CN113095753A - Unmanned truck-collecting dispatching method based on intelligent container management position allocation - Google Patents
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Abstract
The invention provides a position allocation method based on intelligent container management unmanned truck-mounted dispatching, which specifically comprises the steps of constructing an optimization model of port-road transportation cooperative dispatching, finishing an optimized line by taking the minimum total cost as an optimization target, setting the total cost as a target function Z, solving and analyzing the target function Z by adopting a Kuhn-Munkres algorithm, performing bipartite graph matching, augmented path and weighted bipartite graph optimal matching processing through a simplified model, and then performing example analysis to obtain the optimal path and box quantity of a truck-mounted pair. According to the method, a model is established by analyzing the problems and taking the lowest transportation cost as an optimization target, then the KM algorithm is adopted for solving and analyzing, and intelligent optimization position allocation is carried out, so that flexible scheduling can be realized, the idle rate of a secondary transportation task can be reduced by about 40% by optimizing the transportation path of a truck collection team, the overall transportation efficiency is obviously improved, and the transportation cost is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent production management of containers, and particularly relates to an unmanned truck-collecting dispatching method based on intelligent management of a container position.
Background
With the rapid development of import and export trade, the freight demand of import and export goods is increasing day by day. As a key node of import and export trade, a port is a hub for constructing a comprehensive transportation network and is also an important strategic resource for the development of the logistics transportation industry. The continuously increased port throughput puts higher requirements on port service levels, and an efficient port collection and distribution system is urgently required to be established, so that the operation capacity of a port is improved, and the development requirements of the economy and the society are met.
According to the development and change of the port collection and distribution at home and abroad, the occupation ratio of the road collection and distribution in a collection and distribution system is continuously improved, which causes a plurality of problems, particularly the problem of transportation efficiency caused by the congestion of the road collection and distribution and the problem of environmental pollution caused by freight vehicles, how to improve the port collection and distribution system and further how to effectively solve the problems of traffic congestion, environmental pollution and the like of the current port, and the method becomes a key point which troubles workers in the field.
Disclosure of Invention
In order to solve the technical problems, the invention researches a port system and a road transportation system as a whole, realizes the efficient distribution of port goods in a road transportation mode according to dynamic real-time vehicle and goods information, such as port freight transportation amount, customer demand, goods loading and unloading operation time, vehicle travel and the like, and particularly provides a position allocation method based on intelligent container management unmanned truck dispatching.
The specific scheduling steps are as follows: (1) constructing an optimization model of the port-road transportation cooperative scheduling, taking the minimum total cost as an optimization target, carrying out allocation transportation of a port container through road transportation within scheduling time, transporting goods to a client, and transporting the goods at the client to a port to finish an optimized line, wherein the total cost is set as a target function Z;
(2) setting as a primary transportation task or a secondary transportation task according to the transportation condition of the container in the port, and solving by adopting an algorithm;
(3) and when the second-level transportation task is performed, solving and analyzing the objective function Z by adopting a Kuhn-Munkres algorithm, performing bipartite graph matching, an augmented path and weighted bipartite graph optimal matching processing through a simplified model, and performing example analysis to obtain the optimal path and box amount of the truck collection pair.
The model is established by taking port collection and distribution as a research focus, meeting the transportation requirement of port imported goods through road transportation in a scheduling period, and transporting the port goods to a client; and meanwhile, the transportation requirements of export goods of customers are met, and the goods of the customers are transported to a port. Data that can generally be collected are; the method comprises the steps of calculating a target function through an algorithm, wherein the target function is obtained by taking the minimum total cost as an optimization target, taking the application of an unmanned technology as a core, taking the working feasibility in an actual dispatching time period as guidance, and thus optimizing the whole process of port-to-public dispatching.
As an improvement, in the step (1), the objective function Z is
S in the formula (1-1) represents a freight yard station set; s is a certain freight station;
representing a set of customer points; c represents a certain customer point;
representing the speed at which the hub is empty;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
showing a set of personsThe number of times of no-load transportation operation of the card between the clients;
、 
representing the distance from the freight yard to the customer and representing the distance of transport between the customers.
As an improvement, the constraint conditions of the objective function are:
wherein, the formula 1-2 represents the truck slave freight station
The number of times of transportation to the client after boxing is equal to the number of containers to be delivered by the freight station; formulas 1-3 show that the times of transporting the container trucks to the freight station s after being boxed from each customer point are equal to the number of containers required to be received by the freight station; equations 1-4 represent the shipment of the container cards from the various freight yard stations to the customer after boxing
Is equal toCustomer
The number of containers required; equations 1-5 represent the hub slave client
The times of transportation to each freight station after boxing are equal to that of customers
The quantity of containers supplied; equations 1-6 indicate that the container trucks are at freight stations
The number of times of no-load transportation operation between each client; js represents the total number of containers delivered by the freight yard to the customer;
indicating that the freight site receives the total number of containers for the customer;
representing a customer's container demand;
representing the container supply of the customer;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
representing the times of heavy-load transportation operation of the manned trucks from the freight yard station to the customer;
representing the number of heavy haul operations for a manned truck from the customer to the freight yard.
As an improvement, in the step (3), when bipartite graph matching is performed, a specific method is as follows: dividing all tasks to be operated into two sets, dividing a journey-going task set M and a return task set N, setting a freight station as a task starting point, wherein each matching side represents the distance of idle running required by a continuous return task after the journey-going task is completed, and when the maximum matching of bipartite graphs is obtained, the sum of all matching side lengths is the latter half of a simplified objective function.
As an improvement, when the chart formed when the demands between the freight station and the customer point are not balanced does not have a perfect match, if the outbound job task is less than the return job task, the outbound job task is supplemented
One represents a task that starts empty from a freight yard,
the value of (a) is determined by a constraint condition; conversely, if the return task is less than the outbound task, the return task is supplemented
One represents the task of returning from the customer site empty,
the value of (b) is determined by a constraint condition, and m and n are positive integers.
As an improvement, the path is augmented in step (3), specifically, an alternate path is formed by starting from one unmatched point, and sequentially and alternately passing through a unmatched edge and a matched edge to another unmatched point, namely a non-starting point, until the maximum matching is achieved.
As an improvement, when weighted bipartite graph optimal matching processing is performed in the step (3), the specific method comprises the following steps: and initializing the assignment of the vertex, searching for the complete matching, and modifying the value of the feasible top mark when the complete matching is not found until the complete matching of the equal subgraphs is found, namely finishing the optimal matching process with the shortest total path in the no-load mode.
Has the advantages that: the invention provides a position allocation method based on container intelligent management unmanned truck-collecting scheduling, which solves mixed truck-collecting scheduling between a freight station and a client point in the process of transporting containers at a port, establishes a model with the lowest transportation cost as an optimization target through problem analysis, and performs intelligent optimization position allocation through solving and analyzing by adopting a KM algorithm, can flexibly schedule, can reduce the idle rate of secondary transportation tasks by about 40 percent through optimizing the transportation path of a truck-collecting team, obviously improves the overall transportation efficiency and reduces the transportation cost.
Drawings
FIG. 1 is a schematic diagram of the construction of a bipartite graph according to the present invention.
FIG. 2 is a schematic diagram of the amplification path of the present invention.
Detailed Description
The figures of the present invention are further described below in conjunction with the embodiments.
A position allocation method based on intelligent management unmanned container truck scheduling comprises the following specific scheduling steps: (1) constructing an optimization model of the port-road transportation cooperative scheduling, taking the minimum total cost as an optimization target, carrying out allocation transportation of a port container through road transportation within scheduling time, transporting goods to a client, and transporting the goods at the client to a port to finish an optimized line, wherein the total cost is set as a target function Z;
(2) setting as a primary transportation task or a secondary transportation task according to the transportation condition of the container in the port, and solving by adopting an algorithm;
(3) and when the second-level transportation task is performed, solving and analyzing the objective function Z by adopting a Kuhn-Munkres algorithm, performing bipartite graph matching, an augmented path and weighted bipartite graph optimal matching processing through a simplified model, and performing example analysis to obtain the optimal path and box amount of the truck collection pair.
The model is established by taking port collection and distribution as a research focus, meeting the transportation requirement of port imported goods through road transportation in a scheduling period, and transporting the port goods to a client; and meanwhile, the transportation requirements of export goods of customers are met, and the goods of the customers are transported to a port. Data that can generally be collected are; the method comprises the steps of calculating a target function through an algorithm, wherein the target function is obtained by taking the minimum total cost as an optimization target, taking the application of an unmanned technology as a core, taking the working feasibility in an actual dispatching time period as guidance, and thus optimizing the whole process of port-to-public dispatching.
In step (1), the objective function Z is
S in the formula (1-1) represents a freight yard station set; s is a certain freight station;
representing a set of customer points; c represents a certain customer point;
representing the speed at which the hub is empty;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
representing the number of empty transportation operations of the manned card among the customers;
、 
representing the distance from the freight yard to the customer and representing the distance of transport between the customers. The constraints of the objective function are:
wherein, the formula 1-2 represents the truck slave freight station
The number of times of transportation to the client after boxing is equal to the number of containers to be delivered by the freight station; formulas 1-3 show that the times of transporting the container trucks to the freight station s after being boxed from each customer point are equal to the number of containers required to be received by the freight station; equations 1-4 represent the shipment of the container cards from the various freight yard stations to the customer after boxing
Is equal to the number of customers
The number of containers required; equations 1-5 represent the hub slave client
The times of transportation to each freight station after boxing are equal to that of customers
The quantity of containers supplied; equations 1-6 indicate that the container trucks are at freight stations
The number of times of no-load transportation operation between each client; js represents the total number of containers delivered by the freight yard to the customer;
indicating that the freight site receives the total number of containers for the customer;
representing a customer's container demand;
representing the container supply of the customer;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
representing the times of heavy-load transportation operation of the manned trucks from the freight yard station to the customer;
representing the number of heavy haul operations for a manned truck from the customer to the freight yard.
When bipartite graph processing is carried out, each container truck can only load one standard container at a time, the demand of each node is an integer number of standard containers, and the phenomenon of bulk cargo does not exist. All tasks to be operated can be divided into two sets, and if the freight yard station is taken as a task starting point, a outbound task set M and a backhaul task set N can be divided, as shown in fig. 1.
Each matching edge represents the distance of idle running required by the continuous return task after the completion of the outbound task. And when the maximum matching of the graph is obtained, the sum of all the matching side lengths is the latter half of the simplified objective function.
When the demands between the freight yard and the client point are not balanced, a formed graph does not have perfect matching, when the outbound task is less than the return task, m tasks which represent the no-load departure of the freight yard are supplemented in the outbound task, and the value of m is determined by a constraint condition; on the contrary, when the return task is less than the return task, n tasks which represent no-load return from the client point are supplemented in the return task, and the value of n is determined by the constraint condition.
When the augmented path processing is carried out, an alternate path is formed by starting from one unmatched point, sequentially and alternately passing through a unmatched edge and a matched edge to the other unmatched point, namely a non-starting point, until the maximum matching is achieved. As shown in fig. 2, the light-increasing path starts from the unmatched point 3 and alternately passes through the unmatched edge and the matched edge in sequence to the other unmatched point 5. Obviously, the non-matching edge of the augmented path is 1 more than the matching edge. Thus, matching can be improved by interchanging matching and non-matching edges in the augmented path. Because the middle matching node does not have other connected matching edges, the matching property is not changed after the exchange, and the number of the matching edges is increased by 1 compared with the original number.
Further, when the weighted bipartite graph optimal matching processing is performed in the step (3), the specific method comprises the following steps: and initializing the assignment of the vertex, searching for the complete matching, and modifying the value of the feasible top mark when the complete matching is not found until the complete matching of the equal subgraphs is found, namely finishing the optimal matching process with the shortest total path in the no-load mode. To better explain the method of weighted bipartite graph optimal matching, the following example is presented.
Assume that the left vertex set of the bipartite graph is
The right vertex set is
For connection of
Is given the right of
Vertex, point
Top mark is
Vertex, point
Is indicated by the top symbol
Then, for any one of the edges,
this is always true.
If all satisfy from the bipartite graph
The subgraphs (i.e. the equal subgraphs) formed by the edges have a perfect match, and then the perfect match is the maximum weight match of the bipartite graph. For any matching of the bipartite graph, if the matching is contained in the equal subgraph, the sum of the edge weights is equal to the sum of the top marks of all the vertexes; if there is an edge not included in the equal subgraph, the sum of the edge weights is smaller than the sum of the top labels of all the vertices, i.e. the perfect match of the equal subgraph must be the maximum weight match of the bipartite graph.
Example 1
Further, the algorithm is analyzed by way of example, and it is assumed that the port has 2 highway freight stations a and B capable of performing transshipment and turnover operations around the port to serve 10 customer sites around the port, and the distances between the freight stations and the customer sites are shown in table 1 below.
TABLE 1 distance (unit: km) between freight yard and customer site
A plurality of trucks are arranged in each freight yard, and unmanned trucks for completing primary transportation tasks are arranged for standby. The distribution of the specific shipping requirements for each customer site according to job assignment is shown in table 2.
TABLE 2 customer Point demand distribution
The freight yard should serve nearby customer sites, i.e., the yard selected for the shipping task at each customer site can be determined directly from the distance. Therefore, the distribution of the demands of the customer points can obtain that the freight station A mainly serves the customer points 1, 2, 3, 4 and 5, 40 standard boxes of goods need to be delivered, and 25 standard boxes of goods need to be received; freight yard B serves primarily customer site 6, customer site 7, customer site 8, customer site 9 and customer site 10, and needs to deliver 60 standard boxes of goods and receive 25 standard boxes of goods. Since the source of the cargo delivered by the freight yard and the destination of the received cargo are both the ports, the dispatch plan between the port and the freight yard will be determined accordingly.
According to the data, the Kuhn-Munkres algorithm is used for programming and solving to obtain the optimal path and the box amount of the card collecting fleet as shown in the table.
TABLE 3 hybrid fleet optimal Path
By utilizing the characteristic of unmanned truck collection, flexible scheduling is realized, and the empty load rate of a secondary transportation task can be reduced by about 40% by optimizing the transportation path of a truck collection team, so that the overall transportation efficiency is obviously improved, and the transportation cost is reduced.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (7)
1. A position allocation method based on intelligent management unmanned container truck scheduling is characterized in that: the specific scheduling steps are as follows: (1) constructing an optimization model of the port-road transportation cooperative scheduling, taking the minimum total cost as an optimization target, carrying out allocation transportation of a port container through road transportation within scheduling time, transporting goods to a client, and transporting the goods at the client to a port to finish an optimized line, wherein the total cost is set as a target function Z;
(2) setting as a primary transportation task or a secondary transportation task according to the transportation condition of the container in the port, and solving by adopting an algorithm;
(3) and when the second-level transportation task is performed, solving and analyzing the objective function Z by adopting a Kuhn-Munkres algorithm, performing bipartite graph matching, an augmented path and weighted bipartite graph optimal matching processing through a simplified model, and performing example analysis to obtain the optimal path and box amount of the truck collection pair.
2. The method for dispatching based on intelligent container management unmanned truck dispatching as claimed in claim 1, wherein: in step (1), the objective function Z is
1-1, wherein S in the formula (1-1) represents a freight yard station set; s is a certain freight station;
representing a set of customer points; c represents a certain customer point;
representing the speed at which the hub is empty;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
representing the number of empty transportation operations of the manned card among the customers;
、 
representing the distance from the freight yard to the customer and representing the distance of transport between the customers.
3. The method for dispatching based on intelligent container management unmanned truck-pooling dispatching as claimed in claim 2, wherein: the constraints of the objective function are:
wherein, the formula 1-2 represents that the container truck is boxed from the freight station s and then transported to the customerThe number of times is equal to the number of containers to be delivered by the freight yard; formulas 1-3 show that the times of transporting the container trucks to the freight station s after being boxed from each customer point are equal to the number of containers required to be received by the freight station; equations 1-4 show that the number of times a container card is shipped to customer c after being boxed from each freight yard station is equal to the number demand of containers for customer c; equations 1-5 show that the number of times a container card is shipped to each freight yard after being boxed from customer c is equal to the number of containers supplied by customer c; formulas 1-6 represent the number of no-load transportation operations of the container truck between the freight yard s and each customer; js represents the total number of containers delivered by the freight yard to the customer;
indicating that the freight site receives the total number of containers for the customer;
representing a customer's container demand;
representing the container supply of the customer;
the number of times of no-load transportation operation of the person group card between the freight station and the client is shown;
representing the times of heavy-load transportation operation of the manned trucks from the freight yard station to the customer;
representing the number of heavy haul operations for a manned truck from the customer to the freight yard.
4. The method for dispatching based on intelligent container management unmanned truck dispatching as claimed in claim 1, wherein: in the step (3), when bipartite graph matching is performed, the specific method is as follows: dividing all tasks to be operated into two sets, dividing a journey-going task set M and a return task set N, setting a freight station as a task starting point, wherein each matching side represents the distance of idle running required by a continuous return task after the journey-going task is completed, and when the maximum matching of bipartite graphs is obtained, the sum of all matching side lengths is the latter half of a simplified objective function.
5. The method for dispatching based on intelligent container management unmanned truck-pooling dispatching as claimed in claim 4, wherein: when the requirements of the freight yard and the client point are not balanced, the formed graph has no perfect match, if the outbound task is less than the return task, the outbound task is supplemented with a task which represents the no-load departure of the freight yard, and the value of the task is determined by the constraint condition; on the contrary, if the return task is less than the return task, the return task is supplemented with a value representing the no-load return task from the client point, which is determined by the constraint condition, and m and n are positive integers.
6. The method for dispatching based on intelligent container management unmanned truck dispatching as claimed in claim 1, wherein: and (3) increasing the path, specifically, starting from one unmatched point, and sequentially and alternately passing through the unmatched edge and the matched edge to form an alternate path from the other unmatched point, namely the non-starting point, until the maximum matching is achieved.
7. The method for dispatching based on intelligent container management unmanned truck dispatching as claimed in claim 1, wherein: when the weighted bipartite graph optimal matching processing is carried out in the step (3), the specific method comprises the following steps: and initializing the assignment of the vertex, searching for the complete matching, and modifying the value of the feasible top mark when the complete matching is not found until the complete matching of the equal subgraphs is found, namely finishing the optimal matching process with the shortest total path in the no-load mode.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116228079A (en) * | 2023-05-09 | 2023-06-06 | 交通运输部水运科学研究所 | Grain container transportation scheduling method and system |
| CN116502985A (en) * | 2023-06-27 | 2023-07-28 | 鱼快创领智能科技(南京)有限公司 | Efficient waybill matching method for reducing empty rate in scenerization |
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