduling according to the principle that whether the transport vehicles are in the same way or not in real-time scheduling, enterprise profits are improved while customer service time requirements are met, the number of passenger vehicle transport vehicles is greatly reduced, and carbon emission is effectively reduced.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the specific details set forth above, and that these and other objects that can be achieved with the present invention will be more clearly understood from the detailed description that follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For purposes of illustrating and describing some portions of the present invention, corresponding parts of the drawings may be exaggerated, i.e., may be larger, relative to other components in an exemplary apparatus actually manufactured according to the present invention. In the drawings:
fig. 1 is a flowchart illustrating a dynamic vehicle scheduling method for passenger vehicle transportation based on dynamic order priority according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of an order timeout penalty curve according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a vehicle detention penalty curve according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not closely related to the present invention are omitted.
It should be emphasized that the term "comprises/comprising/comprises/having" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same or similar parts, or the same or similar steps.
Currently, research in the field of dynamic path planning focuses on the research on transportation cost, regardless of differences and values of service objects, and all customers and all orders are considered as general service objects. In real life, the order is often classified as urgent, and the urgent degree of the order (i.e. the priority of the order) changes with the passage of time; the order transportation requirements with higher emergency degree are met preferentially, the service satisfaction and evaluation of customers to the enterprise can be improved, and the competitiveness of the enterprise is improved. In addition, for orders with low emergency degree, a part can be selected for scheduling according to the principle that whether the transport vehicle is on the way or not in real-time scheduling, so that the service time requirement of customers is met, and meanwhile, the enterprise profit is improved. At present, both the general dynamic path planning problem and the dynamic scheduling problem considering customer difference are focused on independent analysis of a dynamic planning model based on time period division, so that orders in each time slice can only be scheduled in the scheduling, model changes caused by continuity of customer order time and continuity of scheduling are ignored, and the actual engineering problems of utilization rate of transport vehicles and low overall profit in the whole vehicle scheduling of passenger vehicles cannot be effectively solved. Aiming at the problems, the invention realizes a passenger vehicle logistics dynamic scheduling model considering order dynamic priority and a rapid solving algorithm; the characteristic that the dynamic priority of the order changes along with time is taken into consideration, and the order with low order urgency degree can be processed in subsequent dispatching, so that the maximization of enterprise profit in each dispatching is guaranteed.
FIG. 1 is a flowchart illustrating a dynamic scheduling method for passenger car transportation based on dynamic priority of orders according to an embodiment of the present invention, as shown in FIG. 1, the method includes at least steps S10-S60.
Step S10: the method comprises the steps of obtaining a transport vehicle data set and an order data set, wherein each piece of vehicle data in the vehicle data set comprises vehicle joining time, a vehicle current position, vehicle rated capacity, vehicle running speed and vehicle loaded capacity, and each piece of order data in the order data set comprises order joining time, latest delivery time, an order pick-up point and an order delivery point.
Suppose that the whole passenger vehicle logistics transportation network G (E, V) is at the moment
Has the advantages of
The vehicle data collection comprises a vehicle transportation vehicle and M orders, wherein the vehicle data collection correspondingly comprises vehicle data of K vehicles; in order to avoid long waiting time of the transport vehicle and impose punishment on the waiting time, the vehicle data comprises vehicle joining time, and the vehicle joining time of the vehicle needs to be reset when the vehicle is rejoined to the system after completing the transportation of a certain order; the vehicle running speed may specifically be an average of the vehicleThe running speed and the current position of the vehicle can be recorded as AkIf so, the set of the initial positions of all vehicles in the whole transportation network is A; in addition, each transport vehicle k also has a rated capacity QkLoaded capacity hkAnd the like, wherein K =1,2,3 … K.
M orders = M1The order + M scheduled and taken but not completed in the last scheduling period2An unscheduled order; wherein M is2The unscheduled orders comprise orders which are unscheduled in the last scheduling period and newly-added orders, wherein the newly-added orders comprise orders which are newly generated from the last scheduling period to the scheduling period, orders which are scheduled but not taken in the last scheduling period and orders which have the remaining part unscheduled in the last scheduling period. The "order with remaining part not scheduled" in the last scheduling period means that part of the goods in an order in the last scheduling period has been transported, and part of the goods in the order has not been transported, and the corresponding order with remaining part of the goods not transported also serves as the order of the scheduling period. If the order data set has M orders, the order data set also has M order data; in particular, order m
The data includes order entry time, latest delivery time, order pick-up point OmAnd order delivery point DmM =1,2,3 … M; in addition, the order data of order m may also include order demand; and the order data for orders that have participated in the dispatch but have not completed the shipment for the previous cycle also includes the delivery vehicles. In the whole passenger car logistics transportation network G (E, V),
(ii) a And O is a set of M order pick points, and D is a set of M order delivery points.
Step S20: calculating the emergency degree of each order based on the current time, the latest delivery time of each order, the order adding time and the transportation time required for directly transporting the order from the order taking point to the corresponding order delivery point, calculating the priority of each order based on the emergency degree of each order, and determining the order to be transported in the scheduling period based on the priority of each order.
In this step, the latest delivery time of the order, which may be requested by the customer or automatically allocated by the system according to a fixed time limit, and the order joining time are the corresponding part of order data acquired in step S10; the transportation time of the order refers to the time required for the order to be transported directly from the order pick-up point to the order delivery point, and the order pick-up point and the order delivery point are also the order data obtained in step S10.
The urgency and priority of an order changes over time and may therefore be understood as the dynamic priority of an order or the dynamic urgency of an order. Each order for a customer has a priority and urgency, and the urgency of the order can be used EmIs shown, and
wherein E ismaxCalculating the maximum urgency degree of the plurality of urgency degrees in the scheduling time; and EmThe smaller the value of (A) is, the more urgent the order is, namely the higher the order priority is, the transportation should be prioritized; it is understood that an order with an urgency value of 0 is an order that is designated as having to be scheduled in this scheduling, and an order with an urgency value greater than 0 is an order that can be selectively scheduled.
Illustratively, calculating the urgency of each order based on the current time, the latest arrival time of each order, the order joining time, and the transport time required to transport directly from the order pick point to the corresponding order delivery point includes: determining the distance between the order picking and delivering points of each order based on the order picking and delivering points of each order and the positions of the order delivering points, and respectively calculating the ratio of the distance between the order picking and delivering points of each order to the minimum driving speed of the vehicle to be used as the transportation time of each order; calculating the difference value between the latest delivery time of each order and the order adding time, the transportation time and the current time; and taking the calculated difference value as the urgency degree of the order.
In particular, the method comprises the following steps of,
(ii) a v is the minimum rated speed of the vehicle,
，tmindicates the time required to transport order m directly from its pick-up point to the delivery point, dmThe distance between the pick point and the delivery point for order m.
，EmFor the urgency of the order m at this scheduling time, DTmFor the latest delivery time of the requested order, BmTime is added for the order and T is the current time. Since the current time T varies in real time, it can be seen from the above formula that the urgency of an order is not a fixed value but a dynamic value that varies with time, and when the remaining transit time of an order is less than the latest arrival time of the order, E of the ordermThe value can be changed directly to 0, i.e. EmIs 0, indicating that the order must be shipped.
In this step, the urgency level of each order at the current time is calculated to obtain a maximum value Emax，EmaxAnd the maximum value of the emergency degrees of a plurality of orders calculated at the scheduling time is shown. Illustratively, calculating the priority of each order based on the urgency of each order includes: determining the maximum urgency level based on the calculated urgency levels of the orders, and determining the maximum urgency level according to the formula
Calculating the priority of each order; wherein P ismPriority of order m, EmaxTo maximum urgency, EmIndicating the urgency of said order, DTmThe latest arrival time of order m, BmIndicates the time of addition, t, of order mmRefers to the transit time for order m. The above formula shows PmIs between (0, 1), when P ismThe closer the value of (1), the greater the probability that the order will participate in the scheduling in the current scheduling.
In an embodiment of the present invention, the priority of the newly added order is an initial priority, and the priority of the order not participating in scheduling in the previous scheduling period is a recalculated priority value; it should be understood that, in the present invention, orders whose order priority reaches a certain fixed value must all be transported in the scheduling, and the order priority that has participated in scheduling but has not been completed in the previous scheduling period has a fixed threshold (no recalculation is needed in the scheduling period), that is, the orders must be transported by the same vehicle again in the scheduling period.
In addition, determining the orders to be transported in the scheduling period based on the priority of each order includes: selecting
Corresponding order and will
The corresponding order is used as the order to be transported in the scheduling period. Specifically, an arbitrary value between 0 and 1 is taken during scheduling, when P of the order ismIf the value is larger than the random number, the order participates in the scheduling, otherwise the order cannot be selected in the scheduling; it should be understood that since the priority of orders dynamically changes over time, orders that do not participate in the scheduling process may still be selected in the next scheduling.
Step S30: and acquiring a vehicle dispatching initial solution set at the dispatching time by adopting a greedy algorithm for the order to be transported.
Greedy algorithm, also called greedy algorithm, is an example of an algorithm that follows a heuristic solution method that makes local optimal choices at each stage in order to find a globally optimal solution. The vehicle scheduling initial solution here is a travel route of each vehicle, that is, a node order that each vehicle passes through.
Step S40: and taking the current position of each vehicle as a clustering center, and dividing the vehicle scheduling initial solution set into a plurality of clusters through a clustering algorithm.
In the step, a clustering algorithm is adopted to divide the vehicle dispatching initial solution set into a plurality of clusters according to the current position of the vehicle, wherein the adopted clustering algorithm is not limited as long as the clustering operation required in the step can be met.
Step S50: and establishing a transportation profit function at the scheduling moment, taking the transportation profit function as an objective function, and optimizing feasible solutions in various clusters by adopting a tabu search algorithm.
In this step, a solvable optimization is performed based on a tabu search algorithm, and a transportation profit function is taken as a target optimization function.
In an exemplary manner, the first and second electrodes are,
；
Further, the sum of the earnings of all vehicles at the current time is:
，Wmin order to take advantage of the benefits of the order m,
a decision value indicating whether order M is transported by vehicle K, M =1,2,3 … M, K =1,2,3 … K. Total transportation cost at present
(ii) a Wherein the content of the first and second substances,
the unit load cost for vehicle k carrying order m,
the number of goods transported by vehicle k for order m,
is the distance from node i to node j,
a decision value corresponding to whether order m is transported by vehicle k,
and the decision value is the corresponding decision value of whether the vehicle k runs from the node i to the node j. Vehicle fixed cost
(ii) a Wherein, FKFor a fixed cost of the vehicle k,
a decision value corresponding to whether order m is transported by vehicle k,
is 0 or 1. Order timeout penalty cost
，
Penalizing cost for overtime of the order m; the order timeout penalty graph is shown in figure 2. Cost of vehicle detention
(ii) a Wherein the content of the first and second substances,
；
as the idle time of the vehicle k,
t is the current time, LKAs the joining time of the vehicle k,
to relate to
A function of (a); fig. 3 is a schematic diagram of a vehicle detention penalty curve, where the abscissa of the curve in fig. 3 represents the number of days (Day) that the vehicle is detained, and the ordinate represents the cost due to the vehicle being detained.
Based on the above, the objective function optimized by the tabu search algorithm
To find out immediately
(ii) a In particular, the method comprises the following steps of,
The following constraints are provided for the above objective function formula:
Step S60: and combining and recombining feasible solutions subjected to tabu optimization in each cluster to obtain the optimal solution for vehicle scheduling at the scheduling moment.
In this step, the feasible solutions after tabu optimization in each cluster are further collected and recombined to obtain the final optimal solution. The optimal solution for vehicle scheduling at the present scheduling time is similar to the initial solution for vehicle scheduling, and the contents of the solutions are the travel paths of the vehicles, that is, the node sequences that the vehicles pass through.
In an embodiment of the present invention, in the tabu search algorithm, the generation method of the neighborhood includes one of the following: removing and inserting a pick point and a delivery point of a first order in the first path to the same position of the second path; exchanging the pick-up point of the first order and the pick-up point of the second order in the first path, and exchanging the delivery point of the first order and the delivery point of the second order; exchanging the goods taking points and the goods delivering points of the two orders at the same positions in the first path and the second path respectively; removing both the pick point and the delivery point of the first order in the first path; inserting a goods taking point and a goods delivering point of the first order into any path respectively; and moving the position of the pick point of the first order in the first path to be behind the delivery point of the first order. In addition, the tabu table length of the tabu search algorithm may be 100, and the number of feasible solutions within each class of clusters may be 20.
The invention further discloses a passenger vehicle transportation dynamic vehicle scheduling method based on order dynamic priority, which is provided by the following specific example, and aims at the problem of dynamic change of order demand in the problem of dynamic scheduling of vehicle paths of a whole vehicle logistics network of a passenger vehicle, an order priority dynamic change model, a passenger vehicle transportation scheduling planning model based on the order priority dynamic change model and a solving algorithm are designed, so that the transportation profit of enterprises in a period can be effectively improved, the number of required transportation vehicles is reduced, and carbon emission is effectively reduced. Specifically, the method adopts a tabu search hybrid parallel algorithm solution model, adopts a greedy insertion algorithm to construct an initial solution, uses six variable neighborhood search operators and a tabu table to control the search direction so as to prevent the occurrence of the situation of a locally optimal solution, and uses a clustering algorithm to split and recombine a feasible solution optimization process so as to improve the overall optimization efficiency. The overall algorithm framework is shown as algorithm 1.
For the above algorithm, the specific steps are as follows:
the method comprises the following steps: data set and parameter setting and initialization: and reading data sets such as the order set M and the vehicle set A and related configuration files.
Step two: and when the initial scheduling time is reached, calculating the dynamic priority of all orders in the order set, and selecting the orders according to an order selection rule.
Step three: and generating an initial solution for the selected order by adopting a greedy insertion algorithm.
Step four: and dividing the initial solution set into several clusters according to the current position of the vehicle by adopting a clustering algorithm.
Step five: and aiming at the solution set in each cluster class, optimizing the feasible solutions by adopting a tabu search algorithm, wherein the length of a tabu table is 100, and the number of the diversity solutions in the reference set is 20.
Step six: and (4) performing solution set and recombination after tabu optimization in each cluster class to obtain the final optimal solution.
Step seven: and when the next scheduling moment is reached, recalculating the dynamic priorities of the newly added order, the unscheduled order in the previous scheduling period and the scheduled but unscheduled order, picking the order again according to the order picking rule, and repeating the third step, the sixth step and the fourth step to obtain a new optimal solution.
Step five: waiting for the next order scheduling period to be entered and looping through the above steps.
Wherein, six mutation operators adopted when neighborhood generation is carried out are all formed by carrying out re-expansion on common mutation operation based on a standard VRP (vehicle route planning) problem, and the method comprises the following steps of node relocation and node position exchange:
1. removing two goods taking and delivering nodes of a certain order in one path and inserting the two goods taking and delivering nodes into the same position of the other path;
2. respectively exchanging the positions of the goods taking points and the goods delivering points of the two orders in one path;
3. respectively exchanging the goods taking points and the goods delivering points of the two orders at the same positions in the two paths;
4. removing both pick-and-send nodes of a certain order in a path;
5. randomly inserting two goods taking and delivering nodes of a certain order into a path;
6. randomly changing the position of the delivery point of a certain order in a path to a position behind the delivery point
The dynamic scheduling of orders in the above embodiments of the present invention takes multiple periods as an observation scope, considers the influence of the continuity of customer order time and the continuity of scheduling itself on the scheduling model, and describes the order urgency degree, i.e. dynamic priority, based on the time continuity, and as the time and scheduling period advance, the priority of orders also changes, and at the beginning of each scheduling period, all unfinished orders are classified, including orders that did not participate in scheduling before the period, orders that did participate in scheduling but not be picked before the period, orders that participated in scheduling before the period and have been picked already, and newly added orders of the period. Orders not participating in scheduling before the period, orders not being taken but participating in scheduling before the period and newly added orders of the period can be classified into a category, the category is the newly added orders, the priorities of the orders are calculated again, and the orders taken before the period are still transported by the original vehicle. For new orders, the orders which need to be dispatched are selected according to order priority, then the orders are inserted into the appropriate insertion points in the optimized path with the goal of maximizing profit, if no insertion points are available in the vehicle path, a new vehicle is randomly allocated to transport the orders until all the orders which need to be dispatched are allocated.
Through the embodiment, the scheduling method which aims at maximizing the total profit within a period of time (week, ten days, month and quarter) based on the dynamic priority of the order is found in the dynamic vehicle scheduling problem of the third-party passenger vehicle logistics transportation network. The method first establishes a dynamic pick-and-place scheduling problem (DP-DPTWHVNDDP) model with time windows, heterogeneous fleet and no warehouse based on order dynamic priority. And then, a mixed heuristic parallel algorithm is used, and tabu search and variable neighborhood search algorithms are combined to solve the model. Experimental data and actual operation effect show that the method can greatly improve enterprise transportation profits in a period of time, greatly reduce the number of passenger vehicle transportation vehicles and effectively reduce carbon emission.
Correspondingly, the invention also discloses a dynamic passenger vehicle transportation scheduling system based on order dynamic priority, which comprises a processor and a memory, wherein the memory stores computer instructions, the processor is used for executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system realizes the steps of the method of any one of the above embodiments.
In addition, the invention also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method according to any of the above embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations of both. Whether this is done in hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments in the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A dynamic vehicle dispatching method for passenger vehicle transportation based on dynamic order priority is characterized by comprising the following steps:
acquiring a transport vehicle data set and an order data set, wherein each piece of vehicle data in the vehicle data set comprises vehicle joining time, a vehicle current position, vehicle rated capacity, vehicle running speed and vehicle loaded capacity, and each piece of order data in the order data set comprises order joining time, latest delivery time, an order pick-up point and an order delivery point;
calculating the emergency degree of each order based on the current time, the latest delivery time of each order, the order adding time and the transportation time required for directly transporting the order from the order taking point to the corresponding order delivery point, calculating the priority of each order based on the emergency degree of each order, and determining the order to be transported in the scheduling period based on the priority of each order;
adopting a greedy algorithm to the order to be transported to obtain a vehicle dispatching initial solution set at the dispatching time;
dividing the vehicle scheduling initial solution set into a plurality of clusters by a clustering algorithm by taking the current position of each vehicle as a clustering center;
establishing a transportation profit function at the scheduling moment, taking the transportation profit function as an objective function, and optimizing feasible solutions in various clusters by adopting a tabu search algorithm;
and combining and recombining feasible solutions subjected to tabu optimization in each cluster to obtain the optimal solution for vehicle scheduling at the scheduling moment.
2. The dynamic passenger vehicle transportation order priority based scheduling method of claim 1 wherein the objective function is:
3. The dynamic passenger vehicle transportation order priority based scheduling method according to claim 1, wherein the orders in the order data set comprise orders scheduled and taken but not completed in the last scheduling cycle, orders not scheduled in the last scheduling cycle, and newly added orders, and the newly added orders comprise orders newly generated from the last scheduling cycle to the middle of the current scheduling cycle, orders scheduled but not taken in the last scheduling cycle, and orders with the remaining part not scheduled in the last scheduling cycle.
4. The dynamic passenger vehicle transportation order priority based vehicle scheduling method of claim 1 wherein calculating the urgency of each order based on the current time, the latest arrival time of each order, the order joining time, and the transportation time required to transport directly from the order pickup location to the corresponding order pickup location comprises:
determining the distance between the order picking and delivering points of each order based on the order picking and delivering points of each order and the positions of the order delivering points, and respectively calculating the ratio of the distance between the order picking and delivering points of each order to the minimum driving speed of the vehicle to be used as the transportation time of each order;
calculating the difference value between the latest delivery time of each order and the order adding time, the transportation time and the current time;
and taking the calculated difference value as the urgency degree of the order.
5. The dynamic passenger vehicle transportation vehicle scheduling method based on order dynamic priority according to claim 4, wherein calculating the priority of each order based on the urgency of each order comprises:
determining the maximum urgency level based on the calculated urgency levels of the orders, and determining the maximum urgency level according to the formula
Calculating the priority of each order; wherein
The priority of the order m is referred to,
to maximum urgency, EmIndicating the urgency of the order.
6. The dynamic passenger vehicle scheduling method for passenger vehicle transportation based on dynamic order priority of claim 5, wherein determining the orders to be transported in the scheduling period based on the priority of each order comprises:
selecting
Corresponding order and will
The corresponding order is used as the order to be transported in the scheduling period.
7. The dynamic passenger vehicle transportation order priority-based vehicle dispatching method according to claim 1, wherein in the tabu search algorithm, the neighborhood generation method comprises one of the following:
removing and inserting a pick point and a delivery point of a first order in the first path to the same position of the second path;
exchanging the pick-up point of the first order and the pick-up point of the second order in the first path, and exchanging the delivery point of the first order and the delivery point of the second order;
exchanging the goods taking points and the goods delivering points of the two orders at the same positions in the first path and the second path respectively;
removing both the pick point and the delivery point of the first order in the first path;
inserting a goods taking point and a goods delivering point of the first order into any path respectively;
and moving the position of the pick point of the first order in the first path to be behind the delivery point of the first order.
8. The dynamic passenger vehicle transportation order priority-based vehicle scheduling method according to claim 6, wherein the length of the tabu table of the tabu search algorithm is 100, and the number of feasible solutions in each cluster is 20.
9. Dynamic passenger vehicle transportation order priority based scheduling system comprising a processor and a memory, wherein the memory has stored therein computer instructions for executing the computer instructions stored in the memory, which when executed by the processor performs the steps of the method according to any of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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