Method and apparatus for managing vehicle transportation

Abstract

The present invention provides a method and apparatus for managing vehicle transportation, a computer program product, and a computer-readable medium. The method comprises: on the basis of transportation information comprising station information and order information, obtaining an initial transportation route; deleting one or more stations in the initial transportation route to obtain an incomplete transportation route; adding a station regenerated from the deleted one or more stations to the incomplete transportation route, so as to obtain a reconstructed transportation route; and on the basis of an objective function, determining whether the reconstructed transportation route is to be further optimized, wherein the objective function comprises a sum of a milk run cost and a less-than-truck-load (LTL) transportation cost.

Description

Method and device for managing vehicle transportation Technical Field

[0001] In general, the present invention relates to path optimization problems, and more particularly, to a method and apparatus for optimizing vehicle transportation routes. Background Art

[0002] In fields such as logistics, supply chain management, and transportation, vehicle routing optimization problems (e.g., vehicle routing problems, or VRPs) are often needed to improve operational efficiency and control or reduce operating costs. For example, VRPs are a classic combinatorial optimization problem that involves efficiently allocating vehicles to visit multiple sites (e.g., customer sites) while minimizing the total travel distance or cost while satisfying constraints.

[0003] During route optimization, loop transport, or milk run, is a lean transportation method. It uses a single vehicle to load cargo from various stops along the way, enabling efficient delivery of frequent, small-batch orders. However, loop transport also presents challenges such as requiring high system-wide planning and management, or the possibility that a delay at a single stop could lead to delays for all shipments. Less-Than-Truck-Load (LTL) transport is another point-to-point method, offering high reliability but often higher costs.

[0004] Therefore, it is desirable to provide an improved method or technology for optimizing routes to adapt to and meet the increasingly complex and changing transportation scenarios and their needs.

[0005] Summary of the Invention

[0006] The following is a brief overview of one or more embodiments to provide a basic understanding of these embodiments. This overview is not a comprehensive summary of all contemplated embodiments, nor is it intended to identify key or important elements of all embodiments or to delineate the scope of any or all embodiments. Its purpose is simply to provide some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description provided later.

[0007] In one aspect of the present disclosure, a method for managing vehicle transportation is provided, comprising: obtaining an initial transportation route based on transportation information including station information and order information; deleting one or more stations in the initial transportation route to obtain a missing transportation route; adding stations regenerated from the deleted one or more stations to the missing transportation route to obtain a reconstructed transportation route; and determining whether the reconstructed transportation route needs to be further optimized based on an objective function, wherein the objective function includes the sum of a milk run cost and a less-than-truckload (LTL) transportation cost.

[0008] In another aspect of the present disclosure, an apparatus for managing vehicle transportation is provided, comprising a memory storing computer instructions and at least one processor, wherein the at least one processor executes the computer instructions to implement the following steps: obtaining an initial transportation route based on transportation information including station information and order information; deleting one or more stations in the initial transportation route to obtain a missing transportation route; adding stations regenerated from the deleted one or more stations to the missing transportation route to obtain a reconstructed transportation route; and determining whether the reconstructed transportation route needs to be further optimized based on an objective function, wherein the objective function includes the sum of milk run cost and less-than-truckload (LTL) transportation cost.

[0009] In another aspect of the present disclosure, a computer program product for managing vehicle transportation is provided, comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the following steps: obtaining an initial transportation route based on transportation information including station information and order information; deleting one or more stations in the initial transportation route to obtain a missing transportation route; adding stations regenerated from the deleted one or more stations to the missing transportation route to obtain a reconstructed transportation route; and determining whether the reconstructed transportation route needs to be further optimized based on an objective function, wherein the objective function includes the sum of milk run cost and less-than-truckload (LTL) transportation cost.

[0010] In another aspect of the present disclosure, a computer-readable medium for managing vehicle transportation is provided, having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the following steps: obtaining an initial transportation route based on transportation information including station information and order information; deleting one or more stations in the initial transportation route to obtain a missing transportation route; adding stations regenerated from the deleted one or more stations to the missing transportation route to obtain a reconstructed transportation route; and determining whether the reconstructed transportation route needs to be further optimized based on an objective function, wherein the objective function includes the sum of milk run cost and less-than-truckload (LTL) transportation cost.

[0011] By including both LTL transportation cost and round-trip transportation cost in the objective function, both modes of transportation can be taken into account simultaneously during the optimization process, thereby improving the efficiency of optimization. Moreover, by modifying the objective function, compatibility with existing optimization algorithms (for example, Slack Induction by String Removals (SISRs)) can be achieved in a convenient way.

[0012] Other aspects and modifications of the present disclosure will become more apparent upon consideration of the following detailed description and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] FIG1 is a schematic diagram illustrating an example of initial shipping routes for multiple orders, according to one or more aspects of the present disclosure.

[0014] FIG2 is a schematic diagram illustrating an example of a missing transport route after deleting a station in an initial transport route according to one or more aspects of the present disclosure.

[0015] FIG3 shows a schematic diagram of an example of a reconstructed transport route 300 with newly added stations, according to one or more aspects of the present disclosure.

[0016] FIG4 illustrates a flowchart of an exemplary method for path optimization according to one or more aspects of the present disclosure.

[0017] FIG5 illustrates a flowchart of another exemplary method for path optimization according to one or more aspects of the present disclosure.

[0018] FIG6 illustrates an example of a hardware implementation of an apparatus for path optimization according to one or more aspects of the present disclosure. DETAILED DESCRIPTION

[0019] A number of embodiments will now be described with reference to the accompanying drawings, wherein like reference numerals are used to designate like elements herein. In the following description, for ease of explanation, numerous specific details are provided to provide a comprehensive understanding of one or more embodiments. However, it will be apparent that the embodiments may be practiced without these specific details. In other examples, well-known structures and devices are shown in block diagram form to facilitate description of one or more embodiments.

[0020] In daily life and production, there are numerous problems involving routing optimization of varying types or characteristics, resulting in a range of VRP variants. For example, there is the Capacity Constrained Vehicle Routing Problem (CVRP), in which each station has a specific volume to be transported and needs to provide delivery services while satisfying the total vehicle capacity constraints; the Time Window Vehicle Routing Problem (TW), in which each station has a time window in which it can be visited and needs to minimize the total travel distance or cost while satisfying the time window (and total vehicle capacity) of each station; the Hybrid Vehicle Routing Problem (HVRP), in which different types of vehicles are allowed, each with different capacities and costs, and the goal is to minimize the total cost; the Multi-Objective Vehicle Routing Problem (MOVRP), whose main characteristic is that multiple objective functions need to be considered during the optimization process, rather than just a single objective. For example, the following objectives can be optimized simultaneously: minimizing the total travel distance, minimizing the cost, and minimizing the number of vehicles; and the Dynamic Vehicle Routing Problem (DVRP), in which the route is adjusted and optimized in real time based on changes in demand or location of stations after the route has already been executed.

[0021] As demand becomes increasingly complex and variable, the optimization problems involved in actual transportation scenarios may be a combination of one or more of the above variants or other variants. Therefore, how to provide an efficient and flexible route optimization method is the focus of this paper.

[0022] Inspired by the practice of dairy companies collecting milk door-to-door from farms each morning, this model has been applied to the logistics industry, becoming known as circular transport or milk run. Circular transport involves picking up goods at various stations along a designed route and then delivering them to the recipient. This method improves transportation efficiency and volume ratio, significantly reducing costs. However, circular transport also places high demands on route design and execution. Furthermore, response speed is limited to changes in demand (for example, redesigning routes can take a long time). Furthermore, if a problem at a particular station or link in the circular transport process causes a delay, the entire transportation chain will be impacted.

[0023] Less-than-truckload (LTL) transport is another mode of transport that differs from loop transport in that it uses a direct route between the starting and ending points without passing through intermediate points, resulting in higher reliability and timeliness but also higher costs.

[0024] In one or more aspects of the present disclosure, a hybrid method of loop transport and less-than-truckload transport is adopted to design or plan routes, so that a transport method suitable for each order can be adopted according to its characteristics and needs.

[0025] FIG1 illustrates a schematic diagram of an example of an initial transport route 100 for multiple orders, according to one or more aspects of the present disclosure. The multiple orders may include a starting station and a destination station for each order. In this example, the starting stations for orders 1, 2, 3, 4, and 5 are supplier 1 site 120, supplier 2 site 122, supplier 3 site 124, supplier 4 site 126, and supplier 5 site 128, respectively, and the destination stations for orders 1, 2, 3, 4, and 5 are all factory site 110. The initial transport route 100 may utilize a circular transport method, where a vehicle departs from supplier 2 site 122, sequentially passes through supplier 3 site 124, supplier 5 site 128, supplier 4 site 126, and supplier 1 site 120, and ultimately arrives at factory site 110. However, due to accessibility window restrictions or other restrictions or requirements, it may be necessary to arrive at supplier 5 site 128 before arriving at supplier 4 site 126.

[0026] FIG2 illustrates a schematic diagram of an example of a missing transport route 200 after removing sites from the initial transport route 100, according to one or more aspects of the present disclosure. According to the SISR algorithm, one or more sites in the initial transport route 100 can be removed to create a loose structure, providing space for the subsequent addition of optimized site locations. This can also reduce the variables or parameters involved in the optimization, thereby reducing the computational complexity. For example, the one or more sites to be removed or deleted can be selected randomly or through a predetermined rule. In this example, the Supplier 1 site 120 and the Supplier 5 site 128, represented by dashed lines, are removed.

[0027] FIG3 is a schematic diagram showing an example of a reconstructed transport route 300 after re-adding sites according to one or more aspects of the present disclosure. SISR re-adds sites from one or more deleted sites to the route to obtain the current route, and calculates the objective function based on the current route to find the optimal solution. According to one aspect of the present disclosure, the objective function may include both the circular transport cost and the less-than-truckload transport cost, so that both modes of transport can be taken into account or examined at the same time in the process of finding the optimal solution (i.e., the final transport solution). As analyzed above, circular transport and less-than-truckload transport each have their advantages and limitations, and may be suitable for different application scenarios. By including both the circular transport cost and the less-than-truckload transport cost in the objective function, the respective advantages of the two modes of transport can be brought into play, and a balance can be achieved between the circular transport cost and the less-than-truckload transport cost.

[0028] As shown in Figure 3, due to the specific requirements of supplier 5 (for example, time window restrictions), less-than-truckload transportation can be used for order 5, and when the sum of the less-than-truckload transportation cost of order 5 and the cyclic transportation cost of orders 1, 2, 3, and 4 is less than the cyclic transportation cost of orders 1, 2, 3, 4, and 5, the reconstructed transportation route 300 is used to update the initial transportation route 100, and the reconstructed transportation route 300 is used as the input route for the next iteration to repeat the steps of deleting, re-adding, and calculating the cost until the calculated cost is considered to be the lowest (for example, with a certain probability), and the optimal path is obtained.

[0029] It should be understood that the examples in Figures 1, 2 and 3 are merely exemplary, and in other examples that do not depart from the scope of the present disclosure, there may be more or fewer stations (for example, including the starting and ending stations in each order and / or other stations (for example, relay stations, etc.)), and the final transportation plan may include all stations or part of the stations in the initial transportation route (for example, all deleted stations are re-added after one or more iterations or part of all deleted stations are re-added).

[0030] 4 shows a flowchart of an exemplary method 400 for path optimization according to one or more aspects of the present disclosure. In step 410, an initial transport route (e.g., initial transport route 100) can be obtained based on transport information including station information and order information. For example, the station information can include an identification (ID) and location information of each station, such as longitude and latitude, as well as a service time window (e.g., 8 a.m. to 8 p.m.) and a service time for loading and unloading (e.g., 1 hour) of each station. The order information can include an order list, with each row entry in the list representing an order. Each order can include the name or ID of the starting station and the end station, the transport volume (e.g., 15, 50, or 100 pallets), the minimum transport volume for each transport (e.g., 3 pallets as the minimum unit of transport), and the cost when using less-than-truckload transport.

[0031] In step 420, one or more sites in the initial transport route can be deleted based on a random manner and / or a predetermined rule to obtain a missing transport route. For example, in one iteration, a site is randomly selected from all sites as a random seed, the other sites are sorted according to the distance from the random seed, and it is determined in turn whether the current site is to be deleted. For another example, the maximum number of sites allowed to be included in a route can be preset (for example, 10). One or more sites in the route that exceeds the maximum number of sites are deleted. For another example, it can be determined based on a comparison between a random number and a threshold whether to delete an order that uses less-than-truckload transport and the associated sites, and if it is determined that an order that uses less-than-truckload transport is to be deleted, the order can be deleted and treated as an unadded order. In one example, if the starting site or the end site of an order is a deleted site, the order can be deleted and the deleted order can be treated as an unadded order.

[0032] At step 430, selected stations from the deleted one or more stations are added back to the missing transport route to obtain a reconstructed transport route. For example, the deleted stations may be randomly sorted and placed into the existing route based on the requirements or constraints in the transport information. This re-addition step may process the deleted stations sequentially, calculate the insertion positions for each, and select the optimal position for insertion.

[0033] In step 440, the total cost of the current reconstructed transport route is calculated based on the objective function that includes the recurring transport cost and the less-than-truckload transport cost to determine whether further optimization is required. If further optimization is determined, steps 420, 430, and 440 are repeated in the next iteration, as indicated by the dashed arrows. For example, the optimization process can begin with the initial transport route and continuously optimize the current transport route by incorporating factors such as deletion and re-addition strategies and temperature control in simulated annealing. In each iteration, the total cost of the current transport route is calculated and accepted with a certain probability to avoid falling into a local optimum.

[0034] Figure 5 shows a flowchart of another exemplary method 500 for path optimization according to one or more aspects of the present disclosure. Steps 510 and 520 are similar to steps 410 and 420 in method 400. In step 525, it is determined whether the current order adopts less-than-truckload transportation or loop transportation. For example, the judgment can be based on the constraints in the transportation information, such as whether the current order needs to adopt loop transportation or whether it needs to adopt less-than-truckload transportation. For example, it can be determined which mode of transportation is adopted for the current order based on the comparison between a random number and a threshold. If it is determined that the less-than-truckload transportation mode is adopted, proceed to step 530 to generate a new less-than-truckload transportation route; if it is determined that the loop transportation mode is adopted, proceed to step 532, wherein the current order can be added to the existing loop path, for example, a greedy algorithm can be used to determine the optimal position.

[0035] In step 540, similar to step 440 in method 400, the total cost of the current reconstructed transport route is calculated based on an objective function that includes the cycle transport cost and the less-than-truckload transport cost to determine whether further optimization is required. If further optimization is required, as indicated by the dashed arrow, steps 520, 525, 530, or 532 may be repeated.

[0036] FIG6 illustrates an example of a hardware implementation of an apparatus 600 for path optimization according to one or more aspects of the present disclosure. The apparatus 600 for path optimization may include a memory 610 and at least one processor 620. The processor 620 may be coupled to the memory 610 and configured to perform the methods or steps described above with reference to FIG1 , FIG2 , FIG3 , FIG4 , and FIG5 . The processor 620 may be a general-purpose processor or may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or other such structures. The memory 610 may store current data in a loop iteration, data generated by the processor 620, and / or instructions executed by the processor 620.

[0037] The various units, components, operations, models, and networks described in conjunction with the present disclosure may be implemented as hardware, software executed by a processor, firmware, or any combination thereof. According to one or more aspects of the present disclosure, a computer program product for path optimization may include a processor-executable computer code for executing one or more of the methods or steps described above with reference to Figures 1, 2, 3, 4, and 5. According to other aspects of the present disclosure, a computer-readable medium may store computer code for path optimization that, when executed by a processor, may cause the processor to execute one or more of the methods or steps described above with reference to Figures 1, 2, 3, 4, and 5. Computer-readable media include both non-temporary computer storage media and communication media, and communication media include any media that facilitates transferring a computer program from one location to another. Any connection may be appropriately referred to as a computer-readable medium.

[0038] In one example, before deleting or removing a site, hyperparameters may be set, such as the maximum or minimum number of affected paths or the degree of damage. By setting the hyperparameters, it is possible to control the deletion of a sufficient number of sites to provide a sufficiently loose structure, thereby providing sufficient space for path optimization and adjustment.

[0039] In one example, the transport information may also include vehicle information, which may include vehicle type, capacity, pricing, and speed. For example, a tailgate truck or a van may be used for different orders.

[0040] In one example, in the step of re-adding the deleted site, the transport route can be reconstructed based on various constraints or conditions in the transport information. For example, the site can be re-added based on at least one of the service time window, the service time for loading and unloading, the maximum delivery frequency (i.e., the multiple between the transport volume of the order and the minimum transport volume), and the vehicle information. Since the price of a van is lower than that of a tailgate truck at the same capacity, in the example of FIG3 , the optimized route may include a LTL transport route from supplier 5 site 128 to factory site 110 for order 5 transported by a van, and a circular transport route from supplier 2 site 122, supplier 3 site 124, supplier 4 site 126 and supplier 1 site 120 to factory site 110 for orders 1, 2, 3, and 4 transported by a tailgate truck in sequence.

[0041] In one example, the selection of a threshold for selecting LTL or loop shipping can affect the speed of convergence. To speed up convergence, for example, the threshold can be set to 0.1, and when a random number (e.g., the random number can have a value range of [0, 0.2]) is less than the threshold, LTL shipping is selected for the current order.

[0042] In one example, in the step of re-adding the deleted site (e.g., step 430), the site to be re-added can be selected based on the orders that have not been added. The orders that have not been added can be re-ordered (e.g., randomly ordered) to prevent falling into a local optimal solution.

[0043] In one example, the result of the route optimization may include route information for each order or order information for each route, or both.

[0044] The above description of the present disclosure is provided to enable those skilled in the art to use or implement the various embodiments. Various modifications to the above embodiments will be apparent to those skilled in the art, and the basic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Therefore, the scope of the claims is not intended to be limited to the embodiments disclosed herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing vehicle transportation, comprising: Obtaining an initial transportation route based on transportation information including site information and order information; Deleting one or more stations in the initial transport route to obtain the missing transport route; adding the regenerated site from the deleted one or more sites to the missing transport route to obtain a reconstructed transport route; as well as Based on an objective function, it is determined whether the reconstructed transportation route is to be further optimized, wherein the objective function includes the sum of a milk run cost and a less-than-truckload (LTL) transportation cost.

2. The method according to claim 1, wherein When it is determined that further optimization is to be performed, the steps of deleting, adding and determining are repeated until the current reconstructed transport route is determined to be an optimized route.

3. The method according to claim 1, wherein The order information includes information of multiple orders, wherein the information of each order includes the name or identification of the starting site and the ending site.

4. The method according to claim 3, wherein: The step of adding a station regenerated from the deleted one or more stations to the missing transport route further comprises: For the orders that have not been added, whether to adopt loop transportation or LTL transportation is determined based on the comparison between the random number and the threshold.

5. The method according to claim 1, wherein The order information includes the maximum delivery frequency; The station information includes the location of the station, service time window and loading and unloading service time; And the transportation information also includes vehicle information, and the vehicle information includes model, capacity and pricing.

6. The method according to claim 5, wherein: The step of adding a station regenerated from the deleted one or more stations to the missing transport route further comprises: A regenerated site is selected from the one or more deleted sites based on at least one of the service time window, the loading and unloading service time, the maximum delivery frequency, or the vehicle type.

7. The method according to claim 1, wherein The step of deleting one or more sites further comprises: Determine whether to delete the order using LTL shipping based on a comparison between the random number and a threshold; or Based on the maximum number of stops allowed in a route, determine whether to delete a stop and the order corresponding to the stop.

8. A device for managing vehicle transportation, comprising: memory, which stores computer instructions; as well as One or more processors, wherein the one or more processors execute the computer instructions to implement the steps of any one of claims 1 to 7.

9. A computer program product for managing vehicle transportation, comprising computer instructions, wherein: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable medium for managing vehicle transportation, having computer instructions stored thereon, wherein: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images