Route planning method based on staged and combined tabu algorithms and related equipment

Abstract

The invention relates to the technical field of logistics optimization, in particular to a route planning method and related equipment based on a staged and combined taboo algorithm, and the method comprises the steps: obtaining the order information of an order and the information of a warehouse capable of receiving the order; based on the order information and the warehouse information, calculating a distance between an order place and each warehouse place; according to the distance and a preset order allocation rule, allocating the order to a corresponding warehouse for integration; and carrying out route planning operation on the orders integrated in the warehouse, wherein the route planning operation comprises at least one of optimization of a single route, partial order exchange of two routes and partial order separation and insertion operation of the two routes. The method has the effect of improving the route planning efficiency and accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of logistics optimization, and in particular to a route planning method and related equipment based on a phased and combined taboo algorithm. Background Art

[0002] With the rapid development of e-commerce and logistics industries, logistics distribution efficiency has become one of the key factors in industry competition. In terms of order delivery route planning in the logistics field, existing technologies usually use heuristic algorithms or precise algorithms to plan routes based on the distribution of order locations, with the aim of minimizing overall transportation costs and making delivery time more reasonable. However, this approach has some drawbacks. When the number of orders is large, the calculation process takes a long time, and the final result is often not ideal. There are some routes that are intertwined and violate common sense, and the efficiency and accuracy of route planning are low.

[0003] Therefore, based on the above problems, the prior art still needs to be improved. Summary of the invention

[0004] The purpose of this application is to provide a route planning method and related equipment based on a phased and combined taboo algorithm, aiming to solve the problem of low efficiency and accuracy of route planning.

[0005] The first purpose of this application is to provide a route planning method based on a phased and combined taboo algorithm, including: Obtaining order information of an order and warehouse information that can accept the order; Based on the order information and the warehouse information, calculating the distance between the order location and each warehouse location; According to the distance and the preset order allocation rules, the order is allocated to the corresponding warehouse for integration; A route planning operation is performed on the orders integrated in the warehouse, wherein the route planning operation includes at least one of single route optimization, partial order interchange between two routes, and partial order separation and insertion between two routes.

[0006] By adopting the above technical solution, order and warehouse information is obtained and the distance is calculated, and then the order is allocated to the warehouse for integration according to the rules, so that the order allocation is more reasonable and efficient, and the warehouse's acceptance capacity and order distribution can be fully considered, reducing unnecessary transportation costs and time waste. The route planning of the integrated orders in the warehouse, including single route optimization, partial order exchange and separation and insertion of two routes, can further optimize the delivery path, improve delivery efficiency, reduce delivery time, and improve the adaptability to complex order situations. It effectively solves problems such as unreasonable route planning, long calculation time, and unsatisfactory results, and improves the efficiency and accuracy of route planning.

[0007] In a possible implementation of the present application, the step of calculating the distance between the order location and each warehouse location includes: Obtain traffic information and road type information between the order location and the warehouse location, as well as the order delivery time requirements and the current time period; Analyze the delivery time requirement of the order and the time period information of the current time; Determine whether the delivery time requirement of the order is during a peak period; If the delivery time requirement of the order is to be delivered during peak hours, or the current time is during peak hours and the order has no special time requirement, then the road weight for marking the delivery of the order needs to be adjusted; Based on the traffic condition information and road type information, a dynamic weight is set for the road where the order is to be delivered; The weighted road weights are used in combination with the actual geographical distance between the order location and the warehouse to calculate the dynamic distance between the order location and each warehouse.

[0008] By adopting the above technical solution, the dynamic distance between the order location and the warehouse location is calculated by considering traffic conditions, road types, delivery time requirements and time period information, which can more accurately reflect the difficulty of actual delivery. Adjusting the road weight during peak hours or when the order is delivered during peak hours and there are no special requirements avoids delivery delays caused by factors such as traffic congestion, making the distance calculation more in line with the actual delivery scenario. The dynamic distance calculated based on the dynamic weight helps to more reasonably allocate orders to appropriate warehouses, improves the accuracy and scientificity of order allocation, further optimizes the entire logistics distribution process, and improves delivery efficiency and customer satisfaction.

[0009] In a possible implementation manner of the present application, the step of allocating the order to a corresponding warehouse for integration according to the distance and a preset order allocation rule includes: Setting a first preset threshold and a second preset threshold; If the distance between the order location and a certain warehouse location is less than the first preset threshold, the order is preferentially allocated to the warehouse; If the distance between the order location and a certain warehouse location is greater than the first preset threshold, determining whether there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold; if there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold, comprehensively analyzing the load conditions of multiple warehouses, order priorities, and warehouse delivery capabilities to determine the warehouse to which the order is assigned; If there is only one warehouse location whose distance to the order location is within the second preset threshold range, the order is allocated to the warehouse; During the order allocation process, the warehouse load information and order allocation status are updated in real time.

[0010] By adopting the above technical solution and setting the first preset threshold and the second preset threshold to allocate orders, it is possible to make decisions more flexibly based on the distance between the order location and the warehouse location. Orders with a distance less than the first preset threshold are preferentially allocated to the corresponding warehouse, which can reduce transportation costs and time. When the distance is greater than the first preset threshold, the warehouse situation with a dynamic distance within the second preset threshold is further judged, and the order allocation warehouse is determined by comprehensively considering the warehouse load, order priority and distribution capacity, so as to respond to complex order allocation scenarios in a timely manner, improve the utilization efficiency of warehouse resources, and avoid situations where some warehouses are overloaded while other warehouses are idle. At the same time, real-time updates of warehouse load information and order allocation status ensure the accuracy and timeliness of information, which helps to continuously optimize order allocation strategies and improve the operating efficiency and stability of the entire logistics distribution system.

[0011] In a possible implementation of the present application, a route planning operation is performed on the orders integrated in the warehouse, and the route planning operation includes at least one of the steps of optimizing a single route, exchanging parts of orders of two routes, and separating and inserting parts of orders of two routes, including: For the orders consolidated in the warehouse, determine and obtain initial conditions for performing route planning operations, the initial conditions including order quantity, order delivery time requirements, distance distribution between the warehouse and the delivery destination, and other constraints; If you select single line optimization operation: Determine the initial path for each route based on the order in which the orders are generated and the distribution of delivery locations; Calculate the sum of distances and estimated delivery time between orders on each route; optimize the route by adjusting the positions of orders on a single route so as to minimize the sum of distances and optimize the estimated delivery time, while satisfying the delivery time requirements of the orders and the other constraints; If you choose to swap some orders of two lines: Select two lines from the integrated order as operation objects; Determine the range of orders that can be interchanged, taking into account the delivery time requirements of the orders, the locations of the orders on their respective routes, and the impact of the interchange on the overall delivery time and distance of the two routes; Perform order swap operations, recalculate the delivery time and distance of the two routes, compare the results before and after the swap, and retain the route configuration after the swap if it is optimized; If two lines of partial order separation and insertion operations are selected: For the two selected lines, determine the order nodes where separation and insertion operations can be performed; Separate some orders from one line and try to insert them into the appropriate position of another line; Recalculate the delivery time and distance of the two routes, evaluate the effect of the operation, and update the route configuration if the optimization conditions are met.

[0012] By adopting the above technical solutions, route planning operations are performed for the orders integrated in the warehouse, taking into full consideration the order quantity, delivery time requirements, distance distribution and other constraints. The single route optimization operation determines the initial path based on the order sequence and delivery location, and minimizes the total distance and optimizes the expected delivery time by adjusting the order location, meeting the delivery requirements and constraints, and improving the delivery efficiency of a single route. The partial order exchange operation and separate insertion operation of the two routes optimize the two routes by reasonably selecting the operation object, determining the operation scope and evaluating the operation effect, effectively reducing the overall delivery time and distance of the two routes, enhancing the adaptability and processing capabilities for complex order situations, thereby comprehensively improving the efficiency and accuracy of order delivery in the warehouse and optimizing the route planning of the entire logistics distribution.

[0013] In a possible implementation of the present application, the steps of performing route planning operations on the orders integrated in the warehouse include: Set the operation frequency parameters, where the single line optimization operation frequency is once every N cycles, the two lines partial order swap operation frequency and the two lines partial order separation and insertion operation frequency are both M times every N cycles; For orders consolidated within the warehouse, follow these steps: Carry out route planning based on the optimization operation of a single route, calculate in detail and accurately record the relevant data on the delivery time and total distance involved after this operation; Route planning is carried out based on the exchange of some orders on the two routes, and the corresponding delivery time and total distance data are also calculated and accurately recorded in detail; Plan routes based on the separation and insertion of partial orders on two routes, carefully calculate and accurately record relevant delivery time and total distance data; Compare and analyze the data obtained from the above three operations to determine the best solution; If the operation corresponding to the optimal solution is not the last operation in the currently ongoing cycle, all orders will be re-routed using the operation method corresponding to the optimal solution, and then subsequent cycles will continue according to the above-set operation frequency until the operation time exceeds the set duration.

[0014] By adopting the above technical solution and setting different operation frequency parameters to perform route planning operations for integrated orders in the warehouse, different operations can be arranged reasonably in a targeted manner. The total delivery time and distance data after each operation are calculated and recorded in detail, and the best solution is determined through comparative analysis to ensure the scientificity and accuracy of route planning. If the best solution is not the last operation in the current cycle, all orders are replanned to ensure that the route planning is performed in the best way for each cycle. Continue to cycle at the set frequency until the operation time exceeds the time limit, so that the entire route planning process is continuously optimized, effectively improving the distribution efficiency and reducing the distribution cost. At the same time, it improves the adaptability and processing capabilities for different order situations and optimizes the overall effect of logistics distribution.

[0015] In a possible implementation manner of the present application, the method further includes: In a cycle where the single route optimization operation frequency is once per N cycles, the two routes partial order swap operation frequency and the two routes partial order separation and insertion operation frequency are both M times per N cycles, determine whether the route planning effect is improved; If the route planning effect is not improved, that is, the sum of the delivery time and distance has no optimization trend, a random route planning operation is performed.

[0016] By adopting the above technical solution, the route planning effect is judged in the cycle process and random route planning operations are performed when there is no improvement, which can timely break the local optimal dilemma that may occur. Avoid being trapped in a fixed optimization mode and being unable to further improve distribution efficiency and reduce costs. This dynamic adjustment mechanism increases the flexibility and adaptability of route planning, allowing the entire logistics distribution system to better cope with complex and changing order situations and actual distribution environments, continuously optimize distribution routes, and improve the overall performance of logistics distribution.

[0017] In a possible implementation manner of the present application, the method further includes: Obtain historical order data, traffic data, and warehouse operation data; Analyze the order time distribution, order type distribution and order volume change trend in historical order data; Analyze traffic data to determine road congestion at different times, road construction information, and the impact of special events on traffic; Analyze warehouse operation data including warehouse cargo in and out volume, warehouse inventory level, and warehouse equipment operating status; Use machine learning algorithms to train the historical order data, traffic data, and warehouse operation data analyzed above; Predict future order volumes, traffic congestion, and warehouse load trends; Based on the forecast results, determine whether the order allocation strategy and route planning need to be adjusted; If it is predicted that the future order volume will increase and traffic congestion will increase, or the warehouse load is close to saturation, the order allocation rules will be re-evaluated; If it is predicted that traffic congestion is serious in a specific area, the orders involving that area will be re-routed to avoid congested sections and re-select the delivery route; During the prediction and adjustment process, the parameters of the prediction model are updated in real time to improve the accuracy and adaptability of the prediction.

[0018] By adopting the above technical solutions, historical order, traffic and warehouse operation data are obtained and analyzed, and machine learning algorithms are used to train and predict future trends, which can provide early insights into changes in order volume, traffic congestion and warehouse load. Adjusting order allocation strategies and route planning based on forecast results, such as re-evaluating rules or re-planning order routes in specific areas, can effectively deal with potential problems and avoid delivery delays caused by sudden increases in orders, traffic congestion or warehouse saturation. Real-time updates of forecast model parameters improve forecast accuracy and adaptability, making the logistics distribution system more forward-looking and flexible, optimizing the entire logistics distribution process, and improving delivery efficiency and customer satisfaction.

[0019] The second purpose of this application is to provide a route planning system based on a phased and combined taboo algorithm, the system comprising: Order information and warehouse information acquisition module: used to obtain order information of an order and warehouse information that can accept the order; Distance calculation module: based on the order information and the warehouse information, calculate the distance between the order location and each warehouse location; Order distribution and integration module: according to the distance and the preset order distribution rules, the orders are distributed to the corresponding warehouses for integration; Route planning operation module: performs route planning operations on the orders integrated in the warehouse, and the route planning operations include at least one of single route optimization, partial order exchange between two routes, and partial order separation and insertion between two routes.

[0020] By adopting the above technical solution, order and warehouse information is obtained and the distance is calculated, and then the order is allocated to the warehouse for integration according to the rules, so that the order allocation is more reasonable and efficient, and the warehouse's acceptance capacity and order distribution can be fully considered, reducing unnecessary transportation costs and time waste. The route planning of the integrated orders in the warehouse, including single route optimization, partial order exchange and separation and insertion of two routes, can further optimize the delivery path, improve delivery efficiency, reduce delivery time, and improve the adaptability to complex order situations. It effectively solves problems such as unreasonable route planning, long calculation time, and unsatisfactory results, and improves the efficiency and accuracy of route planning.

[0021] The third object of this application is to provide a route planning device based on a phased and combined taboo algorithm, the device comprising: A memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the route planning method based on the phased and combined taboo algorithms.

[0022] The fourth objective of this application is to provide a storage medium.

[0023] The fourth application objective of this application is achieved through the following technical solutions: A storage medium stores a computer program that can be loaded by a processor and execute the route planning method based on the phased and combined taboo algorithm.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: 1. Order allocation can be carried out by comprehensively considering the dynamic distance between the order location and the warehouse location (combined with factors such as traffic and time period) as well as warehouse load conditions, order priority and delivery capacity. This can make orders more reasonably allocated to various warehouses, avoid the situation where some warehouses are overloaded while other warehouses are idle, and improve the overall utilization efficiency of warehouse resources.

[0025] 2. Multiple route planning operations (single route optimization, partial order exchange and separate insertion of two routes) and reasonably set operation frequency enable route planning to be continuously optimized. By comparing and analyzing the total delivery time and distance data after different operations and selecting the best solution, it can effectively reduce delivery time and distance, improve delivery efficiency, and enhance adaptability to complex order situations.

[0026] 3. Use machine learning algorithms to analyze historical data to predict future order volumes, traffic congestion, and warehouse load trends. Adjust order distribution strategies and route planning in advance based on these prediction results. For example, re-plan routes in advance when serious traffic congestion is predicted. This can better deal with possible problems in the future, reduce delivery delays caused by unforeseen circumstances, and improve the stability and reliability of the logistics distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a route planning method based on a phased and combined taboo algorithm provided in an embodiment of the present application; Figure 2 It is a virtual structural diagram of a route planning system based on a phased and combined taboo algorithm provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0030] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0031] The present application embodiment provides a route planning method based on a phased and combined taboo algorithm, referring to Figure 1 , the main process of the method is described as follows: S1: Obtain order information of an order and warehouse information that can accept the order; Among them, detailed information of the order is obtained from the order management system, including the order's place of shipment, place of receipt, cargo weight, volume, delivery time requirements, etc. At the same time, information of each warehouse is obtained, such as warehouse location, warehouse capacity, current inventory level, number of warehouse staff, number and type of delivery vehicles, etc.

[0032] S2: Calculate the distance between the order location and each warehouse location based on the order information and the warehouse information; Among them, traffic information between the order location and the warehouse location is obtained from the database of the traffic management department or a third-party traffic data provider, such as the average speed of different sections, congestion index, etc. Obtain road type information to distinguish different types of roads such as highways, urban main roads, and branch roads. Parse the delivery time requirements of the order, determine the time period of the current time, and judge whether it is a peak period (such as morning and evening peaks on weekdays). If the delivery time requirement of the order is to be delivered during the peak period, or the current time is during the peak period and the order has no special time requirements, the weight of the road marked for the delivery of the order needs to be adjusted. Based on the traffic information and road type information, set dynamic weights for the roads for order delivery. For example, the weight of congested sections increases, and the weight of highways is relatively small. Using the weighted road weights, combined with the actual geographical distance between the order location and the warehouse, the dynamic distance between the order location and each warehouse is calculated through a suitable algorithm (such as a weighted distance algorithm).

[0033] S3: Allocate the order to a corresponding warehouse for integration according to the distance and a preset order allocation rule; Among them, a first preset threshold and a second preset threshold are set, and these two thresholds can be set according to the actual logistics distribution range and warehouse distribution. For example, the first preset threshold can be set to 50 kilometers, and the second preset threshold can be set to 20 kilometers. For each order, compare the distance between it and each warehouse location. If the distance between the order location and a warehouse location is less than the first preset threshold, the order is preferentially assigned to the warehouse. If the distance between the order location and a warehouse location is greater than the first preset threshold, it is determined whether there are multiple warehouse locations with dynamic distances to the order location within the second preset threshold range. If there are multiple qualified warehouses, the load conditions of multiple warehouses (currently allocated order quantity, remaining inventory space, etc.), order priorities (such as expedited orders are preferentially assigned to warehouses with lighter loads and stronger distribution capabilities) and warehouse distribution capabilities (number of vehicles, number of distribution personnel, etc.) are comprehensively analyzed to determine the warehouse to which the order is assigned. If only one warehouse location is within the second preset threshold range from the order location, the order is assigned to the warehouse. In the process of allocating orders, the warehouse load information and order allocation are updated in real time to ensure the accuracy and timeliness of the data.

[0034] S4: performing route planning operations on the orders integrated in the warehouse, wherein the route planning operations include at least one of single route optimization, partial order exchange between two routes, and partial order separation and insertion between two routes.

[0035] Among them, for the orders integrated in the warehouse, the initial conditions for route planning operations are determined and obtained, including the order quantity, order delivery time requirements, the distance distribution between the warehouse and the delivery destination, and other constraints (such as vehicle load restrictions, delivery personnel working time restrictions, etc.).

[0036] Single route optimization operation: Determine the initial path of each route based on the order of order generation and the distribution of delivery locations. Calculate the sum of distances between orders on each route and the estimated delivery time. Optimize the route by adjusting the position of orders on a single route, such as using a local search algorithm, to minimize the sum of distances and optimize the estimated delivery time, while meeting the delivery time requirements of the orders and other constraints.

[0037] Partial order swap operation for two routes: Select two routes from the integrated orders as the operation objects. Determine the range of orders that can be swapped, considering the delivery time requirements of the orders, the location of the orders on their respective routes, and the impact of the swap on the overall delivery time and distance of the two routes. Swap the orders and recalculate the delivery time and distance of the two routes. Compare the results before and after the swap. If optimized, keep the route configuration after the swap.

[0038] Separation and insertion of some orders on two routes: For the two selected routes, determine the order nodes that can be separated and inserted. Separate some orders from one route and try to insert them into the appropriate position of another route. Recalculate the delivery time and distance of the two routes, evaluate the effect after the operation, and update the route configuration if the optimization conditions are met.

[0039] Set the operation frequency parameters, where the single route optimization operation frequency is once every N cycles, the two routes partial order exchange operation frequency and the two routes partial order separation and insertion operation frequency are M times every N cycles. For the orders integrated in the warehouse, route planning is carried out according to the above different operation steps, and the delivery time and distance total related data involved after each operation are calculated and accurately recorded in detail.

[0040] The data obtained from the above three operations are compared and analyzed to determine the best solution. If the operation corresponding to the best solution is not the last operation in the current cycle, all orders are re-routed in the operation mode corresponding to the best solution, and then the subsequent cycles are continued according to the above set operation frequency until the operation time exceeds the set duration.

[0041] In the process where the frequency of single route optimization operation is once every N cycles, the frequency of partial order swapping between two routes and the frequency of partial order separation and insertion between two routes are all M times every N cycles, it is determined whether the route planning effect is improved. If the route planning effect is not improved, that is, there is no optimization trend in the sum of delivery time and distance, a random route planning operation is performed.

[0042] Specifically, in some possible embodiments, the step of calculating the distance between the order location and each warehouse location includes: Obtain traffic information and road type information between the order location and the warehouse location, as well as the order delivery time requirements and the current time period; Analyze the delivery time requirement of the order and the time period information of the current time; Determine whether the delivery time requirement of the order is during a peak period; If the delivery time requirement of the order is to be delivered during peak hours, or the current time is during peak hours and the order has no special time requirement, then the road weight for marking the delivery of the order needs to be adjusted; Based on the traffic condition information and road type information, a dynamic weight is set for the road where the order is to be delivered; The weighted road weights are used in combination with the actual geographical distance between the order location and the warehouse to calculate the dynamic distance between the order location and each warehouse.

[0043] Among them, traffic information between the order location and the warehouse location is obtained from traffic data providers (such as the traffic information interface of map navigation companies), including real-time vehicle speeds, congestion levels (expressed as congestion indexes) and historical traffic data statistics (such as average vehicle speeds and congestion frequencies during specific time periods) of different road sections. Road type information is obtained from the geographic information system (GIS) database to clearly distinguish between different types of roads such as expressways, national roads, provincial roads, urban trunk roads, secondary trunk roads, and branch roads. The delivery time requirements of the order (such as requiring delivery before a specific time) and the time period of the current time are obtained from the order management system (accurate to the hour, to determine whether it is in a specific time period such as the morning and evening rush hours on weekdays).

[0044] Analyze the delivery time requirements of the order in detail to determine whether it is during peak hours. For example, if the order is required to be delivered between 8:00 and 9:00 a.m. on a weekday, this is considered peak-hour delivery. At the same time, analyze the time period of the current time. If the current time is 7:30 a.m. on a weekday and the order has no special time requirements (such as flexible delivery time), it is determined that the delivery of the order may be affected by peak-hour traffic, and the road weight for the delivery of the order needs to be adjusted.

[0045] Dynamic weights are set based on the traffic information and road type information obtained. For example, for road sections with high congestion (congestion index exceeds a certain set value, such as 0.8), their weights are set to a higher value (such as 3); for smooth highways, the weights are set to a lower value (such as 1). For urban roads of different levels, the corresponding weights are set according to their usual traffic flow and speed. The weight of the main road may be 2, the weight of the secondary road is 2.5, and the weight of the branch road is 3 (the weight values ​​are for reference only and can be adjusted according to actual conditions).

[0046] Use the weighted road weights and the actual geographical distance between the order location and the warehouse for calculation. Assume that there are multiple paths between the order location and the warehouse, and each path consists of multiple road segments. For each path, calculate its weighted distance. For example, path A consists of road segment a1 (length 5 kilometers, weight 2), road segment a2 (length 3 kilometers, weight 3) and road segment a3 (length 2 kilometers, weight 1), then the weighted distance of path A is (5×2+3×3+2×1)=21 kilometers. Calculate the weighted distance of all possible paths between the order location and each warehouse, and select the minimum value as the dynamic distance between the order location and the warehouse.

[0047] Specifically, in some possible embodiments, according to the distance and the preset order allocation rule, the step of allocating the order to the corresponding warehouse for integration includes: Setting a first preset threshold and a second preset threshold; If the distance between the order location and a certain warehouse location is less than the first preset threshold, the order is preferentially allocated to the warehouse; If the distance between the order location and a certain warehouse location is greater than the first preset threshold, determining whether there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold; if there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold, comprehensively analyzing the load conditions of multiple warehouses, order priorities, and warehouse delivery capabilities to determine the warehouse to which the order is assigned; If there is only one warehouse location whose distance to the order location is within the second preset threshold range, the order is allocated to the warehouse; During the order allocation process, the warehouse load information and order allocation status are updated in real time.

[0048] The first preset threshold and the second preset threshold are reasonably set according to the geographical scope of the logistics distribution area, the density of warehouse distribution and the past order distribution experience. For example, in the distribution scenario of a city and its surrounding areas, the first preset threshold (dynamic distance) can be set to 50 kilometers, and the second preset threshold (dynamic distance) can be set to 20 kilometers.

[0049] For each order, the distance between it and each warehouse location is calculated (such as the result obtained by the above dynamic distance calculation method). If the distance between the order location and a warehouse location is less than the first preset threshold (such as the distance between the order location and warehouse A is 30 kilometers, which is less than the first preset threshold of 50 kilometers), the order is preferentially assigned to the warehouse. If the distance between the order location and a warehouse location is greater than the first preset threshold, it is determined whether there are multiple warehouse locations with dynamic distances to the order location within the second preset threshold range.

[0050] When there are multiple qualified warehouses, for example, the distance between the order location and warehouse B is 60 kilometers (greater than the first preset threshold of 50 kilometers), but the dynamic distances to warehouse B, warehouse C, and warehouse D are 18 kilometers, 15 kilometers, and 12 kilometers respectively (all within the second preset threshold of 20 kilometers). At this time, the load of multiple warehouses is comprehensively analyzed to check the number of orders currently allocated to warehouses B, C, and D and the remaining inventory space. At the same time, the priority of the order is considered. If the order is an expedited order, warehouses with strong distribution capabilities and relatively light loads are given priority. The distribution capabilities of the warehouse should also be considered, including factors such as the number of vehicles in the warehouse, the number of distribution personnel, and the type of vehicles (such as whether there are vehicles suitable for the goods of the order). By comprehensively analyzing these factors, the warehouse to which the order is assigned is determined.

[0051] If only one warehouse location is within the second preset threshold range from the order location, for example, the distance between the order location and warehouse E is 60 kilometers (greater than the first preset threshold), but the dynamic distance to warehouse E is 18 kilometers (within the second preset threshold range), and the dynamic distances of other warehouses are not within this range, the order will be assigned to this warehouse.

[0052] During the order allocation process, the warehouse load information and order allocation are updated in real time. Whenever an order is allocated to a warehouse, the number of allocated orders in the warehouse increases and the remaining inventory space decreases accordingly (if inventory factors are taken into account). At the same time, the order allocation records are also updated in a timely manner to facilitate subsequent query and analysis of the rationality and optimization space of order allocation.

[0053] Specifically, in some possible embodiments, a route planning operation is performed on the orders integrated in the warehouse, and the route planning operation includes at least one of a single route optimization, a partial order exchange of two routes, and a partial order separation and insertion of two routes, including: For the orders consolidated in the warehouse, determine and obtain initial conditions for performing route planning operations, the initial conditions including order quantity, order delivery time requirements, distance distribution between the warehouse and the delivery destination, and other constraints; If you select single line optimization operation: Determine the initial path for each route based on the order in which the orders are generated and the distribution of delivery locations; Calculate the sum of distances and estimated delivery time between orders on each route; optimize the route by adjusting the positions of orders on a single route so as to minimize the sum of distances and optimize the estimated delivery time, while satisfying the delivery time requirements of the orders and the other constraints; If you choose to swap some orders of two lines: Select two lines from the integrated order as operation objects; Determine the range of orders that can be interchanged, taking into account the delivery time requirements of the orders, the locations of the orders on their respective routes, and the impact of the interchange on the overall delivery time and distance of the two routes; Perform order swap operations, recalculate the delivery time and distance of the two routes, compare the results before and after the swap, and retain the route configuration after the swap if it is optimized; If two lines of partial order separation and insertion operations are selected: For the two selected lines, determine the order nodes where separation and insertion operations can be performed; Separate some orders from one line and try to insert them into the appropriate position of another line; Recalculate the delivery time and distance of the two routes, evaluate the effect of the operation, and update the route configuration if the optimization conditions are met.

[0054] Among them, obtain the number of integrated orders in the warehouse from the order management system. Check the delivery time requirements for each order and clarify key information such as the latest delivery time. Analyze the distance distribution between the warehouse and the delivery destination. Geographic Information System (GIS) tools can be used to assist in analysis to understand the distance characteristics of different directions and regions. Determine other constraints, such as vehicle load limits, delivery personnel working hours limits, and transportation conditions for certain special goods.

[0055] Single route optimization operation: Preliminarily determine the direction of each route based on the order in which the orders are generated and the distribution of the delivery locations. For example, if the orders are generated in chronological order and the delivery locations are roughly distributed linearly, the initial path can be connected along the order of these locations. Calculate the sum of the distances between the orders on each route and the estimated delivery time. You can use distance calculation formulas (such as the Euclidean distance formula for two-point distance calculation in plane coordinates, or calculate the actual road distance based on the path algorithm of the actual map) and time estimation models (considering factors such as average vehicle speed and parking time). Optimize the route by adjusting the position of the order on a single route. For example, use a local search algorithm to try to move an order to another location on the route, recalculate the sum of distances and the estimated delivery time, and compare the results at different locations until you find an order arrangement that minimizes the sum of distances and optimizes the estimated delivery time, while meeting the delivery time requirements and other constraints of the order.

[0056] Swapping some orders on two routes: From the consolidated orders, select two routes as the operation objects based on the distribution of delivery locations, the balance of order quantities, and other factors. For example, you can select two routes with similar order quantities and a certain overlap or proximity in the delivery areas. Determine the range of orders that can be swapped. Consider the delivery time requirements of the orders. If an order requires delivery within a specific time and swapping may cause delays, do not include it in the swap range. Also consider the position of the orders on each route and the impact of the swap on the overall delivery time and distance of the two routes. For example, if an order is at the front of a route and swapping will significantly increase the delivery time of subsequent orders on that route, you need to carefully consider whether to swap it. Swap the orders and recalculate the delivery time and distance of the two routes. You can use a similar method to the calculation of a single route, but consider the overall situation of the two routes at the same time. Compare the results before and after the swap. If the delivery time is reduced and the distance is better after the swap, keep the route configuration after the swap.

[0057] Separation and insertion of some orders on two routes: For two selected routes, determine the order nodes that can be separated and inserted based on the delivery time requirements, distance distribution and other factors of the orders. For example, orders with loose delivery time requirements and relatively flexible positions on a certain route can be used as candidate nodes for separation and insertion. Separate some orders from one route and try to insert them into the appropriate position of another route. During the insertion process, consider the delivery time requirements and distance changes of the orders before and after the newly inserted position. Recalculate the delivery time and distance of the two routes and evaluate the effect after the operation. If the optimization conditions are met, such as reduced delivery time, reduced total distance, etc., update the route configuration.

[0058] Specifically, in some possible embodiments, the steps of performing route planning operations on the orders integrated in the warehouse include: Set the operation frequency parameters, where the single line optimization operation frequency is once every N cycles, the two lines partial order swap operation frequency and the two lines partial order separation and insertion operation frequency are both M times every N cycles; For orders consolidated within the warehouse, follow these steps: Carry out route planning based on the optimization operation of a single route, calculate in detail and accurately record the relevant data on the delivery time and total distance involved after this operation; Route planning is carried out based on the exchange of some orders on the two routes, and the corresponding delivery time and total distance data are also calculated and accurately recorded in detail; Plan routes based on the separation and insertion of partial orders on two routes, carefully calculate and accurately record relevant delivery time and total distance data; Compare and analyze the data obtained from the above three operations to determine the best solution; If the operation corresponding to the optimal solution is not the last operation in the currently ongoing cycle, all orders will be re-routed using the operation method corresponding to the optimal solution, and then subsequent cycles will continue according to the above-set operation frequency until the operation time exceeds the set duration.

[0059] Among them, according to the characteristics of logistics distribution business, order quantity scale, computing resources and other factors, the operation frequency parameters N and M are reasonably set. For example, in the case of a large number of orders and complex distribution areas, N=21 and M=10 can be set, that is, the single line optimization operation is executed once every 21 cycles, and the partial order exchange operation of two lines and the partial order separation and insertion operation of two lines are executed 10 times every 21 cycles.

[0060] Start a loop operation, and each loop performs the following operations on the orders consolidated in the warehouse: Single route optimization operation, route planning based on single route optimization operation. According to the specific method of single route optimization described above (determine the initial path based on the order generation sequence and the distribution of delivery locations, and optimize the route by adjusting the order location), calculate in detail the total distance between each order on each route after this operation and the estimated delivery time, and accurately record these data.

[0061] The partial order swap operation of two routes is used to plan routes based on the partial order swap operation of two routes. According to the corresponding specific method (select two routes, determine the swap range, perform the swap operation and recalculate the time and distance), the total delivery time and distance of the two routes are calculated in detail, and the relevant data are accurately recorded.

[0062] The separation and insertion of part of the orders on the two routes are used to plan the routes. According to the specific method (determine the operation nodes, perform separation and insertion operations, and recalculate the time and distance), the total delivery time and distance of the two routes are carefully calculated, and the relevant data are accurately recorded.

[0063] After each cycle, the data obtained from the above three operations are compared and analyzed. The delivery time and total distance under different operations are compared, and the solution corresponding to the operation with the shortest delivery time and the smallest total distance is selected as the best solution.

[0064] If the operation corresponding to the best solution is not the last operation in the current cycle, all orders will be re-routed in the operation mode corresponding to the best solution. For example, if the best solution is to swap some orders on two routes, but there are other operations that have not been completed in the current cycle, all orders will be re-routed in the same way as the two routes. Then, the subsequent cycles will continue according to the above-set operation frequency until the operation time exceeds the set duration. During the whole process, the data of each cycle is continuously recorded for subsequent analysis and optimization of the route planning strategy.

[0065] Specifically, in some possible embodiments, the method further includes: In a cycle where the single route optimization operation frequency is once per N cycles, the two routes partial order swap operation frequency and the two routes partial order separation and insertion operation frequency are both M times per N cycles, determine whether the route planning effect is improved; If the route planning effect is not improved, that is, the sum of the delivery time and distance has no optimization trend, a random route planning operation is performed.

[0066] Among them, in the process of looping according to the set operation frequency (the optimization operation frequency of a single route is once every N cycles, the operation frequency of partial order exchange between two routes and the operation frequency of partial order separation and insertion between two routes are all M times every N cycles), the route planning effect must be judged after each cycle. The specific judgment method is to compare the data of the total delivery time and distance in this cycle with the previous cycle (if it is the first cycle, an initial benchmark value can be set). If the delivery time does not decrease and the total distance does not decrease, it is considered that the route planning effect has not been improved and there is no optimization trend in the total delivery time and distance.

[0067] When it is determined that the route planning effect has not been improved, a random route planning operation is performed. The random route planning operation can be implemented in the following ways: For the orders consolidated in the warehouse, randomly disrupt the initial route assignment of the orders (if there is an initial route assignment). Or randomly select a certain number of orders (for example, randomly select 10%-20% of the total number of orders), and then randomly redistribute these orders to different routes (if there are multiple routes). After the random redistribution, recalculate the total delivery time and distance, and then continue to perform subsequent cycles according to the set operation frequency to determine again whether the route planning effect has been improved.

[0068] Specifically, in some possible embodiments, the method further includes: Obtain historical order data, traffic data, and warehouse operation data; Analyze the order time distribution, order type distribution and order volume change trend in historical order data; Analyze traffic data to determine road congestion at different times, road construction information, and the impact of special events on traffic; Analyze warehouse operation data including warehouse cargo in and out volume, warehouse inventory level, and warehouse equipment operating status; Use machine learning algorithms to train the historical order data, traffic data, and warehouse operation data analyzed above; Predict future order volumes, traffic congestion, and warehouse load trends; Based on the forecast results, determine whether the order allocation strategy and route planning need to be adjusted; If it is predicted that the future order volume will increase and traffic congestion will increase, or the warehouse load is close to saturation, the order allocation rules will be re-evaluated; If it is predicted that traffic congestion is serious in a specific area, the orders involving that area will be re-routed to avoid congested sections and re-select the delivery route; During the prediction and adjustment process, the parameters of the prediction model are updated in real time to improve the accuracy and adaptability of the prediction.

[0069] Among them, extract order data from the past period of time (such as the past few months or a year) from the order management system, including order placement time, delivery time, order type (such as ordinary goods, fresh food, large items, etc.), order quantity and other information. Obtain historical traffic data from traffic management departments, map navigation software providers or third-party traffic data service platforms, covering the average speed of roads in different periods, congestion index, road construction information (construction section, construction time, etc.) and the impact of special events (such as large-scale events, traffic accidents, etc.) on traffic. Obtain the in and out volume records of warehouse goods, current inventory levels, and the operating status of warehouse equipment (such as equipment failure records, maintenance time, etc.) from the warehouse management system.

[0070] Analyze the time distribution of orders, such as counting the number of orders in different time periods (such as different hours of the day, different days of the week, different dates of the month, etc.) to understand the time pattern of orders. Analyze the distribution of order types to determine the proportion of various types of orders and their changing trends. Analyze the changing trend of order volume, and observe the growth, decline or fluctuation of order volume by drawing a curve of order volume over time.

[0071] Analyze the road congestion at different times, make congestion maps for different time periods, and understand which sections are prone to congestion at which times. Sort out road construction information, clarify the location of the construction section, the construction time range, and the impact on traffic. Consider the impact of special events on traffic, analyze the time and location of special events, and the impact on traffic on surrounding roads.

[0072] Analyze the volume of goods entering and leaving the warehouse, count the changes in the volume of goods entering and leaving the warehouse on a daily, weekly or monthly basis, and understand the flow of goods in the warehouse. Monitor the warehouse inventory level, and grasp the inventory quantity, inventory turnover rate and other indicators of the warehouse in different time periods. Analyze the operating status of warehouse equipment, evaluate the reliability and maintenance needs of the equipment, and ensure the normal operation of the warehouse.

[0073] Select appropriate machine learning algorithms, such as time series prediction algorithms and regression algorithms, to train the analyzed historical order data, traffic data, and warehouse operation data. Use historical order volume, traffic congestion index, warehouse inventory level and other data as input features, and the corresponding future time period order volume, traffic congestion, warehouse load and other data as output targets for model training. Improve the accuracy and generalization ability of the model by adjusting model parameters, selecting different algorithms or performing feature engineering.

[0074] Use the trained machine learning model to predict the order volume, traffic congestion, and warehouse load change trends in the future. Predictions can be made based on different time granularities, such as predicting the situation in the next day, week, or month.

[0075] Based on the prediction results, determine whether the order allocation strategy and route planning need to be adjusted. If it is predicted that the order volume will increase in the future and traffic congestion will intensify, or the warehouse load is close to saturation, re-evaluate the order allocation rules. For example, adjust the threshold of order allocation, optimize the priority of warehouse selection, etc. If it is predicted that traffic congestion in a specific area is serious, re-plan the route for orders involving this area. You can use the map navigation algorithm to find a new delivery path that avoids congested sections and re-select the optimal route.

[0076] During the prediction and adjustment process, new data is collected in real time, such as current order status, traffic changes, and warehouse operation status. The parameters of the prediction model are continuously updated with new data to improve the accuracy and adaptability of the prediction. For example, when it is found that the prediction results of the model deviate from the actual situation, the model parameters are adjusted in time to make it better adapt to the changes in the actual situation.

[0077] Another embodiment of the present application provides a route planning system based on a phased and combined taboo algorithm, wherein, referring to Figure 2 , a route planning system based on phased and combined tabu algorithms, including: Order information and warehouse information acquisition module 100: used to acquire order information of an order and warehouse information that can undertake the order; Distance calculation module 200: Calculate the distance between the order location and each warehouse location based on the order information and the warehouse information; Order distribution and integration module 300: distributes the orders to corresponding warehouses for integration according to the distance and preset order distribution rules; Route planning operation module 400: performs route planning operation on the orders integrated in the warehouse, wherein the route planning operation includes at least one of single route optimization, partial order exchange between two routes, and partial order separation and insertion between two routes.

[0078] The route planning system based on the phased and combined taboo algorithm provided in this embodiment can implement the various steps of the aforementioned embodiments due to the functions of its modules themselves and the logical connections between each other, and thus can achieve the same technical effects as the aforementioned embodiments. The principle analysis can be found in the relevant description of the aforementioned route planning method steps based on the phased and combined taboo algorithm, which will not be repeated here.

[0079] An embodiment of the present application also provides a route planning device based on a phased and combined taboo algorithm, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the above-mentioned route planning method based on a phased and combined taboo algorithm.

[0080] An embodiment of the present application further provides a storage medium, in which a computer program that can be loaded by a processor and execute the above-mentioned route planning method based on the phased and combined taboo algorithm is stored.

[0081] The storage medium provided in this embodiment can achieve the same technical effects as the aforementioned embodiments because the computer program therein implements the various steps of the aforementioned embodiments after being loaded and run on the processor. The principle analysis can be found in the relevant description of the aforementioned method steps, which will not be repeated here.

[0082] The storage medium includes, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0083] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0084] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0085] In addition, the term "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined, and is only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0086] Thus, any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0087] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A route planning method based on a phased and combined taboo algorithm, characterized in that: include: Obtaining order information of an order and warehouse information that can accept the order; Based on the order information and the warehouse information, calculating the distance between the order location and each warehouse location; According to the distance and the preset order allocation rules, the order is allocated to the corresponding warehouse for integration; A route planning operation is performed on the orders integrated in the warehouse, wherein the route planning operation includes at least one of single route optimization, partial order interchange between two routes, and partial order separation and insertion between two routes.

2. The route planning method based on the phased and combined taboo algorithm according to claim 1, characterized in that: The steps to calculate the distance between the order location and each warehouse location include: Obtain traffic information and road type information between the order location and the warehouse location, as well as the order delivery time requirements and the current time period; Analyze the delivery time requirement of the order and the time period information of the current time; Determine whether the delivery time requirement of the order is during a peak period; If the delivery time requirement of the order is to be delivered during peak hours, or the current time is during peak hours and the order has no special time requirement, then the road weight for marking the delivery of the order needs to be adjusted; Based on the traffic condition information and road type information, a dynamic weight is set for the road where the order is to be delivered; The weighted road weights are used in combination with the actual geographical distance between the order location and the warehouse to calculate the dynamic distance between the order location and each warehouse.

3. The route planning method based on the phased and combined taboo algorithm according to claim 2 is characterized in that: According to the distance and the preset order allocation rules, the step of allocating the order to the corresponding warehouse for integration includes: Setting a first preset threshold and a second preset threshold; If the distance between the order location and a certain warehouse location is less than the first preset threshold, the order is preferentially allocated to the warehouse; If the distance between the order location and a certain warehouse location is greater than the first preset threshold, determining whether there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold; if there are multiple warehouse locations whose dynamic distances to the order location are within the second preset threshold, comprehensively analyzing the load conditions of multiple warehouses, order priorities, and warehouse delivery capabilities to determine the warehouse to which the order is assigned; If there is only one warehouse location whose distance to the order location is within the second preset threshold range, the order is allocated to the warehouse; During the order allocation process, the warehouse load information and order allocation status are updated in real time.

4. The route planning method based on the phased and combined taboo algorithm according to claim 1, characterized in that: Performing route planning operations on the integrated orders in the warehouse, wherein the route planning operations include at least one of single route optimization, partial order exchange of two routes, and partial order separation and insertion of two routes, including: For the orders consolidated in the warehouse, determine and obtain initial conditions for performing route planning operations, the initial conditions including order quantity, order delivery time requirements, distance distribution between the warehouse and the delivery destination, and other constraints; If you select single line optimization operation: Determine the initial path for each route based on the order in which the orders are generated and the distribution of delivery locations; Calculate the sum of distances and estimated delivery time between orders on each route; optimize the route by adjusting the positions of orders on a single route so as to minimize the sum of distances and optimize the estimated delivery time, while satisfying the delivery time requirements of the orders and the other constraints; If you choose to swap some orders of two lines: Select two lines from the integrated order as operation objects; Determine the range of orders that can be interchanged, taking into account the delivery time requirements of the orders, the locations of the orders on their respective routes, and the impact of the interchange on the overall delivery time and distance of the two routes; Perform order swap operations, recalculate the delivery time and distance of the two routes, compare the results before and after the swap, and retain the route configuration after the swap if it is optimized; If two lines of partial order separation and insertion operations are selected: For the two selected lines, determine the order nodes where separation and insertion operations can be performed; Separate some orders from one line and try to insert them into the appropriate position of another line; Recalculate the delivery time and distance of the two routes, evaluate the effect of the operation, and update the route configuration if the optimization conditions are met.

5. The route planning method based on the phased and combined taboo algorithm according to claim 4, characterized in that: The steps for routing orders consolidated within a warehouse include: Set the operation frequency parameters, where the single line optimization operation frequency is once every N cycles, the two lines partial order swap operation frequency and the two lines partial order separation and insertion operation frequency are both M times every N cycles; For orders consolidated within the warehouse, follow these steps: Carry out route planning based on the optimization operation of a single route, calculate in detail and accurately record the relevant data on the delivery time and total distance involved after this operation; Route planning is carried out based on the exchange of some orders on the two routes, and the corresponding delivery time and total distance data are also calculated and accurately recorded in detail; Plan routes based on the separation and insertion of partial orders on two routes, carefully calculate and accurately record relevant delivery time and total distance data; Compare and analyze the data obtained from the above three operations to determine the best solution; If the operation corresponding to the optimal solution is not the last operation in the currently ongoing cycle, all orders will be re-routed using the operation method corresponding to the optimal solution, and then subsequent cycles will continue according to the above-set operation frequency until the operation time exceeds the set duration.

6. The route planning method based on the phased and combined taboo algorithm according to claim 5, characterized in that: The method also includes: In a cycle where the single route optimization operation frequency is once per N cycles, the two routes partial order swap operation frequency and the two routes partial order separation and insertion operation frequency are both M times per N cycles, determine whether the route planning effect is improved; If the route planning effect is not improved, that is, the sum of the delivery time and distance has no optimization trend, a random route planning operation is performed.

7. The route planning method based on phased and combined taboo algorithm according to claim 1, characterized in that: The method also includes: Obtain historical order data, traffic data, and warehouse operation data; Analyze the order time distribution, order type distribution and order volume change trend in historical order data; Analyze traffic data to determine road congestion at different times, road construction information, and the impact of special events on traffic; Analyze warehouse operation data including warehouse cargo in and out volume, warehouse inventory level, and warehouse equipment operating status; Use machine learning algorithms to train the historical order data, traffic data, and warehouse operation data analyzed above; Predict future order volumes, traffic congestion, and warehouse load trends; Based on the forecast results, determine whether the order allocation strategy and route planning need to be adjusted; If it is predicted that the order volume will increase in the future and traffic congestion will increase, or the warehouse load is close to saturation, the order allocation rules will be re-evaluated; If it is predicted that traffic congestion is serious in a specific area, the orders involving that area will be re-routed to avoid congested sections and re-select the delivery route; During the prediction and adjustment process, the parameters of the prediction model are updated in real time to improve the accuracy and adaptability of the prediction.

8. A route planning system based on a phased and combined taboo algorithm, characterized in that, include: Order information and warehouse information acquisition module: used to obtain order information of an order and warehouse information that can accept the order; Distance calculation module: based on the order information and the warehouse information, calculates the distance between the order location and each warehouse location; Order distribution and integration module: according to the distance and the preset order distribution rules, the orders are distributed to the corresponding warehouses for integration; Route planning operation module: performs route planning operations on the orders integrated in the warehouse, and the route planning operations include at least one of single route optimization, partial order exchange between two routes, and partial order separation and insertion between two routes.

9. A route planning device based on a phased and combined taboo algorithm, characterized in that include: A memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes any of the route planning methods based on phased and combined taboo algorithms according to claims 1-7.

10. A storage medium, characterized in that: The computer program is stored which can be loaded by a processor and execute the route planning method based on any one of the phased and combined taboo algorithms of claims 1 to 7.

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