Multi-Vehicle-Type Supply Chain Scheduling Method and Electronic Device Based on Improved Ant Colony Algorithm

Abstract

The present invention relates to artificial intelligence technology, and discloses a multi-vehicle type supply chain scheduling method based on an improved ant colony algorithm, including: constructing an initial vehicle route according to the number of vehicles and demand points, randomly optimizing the initial vehicle route to obtain multiple optimized vehicle routes, calculating a transportation constraint factor set by using relevant transportation information of transportation vehicles and constructing a transportation scheduling model, using the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm, using the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and scheduling multiple optimized vehicle routes according to the route quality evaluation result. The present invention also proposes a multi-vehicle type supply chain scheduling device, an electronic device, and a computer-readable storage medium based on the improved ant colony algorithm. The present invention can solve the problem of relatively low accuracy in multi-vehicle type supply chain scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and particularly to a multi-vehicle supply chain scheduling method, device, electronic device and computer-readable storage medium based on an improved ant colony algorithm. Background Art

[0002] With the continuous development of the economy and the promotion of global economic integration, it has become a reality to allocate resources worldwide. Global procurement and global marketing have become the common goals and strategic behaviors of enterprises. At the same time, the importance of logistics has become increasingly prominent. Modern logistics organically combines transportation, warehousing, loading and unloading, processing, sorting, distribution, information, etc. to form a complete supply chain, providing users with multi-functional and integrated comprehensive services. Among them, the problem of vehicle supply chain scheduling is also very difficult.

[0003] Existing multi-vehicle supply chain scheduling methods usually consider vehicle scheduling in terms of distribution costs and do not take into account multiple dimensional factors affecting vehicle scheduling, thereby affecting the accuracy of multi-vehicle supply chain scheduling. Therefore, there is an urgent need to propose a multi-vehicle supply chain scheduling method with higher accuracy. Summary of the Invention

[0004] The present invention provides a multi-vehicle supply chain scheduling method, device and computer-readable storage medium based on an improved ant colony algorithm, and its main purpose is to solve the problem of low accuracy of multi-vehicle supply chain scheduling based on an improved ant colony algorithm.

[0005] To achieve the above object, a multi-vehicle supply chain scheduling method based on an improved ant colony algorithm provided by the present invention includes:

[0006] Obtain the number of vehicles of the transport vehicles in the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points;

[0007] Randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes;

[0008] Calculate a set of transportation constraint factors obtained by summarizing a transportation path smoothness factor, a transportation cost factor, a transportation time factor, a transportation fuel consumption factor and a load constraint factor by using the relevant transportation information of the transport vehicles;

[0009] Construct a transportation scheduling model based on the set of transportation constraint factors, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm;

[0010] Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes, obtain the route quality evaluation results, and schedule multiple optimized vehicle routes according to the route quality evaluation results.

[0011] Optionally, constructing the initial vehicle route based on the number of vehicles and the demand points includes:

[0012] Start the transport vehicles in the vehicle distribution center with the number of vehicles as the starting number of vehicles, and calculate the distance values between the transport vehicles and the demand points respectively;

[0013] Select the demand points with distance values less than or equal to the preset reference threshold among multiple demand points as target demand points;

[0014] Add the target demand points to the pre-constructed route template to obtain a reference transport route, and calculate the remaining space of the vehicles and the service required time in the reference transport route respectively;

[0015] Determine the set of routes to be added according to the remaining space of the vehicles and the service required time;

[0016] Calculate the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula;

[0017] Insert the target demand point into the minimum cost insertion point in the route with the minimum insertion cost. When all the target demand points are added, output the initial vehicle route.

[0018] Optionally, calculating the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula includes:

[0019] The preset insertion cost formula is:

[0020]

[0021] Among them, represents the insertion cost, i 1 represents the number of the demand point before the insertion position of customer point i, i 2 represents the number of the demand point after the insertion position of customer point i, w 1 and w 2 represent the penalty rates for the service time of the inserted customer point being earlier or later than the time window respectively, TE and TL represent the early arrival and late arrival times respectively, represents the i 1 th route to be added, v k represents the preset insertion frequency parameter.

[0022] Optionally, randomly optimize the initial vehicle route according to the four probability-based exchange operators to obtain multiple optimized vehicle routes, including:

[0023] Identify the route type of the initial vehicle route;

[0024] When the route type of the initial vehicle route is the in-vehicle route type, disconnect two non-adjacent edges in the initial vehicle route;

[0025] Form a new first edge from the first vehicle customer points of the first and second routes, form a new second edge from the second vehicle customer points of the first and second routes, and then reverse the route in the middle of the two edges to obtain multiple optimized vehicle routes;

[0026] When the route type of the initial vehicle route is the inter-vehicle route type, randomly select two routes in the initial vehicle route and separately disconnect one edge of each;

[0027] Connect the first part of the disconnected first route to the reverse route of the first part of the disconnected second route to generate a new route as the optimized vehicle route; or

[0028] Connect the reversed second part of the disconnected second route to the reverse route of the second part of the disconnected first route to generate a new route as the optimized vehicle route.

[0029] Optionally, constructing a transportation scheduling model based on the transportation constraint factor set includes:

[0030] Obtain a preset number of reference weights and assign the multiple reference weights to the transportation path smoothness factor, the transportation cost factor, the transportation time factor, the transportation fuel consumption factor, and the load constraint factor in the transportation constraint factor set;

[0031] Construct a transportation scheduling formula based on the multiple reference weights and the transportation constraint factor set, and construct a corresponding transportation scheduling model according to the transportation scheduling formula.

[0032] Optionally, using the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm, including:

[0033] Use the basic ant colony algorithm to perform path search on multiple optimized vehicle routes to obtain the worst-quality path and the highest-quality path;

[0034] Statistically calculate the length of the worst path corresponding to the worst-quality path and the length of the highest path corresponding to the highest-quality path;

[0035] Construct a pheromone update operation formula based on the worst path length, the highest path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm;

[0036] Use the pheromone update operation formula to update the basic ant colony algorithm to obtain an improved ant colony algorithm.

[0037] Optionally, the constructing a pheromone update operation formula based on the worst path length, the highest path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm includes:

[0038]

[0039] where τ ij (t + 1) is the pheromone update operation value, B worst represents the worst path, |B worst | represents the worst path length, B best represents the highest path, |B best | represents the highest path length, η represents the pheromone enhancement factor, X(j) is the transportation scheduling model, and δ represents a preset pheromone parameter.

[0040] To solve the above problems, the present invention also provides a multi - vehicle type supply chain scheduling device based on an improved ant colony algorithm. The device includes:

[0041] A vehicle route optimization module, configured to obtain the number of transport vehicles at a vehicle distribution center and the demand points corresponding to the vehicle distribution center, construct an initial vehicle route based on the number of vehicles and the demand points, and randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes;

[0042] A constraint factor summarization module, configured to calculate a set of transportation constraint factors obtained by summarizing a transportation path smoothness factor, a transportation cost factor, a transportation time factor, a transportation fuel consumption factor, and a load constraint factor by using relevant transportation information of the transport vehicles;

[0043] An ant colony algorithm optimization module, configured to construct a transportation scheduling model based on the set of transportation constraint factors, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm;

[0044] A vehicle route scheduling module, configured to use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0045] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0046] a memory storing at least one instruction; and

[0047] A processor executes instructions stored in the memory to implement the above-mentioned multi-vehicle supply chain scheduling method based on the improved ant colony algorithm.

[0048] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned multi-vehicle supply chain scheduling method based on the improved ant colony algorithm.

[0049] In the embodiment of the present invention, the initial vehicle route is constructed by the number of vehicles and demand points. The initial vehicle route reflects the route relationship between the transport vehicle and the demand point. The initial vehicle route is randomly optimized according to four probability-based exchange operators to obtain multiple optimized vehicle routes. The four probability-based exchange operators can improve the richness of random optimization, so that multiple optimized vehicle routes can contain various route combinations to the greatest extent. The relevant transportation information of the transport vehicle is used to calculate a set of transportation constraint factors obtained by summarizing the transportation path smoothness factor, transportation cost factor, transportation time factor, transportation fuel consumption factor and load constraint factor, and a transportation scheduling model is constructed based on the transportation constraint factor set. The transportation scheduling model is used as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm. The improved ant colony algorithm can realize accurate evaluation of the optimized vehicle route, and schedule multiple optimized vehicle routes according to the route quality evaluation results. Improve the overall transportation efficiency and reduce transportation costs. Therefore, the multi-model supply chain scheduling method, device, electronic device and computer-readable storage medium based on the improved ant colony algorithm proposed in the present invention can solve the problem of low accuracy of multi-model supply chain scheduling based on the improved ant colony algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart of a multi-model supply chain scheduling method based on an improved ant colony algorithm provided in one embodiment of the present invention;

[0051] Figure 2 A functional module diagram of a multi-vehicle supply chain scheduling device based on an improved ant colony algorithm provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of the structure of an electronic device for implementing the multi-vehicle supply chain scheduling method based on the improved ant colony algorithm provided in one embodiment of the present invention.

[0053] The implementation, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] The embodiment of the present application provides a multi-vehicle supply chain scheduling method based on an improved ant colony algorithm. The execution subject of the multi-vehicle supply chain scheduling method based on the improved ant colony algorithm includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the multi-vehicle supply chain scheduling method based on the improved ant colony algorithm can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0056] Referring to Figure 1 As shown, it is a schematic flow chart of a multi-vehicle supply chain scheduling method based on an improved ant colony algorithm provided by an embodiment of the present invention. In this embodiment, the multi-vehicle supply chain scheduling method based on the improved ant colony algorithm includes:

[0057] S1. Obtain the number of vehicles of the transport vehicles in the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points.

[0058] In the embodiment of the present invention, the vehicle distribution center refers to a supplier that provides vehicles. Among them, the vehicles can be vehicles of different models such as large trucks, forklifts, small trucks, etc. The demand points corresponding to the vehicle distribution center refer to retailers that need to receive goods. Since the main purpose of transport vehicles is to transport goods, there is one supplier and multiple retailers in the supply chain. The supplier can use at most M vehicles to deliver goods to N retailers, and each retailer must be delivered and can only be delivered once. That is, the vehicle distribution center and the demand points corresponding to the vehicle distribution center are in a one-to-many relationship.

[0059] Specifically, the constructing of the initial vehicle route based on the number of vehicles and the demand points includes:

[0060] Start the transport vehicles in the vehicle distribution center with the number of vehicles as the starting number of vehicles, and calculate the distance values between the transport vehicles and the demand points respectively;

[0061] Screen the demand points with distance values less than or equal to a preset reference threshold among multiple demand points as target demand points;

[0062] Add the target demand point to the pre-constructed route template to obtain a reference transportation route, and calculate the remaining space of the vehicle and the required service time in the reference transportation route respectively;

[0063] Determine the set of routes to be added according to the remaining space of the vehicle and the required service time;

[0064] Calculate the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula;

[0065] Insert the target demand point into the minimum cost insertion point in the route with the minimum insertion cost. When all the target demand points are added, output the initial vehicle route.

[0066] Specifically, the distance value between the transport vehicle and the demand point can be calculated by using the existing distance value calculation formula. Among them, the distance value calculation formula can be the Euclidean distance calculation formula. The pre-constructed route template refers to a pre-constructed reference template that can be directly filled in, and the connection method between different routes is included in the route template.

[0067] Further, the calculating the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula includes:

[0068] The preset insertion cost formula is:

[0069]

[0070] Among them, represents the insertion cost, i 1 represents the number of the previous demand point of the insertion position of customer point i, i 2 represents the number of the next demand point of the insertion position of customer point i, w 1 and w 2 respectively represent the penalty rates for the service time of the inserted customer point being earlier or later than the time window, and TE and TL respectively represent the early arrival and late arrival times, represents the i 1 th route to be added, v k represents the preset insertion frequency parameter.

[0071] S2. Randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes.

[0072] In the embodiment of the present invention, the four exchange operators based on probability are respectively the SWAP operator, the RELOCATE operator, the 2-Opt operator and the 2-Opt * operator.

[0073] Specifically, randomly optimizing the initial vehicle route according to four probability-based exchange operators to obtain multiple optimized vehicle routes, including:

[0074] Identifying multiple vehicle customer points in the initial vehicle route;

[0075] Based on the exchange operator, swapping the positions of any two customer points at different positions among the multiple vehicle customer points to obtain multiple optimized vehicle routes.

[0076] Specifically, the exchange operator is the SWAP operator. The function of the SWAP operator is to swap the positions of two vehicle customer points at different positions. The swapped vehicle customer points can be within the route or between routes.

[0077] In another embodiment of the present invention, randomly optimizing the initial vehicle route according to four probability-based exchange operators to obtain multiple optimized vehicle routes, including:

[0078] Identifying any one vehicle customer point in the initial vehicle route and resetting the vehicle customer point to a preset position.

[0079] Specifically, the exchange operator is the RELOCATE operator. The RELOCATE operator is to reset any one vehicle customer point to another position, which can be within the route or between routes.

[0080] In another embodiment of the present invention, randomly optimizing the initial vehicle route according to four probability-based exchange operators to obtain multiple optimized vehicle routes, including:

[0081] Identifying the route type of the initial vehicle route;

[0082] When the route type of the initial vehicle route is the in-vehicle route type, disconnecting two non-adjacent edges in the initial vehicle route;

[0083] Forming a new first edge from the first vehicle customer points of the first and second routes, forming a new second edge from the second vehicle customer points of the first and second routes, and then reversing the route in the middle of the two edges to obtain multiple optimized vehicle routes;

[0084] When the route type of the initial vehicle route is the between-vehicle route type, randomly selecting two routes in the initial vehicle route and separately disconnecting one edge of each;

[0085] Connecting the front part of the first route after disconnection to the reverse route of the front part of the second route after disconnection to generate a new route as the optimized vehicle route; or

[0086] The new route generated by reversing a part of the second route and connecting it in reverse to the reversed part of the first route is used as the optimized vehicle route.

[0087] Specifically, the swap operator is the 2-pt operator.

[0088] In another embodiment of the present invention, randomly optimizing the initial vehicle route according to the four swap operators based on probability to obtain multiple optimized vehicle routes includes:

[0089] Select two vehicle customer points in the initial vehicle route respectively, and swap the routes of the two vehicle customer points to obtain multiple optimized vehicle routes.

[0090] Specifically, the swap operator is 2-Opt * operator.

[0091] S3. Calculate a set of transportation constraint factors obtained by summarizing the transportation path smoothness factor, transportation cost factor, transportation time factor, transportation fuel consumption factor, and load constraint factor by using the relevant transportation information of the transportation vehicle.

[0092] In the embodiment of the present invention, the relevant transportation information of the transportation vehicle includes relevant reference data such as the road smoothness of the transportation vehicle, the actual cost of the transportation vehicle, the time consumed by the transportation vehicle, and the fuel consumption per distance of the transportation vehicle.

[0093] Specifically, calculating the transportation path smoothness factor by using the relevant transportation information of the transportation vehicle includes:

[0094]

[0095] Among them, X 1 (j) is the transportation path smoothness factor, Dk j is the smoothness of the route passed by the transportation vehicle when driving to node j, and Dk jmax is the tolerance limit value of the road passed by the transportation vehicle for smoothness.

[0096] Further, calculating the transportation cost factor by using the relevant transportation information of the transportation vehicle includes:

[0097]

[0098] Among them, X 2 (j) is the transportation cost factor, e j is the highest actual transportation cost, and e jmax is the estimated highest transportation cost.

[0099] Specifically, calculating the transportation time factor by using the relevant transportation information of the transportation vehicle includes:

[0100]

[0101] Among them, X 3 (j) is the transportation time factor, T j is the time used by the actual logistics transportation vehicle, T jmax is the estimated time used by the logistics vehicle.

[0102] Furthermore, calculating the transportation fuel consumption factor by using the relevant transportation information of the transportation vehicle includes:

[0103]

[0104] Among them, a represents the transportation vehicle, FC a represents the fuel consumption per unit distance of vehicle a.

[0105] Specifically, calculating the load constraint factor by using the relevant transportation information of the transportation vehicle includes:

[0106]

[0107] Among them, VL a represents the load capacity of vehicle a, MD a represents the maximum delivery distance of vehicle a, d j represents the cargo demand at the target delivery point j.

[0108] S4. Construct a transportation scheduling model based on the transportation constraint factor set, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm.

[0109] In the embodiment of the present invention, constructing the transportation scheduling model based on the transportation constraint factor set includes:

[0110] Obtain a plurality of preset reference weights, and assign the plurality of reference weights to the transportation path smoothness factor, the transportation cost factor, the transportation time factor, the transportation fuel consumption factor, and the load constraint factor in the transportation constraint factor set;

[0111] Construct a transportation scheduling formula based on the plurality of reference weights and the transportation constraint factor set, and construct a corresponding transportation scheduling model according to the transportation scheduling formula.

[0112] Specifically, the multiple reference weights refer to pre-set weight factors available for downward allocation. Allocating the multiple reference weights to the transportation path smoothness factor, the transportation cost factor, the transportation time factor, the transportation fuel consumption factor, and the load constraint factor in the transportation constraint factor set may mean allocating the reference weight w1 to the transportation path smoothness factor X 1 (j), allocating the reference weight w2 to the transportation cost factor X 2 (j), allocating the reference weight w3 to the transportation time factor X 3 (j), allocating the reference weight w4 to the transportation fuel consumption factor FC a , allocating the reference weight w5 and the reference weight w6 to the vehicle load capacity VL and the vehicle delivery distance MD in the load constraint factor respectively a and a .

[0113] Specifically, constructing the transportation scheduling formula based on the multiple reference weights and the transportation constraint factor set includes:

[0114] X(j) = w1X 1 (j) + w2X 2 (j) + w3X 3 (j) + w4FC a + w5VL a + w6MD a

[0115] where X(j) is the transportation scheduling value, X 1 (j) is the transportation path smoothness factor, X 2 (j) is the transportation cost factor, X 3 (j) is the transportation time factor, VL a represents the load capacity of vehicle a, MD a represents the maximum delivery distance of vehicle a, and w1, w2, w3, w4, w5, and w6 are reference weights.

[0116] Furthermore, using the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain the improved ant colony algorithm includes:

[0117] Using the basic ant colony algorithm to perform path search on multiple optimized vehicle routes to obtain the path with the worst quality and the path with the best quality;

[0118] Statistical the worst path length corresponding to the worst path and the best path length corresponding to the best path;

[0119] Construct a pheromone update operation formula based on the worst path length, the highest path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm;

[0120] Use the pheromone update operation formula to update the basic ant colony algorithm to obtain an improved ant colony algorithm.

[0121] Specifically, the basic ant colony algorithm refers to a bionic algorithm in which ants search for food by exploring routes. It belongs to an artificial intelligence optimization algorithm. Based on rules such as pheromone concentration, it determines the forward routes of ants searching for food. Therefore, using the basic ant colony algorithm to search for paths for multiple optimized vehicle routes can find the worst-quality path and the highest-quality path during the path search process.

[0122] Specifically, constructing the pheromone update operation formula based on the worst path length, the highest path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm includes:

[0123]

[0124] Among them, τ ij (t + 1) is the pheromone update operation value, B worst represents the worst path, |B worst | represents the worst path length, B best represents the highest path, |B best | represents the highest path length, η represents the pheromone enhancement factor, X(j) is the transportation scheduling model, and δ represents a preset pheromone parameter.

[0125] Specifically, the pheromone concentration in the improved ant colony algorithm has been improved, so it can accurately and efficiently search for higher-quality optimized vehicle routes.

[0126] S5. Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0127] In the embodiment of the present invention, the improved ant colony algorithm can avoid the defects of the basic ant colony algorithm and at the same time search for the optimal path, and it is easy to appear local optimal solutions. Therefore, using the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes can obtain a route quality evaluation result.

[0128] Specifically, scheduling multiple optimized vehicle routes according to the route quality evaluation result includes:

[0129] When a vehicle scheduling request is received, an optimized vehicle route whose route quality evaluation result meets the preset standard is selected from multiple optimized vehicle routes as the vehicle route to be scheduled;

[0130] Parse the vehicle scheduling request to obtain a scheduling parsing text, and perform a start operation on the vehicle route to be scheduled according to the scheduling parsing text.

[0131] Specifically, the route quality evaluation result is used as a standard for screening optimized vehicle routes. An optimized vehicle route whose route quality evaluation result meets the preset standard is screened from multiple optimized vehicle routes, where the preset standard is that the route quality is medium or above medium.

[0132] In an embodiment of the present invention, an initial vehicle route is constructed through the number of vehicles and demand points. The initial vehicle route reflects the route relationship between transport vehicles and demand points. The initial vehicle route is randomly optimized according to four exchange operators based on probability to obtain multiple optimized vehicle routes. The four exchange operators based on probability can improve the richness of random optimization, so that multiple optimized vehicle routes can include various route combination situations to the greatest extent. The relevant transport information of the transport vehicle is used to calculate a transport constraint factor set obtained by summarizing a transport path smoothness factor, a transport cost factor, a transport time factor, a transport fuel consumption factor, and a load constraint factor. A transport scheduling model is constructed based on the transport constraint factor set, and the transport scheduling model is used as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm. The improved ant colony algorithm can accurately evaluate the optimized vehicle route, and schedule multiple optimized vehicle routes according to the route quality evaluation result. The overall transport efficiency is improved and the transport cost is reduced. Therefore, the multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm proposed by the present invention can solve the problem of low accuracy of multi-vehicle type supply chain scheduling based on the improved ant colony algorithm.

[0133] As Figure 2 shown, it is a functional module diagram of a multi-vehicle type supply chain scheduling device based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0134] The multi-vehicle type supply chain scheduling device 100 based on the improved ant colony algorithm of the present invention can be installed in an electronic device. According to the functions implemented, the multi-vehicle type supply chain scheduling device 100 based on the improved ant colony algorithm can include a vehicle route optimization module 101, a constraint factor summarization module 102, an ant colony algorithm optimization module 103, and a vehicle route scheduling module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0135] In this embodiment, the functions of each module / unit are as follows:

[0136] The vehicle route optimization module 101 is configured to obtain the number of vehicles of the transport vehicles at the vehicle distribution center and the demand points corresponding to the vehicle distribution center, construct an initial vehicle route based on the number of vehicles and the demand points, and randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes;

[0137] The constraint factor summary module 102 is configured to calculate a set of transport constraint factors obtained by summarizing a transport path smoothness factor, a transport cost factor, a transport time factor, a transport fuel consumption factor, and a load constraint factor by using the relevant transport information of the transport vehicles;

[0138] The ant colony algorithm optimization module 103 is configured to construct a transport scheduling model based on the set of transport constraint factors, and use the transport scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm;

[0139] The vehicle route scheduling module 104 is configured to use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0140] Specifically, the specific implementation manners of each module of the multi-vehicle-type supply chain scheduling device 100 based on the improved ant colony algorithm are as follows:

[0141] Step 1: Obtain the number of vehicles of the transport vehicles at the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points.

[0142] In the embodiment of the present invention, the vehicle distribution center refers to a supplier that provides vehicles. Among them, the vehicles can be vehicles of different models such as large trucks, forklifts, and small trucks. The demand points corresponding to the vehicle distribution center refer to retailers that need to receive goods. Since the main purpose of transport vehicles is to transport goods, there is one supplier and multiple retailers in the supply chain. The supplier can use at most M vehicles to deliver goods to N retailers, and each retailer must be delivered and can only be delivered once. That is, the vehicle distribution center and the demand points corresponding to the vehicle distribution center have a one-to-many relationship.

[0143] Specifically, constructing the initial vehicle route based on the number of vehicles and the demand points includes:

[0144] Start the transport vehicles in the vehicle distribution center with the number of vehicles as the starting number, and calculate the distance values between the transport vehicles and the demand points respectively;

[0145] Select the demand points with distance values less than or equal to the preset reference threshold among multiple demand points as target demand points;

[0146] Add the target demand points to the pre - constructed route template to obtain a reference transport route, and calculate the remaining space of the vehicles and the service required time in the reference transport route respectively;

[0147] Determine the set of routes to be added according to the remaining space of the vehicles and the service required time;

[0148] Calculate the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula;

[0149] Insert the target demand point into the minimum - cost insertion point in the route with the minimum insertion cost. When all the target demand points are added, output the initial vehicle route.

[0150] Specifically, the distance value between the transport vehicle and the demand point can be calculated using the existing distance value calculation formula, where the distance value calculation formula can be the Euclidean distance calculation formula. The pre - constructed route template refers to a pre - constructed reference template that can be directly filled in, and the connection methods between different routes are included in the route template.

[0151] Further, the calculating the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula includes:

[0152] The preset insertion cost formula is:

[0153]

[0154] Where, represents the insertion cost, i 1 represents the number of the demand point before the insertion position of customer point i, i 2 represents the number of the demand point after the insertion position of customer point i, w 1 and w 2 represent the penalty rates for the service time of the inserted customer point being earlier or later than the time window respectively, TE and TL represent the early arrival and late arrival times respectively, represents the i 1 th route to be added, v k represents the preset insertion frequency parameter.

[0155] Step 2: Randomly optimize the initial vehicle route according to four probability-based exchange operators to obtain multiple optimized vehicle routes.

[0156] In the embodiment of the present invention, the four probability-based exchange operators are respectively the SWAP operator, the RELOCATE operator, the 2-Opt operator, and the 2-Opt * operator.

[0157] Specifically, the random optimization of the initial vehicle route according to the four probability-based exchange operators to obtain multiple optimized vehicle routes includes:

[0158] Identify multiple vehicle customer points in the initial vehicle route;

[0159] Based on the exchange operator, exchange the positions of any two customer points at different positions among the multiple vehicle customer points to obtain multiple optimized vehicle routes.

[0160] Specifically, the exchange operator is the SWAP operator. The function of the SWAP operator is to exchange the positions of two vehicle customer points at different positions. The exchanged vehicle customer points can be within the route or between routes.

[0161] In another embodiment of the present invention, the random optimization of the initial vehicle route according to the four probability-based exchange operators to obtain multiple optimized vehicle routes includes:

[0162] Identify any one vehicle customer point in the initial vehicle route and reset the vehicle customer point to a preset position.

[0163] Specifically, the exchange operator is the RELOCATE operator. The RELOCATE operator is to reset any one vehicle customer point to another position, which can be within the route or between routes.

[0164] In another embodiment of the present invention, the random optimization of the initial vehicle route according to the four probability-based exchange operators to obtain multiple optimized vehicle routes includes:

[0165] Identify the route type of the initial vehicle route;

[0166] When the route type of the initial vehicle route is the in-vehicle route type, disconnect two non-adjacent edges in the initial vehicle route;

[0167] Form a new first edge from the first vehicle customer points of the first and second routes, form a new second edge from the second vehicle customer points of the first and second routes, and then reverse the route in the middle of the two edges to obtain multiple optimized vehicle routes;

[0168] When the route type of the vehicle's initial route is the type between vehicle routes, arbitrarily select two routes in the vehicle's initial route and separately disconnect one edge of each;

[0169] Connect the first part of the disconnected first route to the reverse route of the first part of the disconnected second route to generate a new route as the optimized vehicle route; or

[0170] Reverse the second part of the disconnected second route and connect it to the reverse route of the second part of the disconnected first route to generate a new route as the optimized vehicle route.

[0171] Specifically, the exchange operator is the 2-Opt operator.

[0172] In another embodiment of the present invention, randomly optimizing the vehicle's initial route according to the four exchange operators based on probability to obtain multiple optimized vehicle routes, including:

[0173] Respectively select two vehicle customer points in the vehicle's initial route and exchange the routes of the two vehicle customer points to obtain multiple optimized vehicle routes.

[0174] Specifically, the exchange operator is 2-Opt * operator.

[0175] Step 3: Calculate, using the relevant transportation information of the transport vehicle, a set of transportation constraint factors obtained by summarizing the transportation path smoothness factor, transportation cost factor, transportation time factor, transportation fuel consumption factor, and load constraint factor.

[0176] In the embodiment of the present invention, the relevant transportation information of the transport vehicle includes relevant reference data such as the road smoothness of the transport vehicle, the actual cost of the transport vehicle, the time consumed by the transport vehicle, and the distance fuel consumption of the transport vehicle.

[0177] Specifically, calculating the transportation path smoothness factor using the relevant transportation information of the transport vehicle includes:

[0178]

[0179] Wherein, X 1 (j) is the transportation path smoothness factor, Dk j is the smoothness of the route passed by the transport vehicle when driving to node j, and Dk jmax is the tolerance limit value of the road passed by the transport vehicle for smoothness.

[0180] Furthermore, calculating the transportation cost factor using the relevant transportation information of the transport vehicle includes:

[0181]

[0182] Among them, X 2 (j) is the transportation cost factor, e j is the maximum actual transportation cost, e jmax is the estimated maximum transportation cost.

[0183] Specifically, calculating the transportation time factor using the relevant transportation information of the transportation vehicle includes:

[0184]

[0185] Among them, X 3 (j) is the transportation time factor, T j is the time used by the actual logistics transportation vehicle, T jmax is the estimated time used by the logistics vehicle.

[0186] Further, calculating the transportation fuel consumption factor using the relevant transportation information of the transportation vehicle includes:

[0187]

[0188] Among them, a represents the transportation vehicle, FC a represents the fuel consumption per unit distance of vehicle a.

[0189] Specifically, calculating the load constraint factor using the relevant transportation information of the transportation vehicle includes:

[0190]

[0191] Among them, VL a represents the load capacity of vehicle a, MD a represents the maximum delivery distance of vehicle a, d j represents the cargo demand at the target delivery point j.

[0192] Step Four: Construct a transportation scheduling model based on the set of transportation constraint factors, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm.

[0193] In the embodiment of the present invention, constructing the transportation scheduling model based on the set of transportation constraint factors includes:

[0194] Obtain a preset plurality of reference weights, and allocate the plurality of reference weights to the transportation path smoothness factor, the transportation cost factor, the transportation time factor, the transportation fuel consumption factor, and the load constraint factor in the set of transportation constraint factors;

[0195] Construct a transportation scheduling formula based on the multiple reference weights and the set of transportation constraint factors, and construct a corresponding transportation scheduling model according to the transportation scheduling formula.

[0196] Specifically, the multiple reference weights refer to the weight factors that are preset and available for allocation. Allocating the multiple reference weights to the transportation path smoothness factor, the transportation cost factor, the transportation time factor, the transportation fuel consumption factor, and the load constraint factor in the set of transportation constraint factors may mean allocating the reference weight w1 to the transportation path smoothness factor X 1 (j), allocating the reference weight w2 to the transportation cost factor X 2 (j), allocating the reference weight w3 to the transportation time factor X 3 (j), allocating the reference weight w4 to the transportation fuel consumption factor FC a , and allocating the reference weight w5 and the reference weight w6 to the vehicle load capacity VL and the vehicle delivery distance MD in the load constraint factor respectively a and a .

[0197] Specifically, the construction of the transportation scheduling formula based on the multiple reference weights and the set of transportation constraint factors includes:

[0198] X(j) = w1X 1 (j) + w2X 2 (j) + w3X 3 (j) + w4FC a + w5VL a + w6MD a

[0199] where X(j) is the transportation scheduling value, X 1 (j) is the transportation path smoothness factor, X 2 (j) is the transportation cost factor, X 3 (j) is the transportation time factor, VL a represents the load capacity of vehicle a, MD a represents the maximum delivery distance of vehicle a, and w1, w2, w3, w4, w5, and w6 are reference weights.

[0200] Furthermore, the algorithm optimization of the pheromone update method in the basic ant colony algorithm by using the transportation scheduling model as a constraint function to obtain an improved ant colony algorithm includes:

[0201] Using the basic ant colony algorithm to perform path search on multiple optimized vehicle routes to obtain the path with the worst quality and the path with the best quality;

[0202] Statistically analyze the worst path length corresponding to the path with the worst quality and the best path length corresponding to the path with the best quality;

[0203] Based on the worst path length, the best path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm, construct a pheromone update operation formula;

[0204] Use the pheromone update operation formula to update the basic ant colony algorithm to obtain an improved ant colony algorithm.

[0205] Specifically, the basic ant colony algorithm refers to a bionic algorithm in which ants search for food by exploring routes. It belongs to an artificial intelligence optimization algorithm. Based on rules such as pheromone concentration, it determines the forward route of the ant colony searching for food. Therefore, using the basic ant colony algorithm to search for paths for multiple optimized vehicle routes can find the path with the worst quality and the path with the best quality during the path search process.

[0206] Specifically, constructing the pheromone update operation formula based on the worst path length, the best path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm includes:

[0207]

[0208] Among them, τ ij (t + 1) is the pheromone update operation value, B worst represents the worst path, |B worst | represents the worst path length, B best represents the best path, |B best | represents the best path length, η represents the pheromone enhancement factor, X(j) is the transportation scheduling model, and δ represents the preset pheromone parameter.

[0209] Specifically, the pheromone concentration in the improved ant colony algorithm has been increased, so it can accurately and efficiently search for higher-quality optimized vehicle routes.

[0210] Step Five: Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0211] In the embodiment of the present invention, the improved ant colony algorithm can avoid the defects of the basic ant colony algorithm and at the same time search for the optimal path, and it is easy to have a local optimal solution. Therefore, using the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes can obtain a route quality evaluation result.

[0212] Specifically, scheduling multiple optimized vehicle routes according to the route quality evaluation results includes:

[0213] When a vehicle scheduling request is received, screen out the optimized vehicle routes whose route quality evaluation results meet the preset criteria from multiple optimized vehicle routes as the vehicle routes to be scheduled;

[0214] Parse the vehicle scheduling request to obtain a scheduling analysis text, and perform a start operation on the vehicle routes to be scheduled according to the scheduling analysis text.

[0215] Specifically, the route quality evaluation results are used as a criterion for screening optimized vehicle routes, and the optimized vehicle routes whose route quality evaluation results meet the preset criteria are screened out from multiple optimized vehicle routes. Among them, the preset criteria are that the route quality is medium or above medium.

[0216] In the embodiment of the present invention, an initial vehicle route is constructed through the number of vehicles and demand points. The initial vehicle route reflects the route relationship between transport vehicles and demand points. The initial vehicle route is randomly optimized according to four exchange operators based on probability to obtain multiple optimized vehicle routes. The four exchange operators based on probability can improve the richness of random optimization, so that multiple optimized vehicle routes can include various route combination situations to the greatest extent. The relevant transportation information of the transport vehicle is used to calculate a set of transport constraint factors obtained by summarizing the transport path smoothness factor, transport cost factor, transport time factor, transport fuel consumption factor, and load constraint factor. A transport scheduling model is constructed based on the set of transport constraint factors, and the transport scheduling model is used as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm. The improved ant colony algorithm can accurately evaluate the optimized vehicle routes and schedule multiple optimized vehicle routes according to the route quality evaluation results. The overall transport efficiency is improved and the transport cost is reduced. Therefore, the multi-vehicle type supply chain scheduling device based on the improved ant colony algorithm proposed by the present invention can solve the problem of low accuracy in multi-vehicle type supply chain scheduling based on the improved ant colony algorithm.

[0217] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing a multi-vehicle type supply chain scheduling method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0218] The electronic device may include a processor 10, a memory 11, a communication interface 12, and a bus 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a multi-vehicle type supply chain scheduling program based on an improved ant colony algorithm.

[0219] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. The memory 11 can be an internal storage unit of the electronic device in some embodiments, such as the mobile hard disk of the electronic device. The memory 11 can also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. equipped on the electronic device. Further, the memory 11 can also include both the internal storage unit and the external storage device of the electronic device. The memory 11 can be used not only to store the application software installed on the electronic device and various types of data, such as the code of the multi-vehicle type supply chain scheduling program based on the improved ant colony algorithm, etc., but also to temporarily store the data that has been output or will be output.

[0220] The processor 10 can be composed of integrated circuits in some embodiments. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions packaged, including the combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, connecting all components of the entire electronic device through various interfaces and circuits, and by running or executing the programs or modules stored in the memory 11 (such as the multi-vehicle type supply chain scheduling program based on the improved ant colony algorithm, etc.), and calling the data stored in the memory 11, to execute various functions of the electronic device and process data.

[0221] The communication interface 12 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is generally used to establish a communication connection between this electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, and is used to display the information processed in the electronic device and to display a visual user interface.

[0222] The bus 13 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 13 may be divided into an address bus, a data bus, a control bus, etc. The bus 13 is configured to implement the connection and communication between the memory 11 and at least one processor 10, etc.

[0223] Figure 3 Only the electronic device with components is shown, and those skilled in the art can understand that Figure 3 the shown structure does not constitute a limitation on the electronic device, and it may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0224] For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or an inverter, and a power status indicator. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0225] Further, the electronic device may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.

[0226] Optionally, the electronic device may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device and to display a visual user interface.

[0227] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0228] The multi-vehicle supply chain scheduling program based on the improved ant colony algorithm stored in the memory 11 of the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0229] Obtain the number of vehicles of the transport vehicles in the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points;

[0230] Randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes;

[0231] Calculate a set of transport constraint factors obtained by summarizing the transport path smoothness factor, the transport cost factor, the transport time factor, the transport fuel consumption factor, and the load constraint factor by using the relevant transport information of the transport vehicles;

[0232] Construct a transport scheduling model based on the set of transport constraint factors, and use the transport scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm;

[0233] Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0234] Specifically, the specific implementation method of the above instructions by the processor 10 can refer to Figure 1Descriptions of relevant steps in corresponding embodiments are not elaborated herein.

[0235] Furthermore, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0236] The present invention also provides a computer-readable storage medium. The readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, it can implement:

[0237] Obtain the number of vehicles of the transport vehicles in the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points;

[0238] Randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes;

[0239] Calculate a set of transport constraint factors obtained by summarizing a transport path smoothness factor, a transport cost factor, a transport time factor, a transport fuel consumption factor, and a load constraint factor by using relevant transport information of the transport vehicles;

[0240] Construct a transport scheduling model based on the set of transport constraint factors, and use the transport scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm;

[0241] Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

[0242] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation.

[0243] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] In addition, in each embodiment of the present invention, each functional module can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0245] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0246] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced by the present invention. Any reference signs in the claims should not be construed as limiting the claimed invention.

[0247] The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms. Blockchain, essentially a decentralized database, is a string of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity (anti-counterfeiting) of the information and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, an application service layer, etc.

[0248] In addition, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. Words such as "second" are used to denote names and do not denote any particular order.

[0249] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-vehicle supply chain scheduling method based on an improved ant colony algorithm, characterized in that, the method includes: Obtain the number of vehicles in the transportation vehicles of the vehicle distribution center and the demand points corresponding to the vehicle distribution center, and construct an initial vehicle route based on the number of vehicles and the demand points; Randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes; Use the relevant transportation information of the transportation vehicles to calculate a set of transportation constraint factors obtained by summarizing the transportation path smoothness factor, transportation cost factor, transportation time factor, transportation fuel consumption factor, and load constraint factor; Construct a transportation scheduling model based on the set of transportation constraint factors, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm; Use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result; Among them, the step of randomly optimizing the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes includes: identifying the route type of the initial vehicle route; when the route type of the initial vehicle route is the in-vehicle route type, disconnect two non-adjacent edges in the initial vehicle route; form a new first edge by the first vehicle customer points of the first and second routes, form a new second edge by the second vehicle customer points of the first and second routes, and then reverse the route in the middle of the two edges to obtain multiple optimized vehicle routes; when the route type of the initial vehicle route is the inter-vehicle route type, randomly select two routes in the initial vehicle route and separately disconnect one edge of each; connect the first part of the first route after disconnection with the reverse route of the first part of the second route after disconnection to generate a new route as the optimized vehicle route; or, connect the reverse of the second part after disconnection of the second route with the reverse route of the second part after disconnection of the first route to generate a new route as the optimized vehicle route; The step of using the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm includes: using the basic ant colony algorithm to search for paths for multiple optimized vehicle routes to obtain the worst-quality path and the highest-quality path; count the length of the worst-quality path corresponding to the worst-quality path and the length of the highest-quality path corresponding to the highest-quality path; construct a pheromone update operation formula based on the worst-path length, the highest-path length, the transportation scheduling model, and the pheromone constraint formula in the basic ant colony algorithm; use the pheromone update operation formula to update the basic ant colony algorithm to obtain an improved ant colony algorithm.

2. The multi-vehicle supply chain scheduling method based on an improved ant colony algorithm according to claim 1, characterized in that, the step of constructing an initial vehicle route based on the number of vehicles and the demand points includes: Start the transport vehicles in the vehicle distribution center with the number of vehicles as the starting number, and calculate the distance values between the transport vehicles and the demand points respectively; Select the demand points with distance values less than or equal to the preset reference threshold among multiple demand points as target demand points; Add the target demand points to the pre-constructed route template to obtain a reference transport route, and calculate the remaining space of the vehicles and the service required time in the reference transport route respectively; Determine the set of routes to be added according to the remaining space of the vehicles and the service required time; Calculate the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula; Insert the target demand point into the minimum cost insertion point of the route with the minimum insertion cost. When all the target demand points are added, output the initial vehicle route.

3. The multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm as described in claim 2, characterized in that, the calculating the insertion cost of the target demand point in each route to be added in the set of routes to be added according to the preset insertion cost formula includes: the preset insertion cost formula is: Among them, represents the insertion cost, i 1 represents the number of the previous demand point at the insertion position of customer point i, i 2 represents the number of the next demand point at the insertion position of customer point i, w 1 and w 2 respectively represent the penalty rates for the service time of the inserted customer point being earlier or later than the time window. TE and TL respectively represent the early arrival and late arrival times, represents the i 1 th route to be added, v k represents the preset insertion frequency parameter.

4. The multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm as described in claim 1, characterized in that, the constructing the transport scheduling model based on the transport constraint factor set includes: Obtain a preset plurality of reference weights, and assign the plurality of reference weights to the transport path smoothness factor, the transport cost factor, the transport time factor, the transport fuel consumption factor and the load constraint factor in the transport constraint factor set; Construct a transport scheduling formula based on the plurality of reference weights and the transport constraint factor set, and construct a corresponding transport scheduling model according to the transport scheduling formula.

5. The multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm as described in claim 1, characterized in that, the constructing the pheromone update operation formula based on the worst path length, the highest path length, the transport scheduling model and the pheromone constraint formula in the basic ant colony algorithm includes: Among them, τ ij (t + 1) is the pheromone update operation value, B worst represents the worst path, |B worst | represents the length of the worst path, B best represents the best path, |B best | represents the length of the best path, η represents the pheromone enhancement factor, X(j) is the transportation scheduling model, and δ represents the preset pheromone parameter.

6. A multi-vehicle type supply chain scheduling device based on the improved ant colony algorithm, used to implement the multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm as described in any one of claims 1 to 5, characterized in that, the device includes: A vehicle route optimization module, configured to obtain the number of transport vehicles in the vehicle distribution center and the demand points corresponding to the vehicle distribution center, construct an initial vehicle route based on the number of vehicles and the demand points, and randomly optimize the initial vehicle route according to four exchange operators based on probability to obtain multiple optimized vehicle routes; A constraint factor summary module, configured to calculate a transport constraint factor set obtained by summarizing a transport path smoothness factor, a transport cost factor, a transport time factor, a transport fuel consumption factor and a load constraint factor by using relevant transport information of the transport vehicle; An ant colony algorithm optimization module, which is used to construct a transportation scheduling model based on the set of transportation constraint factors, and use the transportation scheduling model as a constraint function to optimize the pheromone update method in the basic ant colony algorithm to obtain an improved ant colony algorithm; A vehicle route scheduling module, which is used to use the improved ant colony algorithm to evaluate the route quality of multiple optimized vehicle routes to obtain a route quality evaluation result, and schedule multiple optimized vehicle routes according to the route quality evaluation result.

7. An electronic device, characterized in that, the electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the multi-vehicle type supply chain scheduling method based on the improved ant colony algorithm according to any one of claims 1 to 5.

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