Whole vehicle logistics loading optimization method based on uniform counterweight constraint

Abstract

The invention discloses a whole vehicle logistics loading optimization method based on uniform counterweight constraints, which comprises the following steps of: taking a to-be-transported commercial vehicle attribute set and an available car transport vehicle attribute set as input data, setting model parameters and algorithm parameters, setting decision variables and objective functions, setting uniform counterweight constraints, and constructing a mixed integer programming model; and solving the model by adopting a memetic algorithm, and taking an optimal solution of a target function value as a final scheduling scheme to realize transportation income maximization.

Description

A method for optimizing vehicle logistics loading based on uniform counterweight constraints Technical Field

[0001] This invention belongs to the field of logistics scheduling algorithm technology, and relates to a multidimensional constraint and meme algorithm technology. Background Technology

[0002] Vehicle logistics is a crucial link in the automotive industry chain, directly impacting transportation costs and safety. The increased weight of new energy vehicles makes it difficult for traditional manual dispatching to balance loading and weight distribution with economic efficiency, increasing the risk of accidents such as skidding and rollovers.

[0003] Existing research focuses on vehicle routing and loading optimization. Loading models only consider limitations such as size and load, without deeply considering the actual safety constraints of uniform weight distribution, making it difficult to cope with the problem of uneven weight distribution.

[0004] Some studies use heuristic algorithms for loading optimization, but the solution efficiency is low in large-scale scenarios and they are not organically combined with uniform weight constraints. Summary of the Invention

[0005] This invention proposes a vehicle loading optimization method based on uniform weight constraints. The method uses a set of attributes of the vehicles to be transported and a set of attributes of available car carriers as input data. The set of attributes of the vehicles to be transported includes the length, height, weight, value, and destination number of each vehicle. The set of attributes of available car carriers includes whether each car carrier is a full trailer / center axle type, the length of the deck, the upper limit of the deck height, the upper limit of the load capacity, and the number of parking spaces. Model parameters and algorithm parameters are set. Model parameters include center of gravity offset parameters, cross-regional cost weights, full load rate penalty weights, and safety distances. Algorithm parameters include population size, maximum running time, and random greedy parameters. Decision variables and an objective function are also set. The objective function includes... The system uses a weighted average of three factors: revenue from transporting commercial vehicles, cost of detour penalties for cross-regional travel, and cost of full load penalty. A mixed-integer programming model is constructed with uniform weight constraints, including length constraints, height constraints, non-overlapping position constraints, centroid constraints in the height and length directions, destination validity constraints, loading path consistency constraints, and cross-regional delivery logic constraints. A meme algorithm is used to solve the model. After population initialization, crossover operators, local searches, and population management are repeatedly performed until the preset maximum running time. The optimal solution to the objective function is used as the final scheduling scheme, with the destination of each car carrier, the loaded commercial vehicles, and their positions on the deck as output data to maximize transportation revenue.

[0006] Collection of goods vehicles awaiting transport Includes: each vehicle length ,high ,weight ,value , destination number .

[0007] Available car carriers collection including: each car carrier is a full-trailer / mid-axle type, deck length deck height upper limit load capacity upper limit rated number of vehicle spaces .

[0008] Model parameters include: center of gravity offset parameter cross-region cost weight full load rate penalty weight safety distance .

[0009] Algorithm parameters include: population size maximum running time<00,00042>"random greedy parameter .

[0010] Decision variables include , , , : If the commercial vehicle is loaded onto the deck of the car carrier )], thentakes the value of 1, otherwise 0; the commercial vehicle on the deck of the car carrier[[ID=,000055]] is the horizontal starting position ; If the car carrier goes to the destination area , then takes the value of 1, otherwise 0; If the car carrier goes to the destination area combination simultaneously, then takes the value of 1, otherwise 0.

[0011] The revenue from transporting commercial vehicles represents the total value of all loaded commercial vehicles, assigns greater weights to high-value or high-urgency orders, and gives priority to meeting core transportation needs; the cross-region detour penalty cost penalizes a single car carrier mission involving multiple destination areas, inhibits complex cross-region distribution paths, and reduces the additional energy consumption and time cost caused by detours; the full load rate penalty cost penalizes the actual loading volume of the car carrier not reaching the rated full load volume, drives the model to improve the vehicle loading rate under the premise of meeting the constraints, and achieves the optimal solution of economic benefits and the utilization efficiency of transport capacity resources.​

[0012] The objective function is set to ,in It is a collection of all combinations of car carriers that can reach their destinations. Destination area Cross-regional costs, It is a car carrier. The full load rate makes Used to calculate the load factor, where This refers to the actual number of vehicles loaded. It specifies the number of fully loaded vehicles. The value is set according to the actual situation and is not used as a loading constraint. If the actual number of loaded vehicles exceeds the specified number, the load factor is set to 1, and the actual value exceeding 1 is discarded.

[0013] Length constraint: Loaded commercial vehicle length Safe distance The total length does not exceed the deck Effective length ,Right now .

[0014] Height constraint: Loaded commercial vehicle height No more than the deck Maximum loading height ,Right now .

[0015] Position non-overlap constraint: If the commercial vehicle and At the same time in the car carrier deck Above, and exist Previously, The end position plus the safety distance not exceeding The starting position, i.e. This ensures that any two vehicles on the same deck do not overlap in physical space.

[0016] Height-direction center of gravity constraint: Taking the junction of the first deck and the foremost point of the car carrier deck as the starting position, the height-direction center of gravity of the entire vehicle after loading is restricted. The center of gravity of the vehicle on the first deck is half the height of the vehicle, and the center of gravity of the vehicle on the second deck is half the height of the vehicle plus the sum of the distance from the second deck to the first deck. ,in It is a commercial vehicle. It is loaded at the center of gravity along the height of the car carrier to prevent the vehicle from tipping over due to an excessively high center of gravity.

[0017] Length direction center of gravity constraint: restricts the position of the vehicle's center of gravity along its length after loading. and ,in It is a commercial vehicle. The center of gravity along the length direction is taken as half the length of the vehicle. It is a car carrier. The deck length is designed to prevent the vehicle from fishtailing or becoming too heavy to steer.

[0018] For full-trailer car carriers, the aforementioned uniform weight constraint is used. For center-axle car carriers, the center-of-gravity offset constraints in the height direction are calculated separately for the tractor and trailer. Multiple parking spaces are equivalent to different decks, and the center-of-gravity offset constraints in the length direction are calculated separately for each deck. ,in It is a deck assembly for tractor units. , It is a collection of center-axle car carriers. The weight of the commercial vehicle loaded on the tractor should not be less than 0.3 times the total weight, to avoid the tractor slipping and spinning due to excessive load.

[0019] Destination validity constraint: If the car carrier Heading to the destination Then at least one vehicle must be loaded to the destination. The commercial vehicles, namely This is to prevent car carriers from driving empty to a destination or on a planned route without loading the corresponding vehicles.

[0020] Load path consistency constraint: If the destination is commercial vehicles Loaded into car carrier Therefore, route planning must include the destination. ,Right now Ensure delivery.

[0021] Cross-regional delivery logic constraints: If the car carrier vehicle The path also has a destination area and ,Right now =1 and =1, then the cross-regional indicator variable It must be activated, i.e., its value is 1, and the corresponding cross-regional cost is calculated in the objective function. .

[0022] The meme algorithm employs a branch and bound algorithm for single-vehicle optimization to determine the optimal internal loading scheme for a single car carrier. The branch and bound algorithm uses a depth-first search, recursively enumerating all combinations of vehicle layer loading, and calculating in real time the minimum space required by the number of loaded vehicles and the unloaded vehicles. If the remaining space in the current upper layer cannot exceed the optimal solution when loading the remaining vehicles, the branch is pruned. If a complete loading scheme is obtained, it is checked whether all constraints are satisfied. If they are satisfied and the remaining space in the upper layer is greater than the current optimal solution, the optimal solution is updated, resulting in a loading scheme with a fixed set of vehicles that satisfies all constraints and maximizes the utilization of upper layer space.

[0023] Population initialization uses a randomized greedy construction method to generate initial feasible solutions: each car carrier is randomly assigned an initial destination, and the population size is generated using a randomized greedy construction method. Given four initial solutions, unassigned vehicles are sorted in descending order of their contribution to the objective function. A random priority method is used to select vehicles, attempting to add the current car carrier. A branch and bound algorithm for single-vehicle optimization is called to verify whether adding the car carrier satisfies all constraints. The internal loading scheme of the car carrier is optimized; otherwise, backtracking is performed to try other vehicles, selecting from the generated solutions. The initial population is formed by maximizing the objective function value. By applying local search to all individuals, the quality of the solution is improved.

[0024] The crossover operator uses destination inheritance and random selection to generate offspring solutions: from the population Two parent solutions are randomly selected from the middle. , Iterate through all car carriers. If two parent car carriers have the same destination, the child car carrier inherits the destination. Otherwise, it randomly selects a car carrier from the parent car carriers. Based on the new destination, it uses a randomized greedy construction method to reload the car carriers in the child car carrier solution. Iterate through all the car carriers in the child car carrier solution and execute a reversal mechanism. It attempts to remove a car and greedily loads other unassigned cars. If the contribution of the objective function increases, it accepts the change.

[0025] Local search employs a commutative operator for neighborhood search: destinations are selected from the current solution. and The two car carriers were swapped, their original loading plans were cleared, and they were reloaded for their new destinations. A cancellation mechanism was implemented. Indicates the current solution Using operators Obtain the neighborhood, i.e. ; Change the original destination of any car carrier in the current solution Adjust to a different destination, clear the original loading plan, reload to the new destination, execute the undo mechanism, and use... Indicates the current solution Using operators Obtain the neighborhood, i.e. Each time a new loading scheme is generated, the branch and bound algorithm for single-vehicle optimization is immediately invoked to satisfy all constraints and optimize single-vehicle loading. If the two operators cannot continue to improve the current solution in their respective neighborhoods, the algorithm terminates.

[0026] Population management updates the population based on the quality and diversity of solutions: it updates the offspring solutions that achieve local optimization. Compared with the current population Comparison, if If the objective value is different from all solutions in the population and is better than the worst solution in the population, then use... Replace the worst solution; otherwise discard it. . Attached Figure Description

[0027] Figure 1 is a flowchart of the meme algorithm. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the embodiments and the accompanying drawings.

[0029] I. Input Data

[0030] 1. Set of attributes of the vehicles to be transported Each commercial vehicle length ,high ,weight ,value Destination number .

[0031] 2. Available car carrier attribute set Each car carrier It is a fully mounted / center-axle type, deck type length ,deck Upper limit of height Maximum load capacity Number of parking spaces .

[0032] II. Model Building

[0033] 1. Set model parameters: center of gravity offset parameter Generally, a weight of 0.1 is used for cross-regional cost. Full load rate penalty weight Safe distance Not less than 0.1m.

[0034] 2. Set algorithm parameters: population size Maximum running time Random greedy parameter 。

[0035] 3. Set decision variables 、 、 、 : If the commercial vehicle is loaded onto the deck of the car carrier , then takes the value of 1, otherwise 0; the commercial vehicle is at the transverse starting position on the deck of the car carrier as ; If the car carrier goes to the destination area , then takes the value of 1, otherwise 0; If the car carrier goes to the destination area combination simultaneously, then takes the value of 1, otherwise 0.

[0036] 4. Set the objective function , where is the set of all car carriers that can reach the destination combination, is the cross-region cost of the destination area , is the full-load rate of the car carrier . Let be used to calculate the full-load rate, where is the actual number of commercial vehicles loaded, is the specified number of commercial vehicles for full load. The value is set according to the actual situation and is not used as a loading constraint. If the actual number of commercial vehicles loaded exceeds the specified number, the full-load rate takes the value of l, and the actual value exceeding 1 is discarded.

[0037] 5. Set length constraints, height constraints, non-overlapping position constraints, height-direction center-of-gravity constraints, length-direction center-of-gravity constraints, destination validity constraints, loading path consistency constraints, and cross-region distribution logic constraints. <00,00251>Length constraint: The total length of the loaded commercial vehicle and the safety distance does not exceed the effective length of the deck , that is . <0000�57>。

[0039] Height constraint: The height of the loaded commercial vehicle does not exceed the maximum loading height of the deck ,Right now .

[0040] Position non-overlap constraint: If the commercial vehicle and Meanwhile, in the car carrier deck Above, and exist Previously, The end position plus the safety distance not exceeding The starting position, i.e. This ensures that any two vehicles on the same deck do not overlap in physical space.

[0041] Height-direction center of gravity constraint: Taking the junction of the first deck and the foremost point of the car carrier deck as the starting position, the height-direction center of gravity of the entire vehicle after loading is restricted. The center of gravity of the vehicle on the first deck is half the height of the vehicle, and the center of gravity of the vehicle on the second deck is half the height of the vehicle plus the sum of the distance from the second deck to the first deck. ,in It is a commercial vehicle. It is loaded at the center of gravity along the height of the car carrier to prevent the vehicle from tipping over due to an excessively high center of gravity.

[0042] Length direction center of gravity constraint: restricts the position of the vehicle's center of gravity along its length after loading. and ,in It is a commercial vehicle. The center of gravity along the length direction is taken as half the length of the vehicle. It is a car carrier. The deck length is designed to prevent the vehicle from fishtailing or becoming too heavy to steer.

[0043] Destination validity constraint: If the car carrier Heading to the destination Then at least one vehicle must be loaded to the destination. The commercial vehicles, namely This is to prevent car carriers from driving empty to a destination or on a planned route without loading the corresponding vehicles.

[0044] Load path consistency constraint: If the destination is commercial vehicles Loaded into car carrier Therefore, route planning must include the destination. ,Right now Ensure delivery.

[0045] Cross-regional delivery logic constraints: If the car carrier vehicle The path also has a destination area and ,Right now =1 and =1, then the cross-regional indicator variable It must be activated, i.e., its value is 1, and the corresponding cross-regional cost is calculated in the objective function. .

[0046] III. Model solution, as shown in Figure 1.

[0047] A branch and bound algorithm is used for single-vehicle optimization to determine the optimal internal loading scheme for a single car carrier. The branch and bound algorithm uses depth-first search, recursively enumerating all combinations of vehicle layer loading, and calculates in real time the number of loaded vehicles and the minimum space required by unloaded vehicles. If the remaining space in the current upper layer cannot exceed the optimal solution when loading the remaining vehicles, the branch is pruned. If a complete loading scheme is obtained, it is checked whether all constraints are satisfied. If they are satisfied and the remaining space in the upper layer is greater than the current optimal solution, the optimal solution is updated, resulting in a loading scheme with a fixed set of vehicles that satisfies all constraints and maximizes the utilization of upper layer space.

[0048] 1. Population initialization: An initial feasible solution is generated using a randomized greedy construction method. Each car carrier is randomly assigned an initial destination, and the population size is generated using a randomized greedy construction method. Given four initial solutions, unassigned vehicles are sorted in descending order of their contribution to the objective function. A random priority method is used to select vehicles, attempting to add the current car carrier. A branch and bound algorithm for single-vehicle optimization is called to verify whether adding the car carrier satisfies all constraints. The internal loading scheme of the car carrier is optimized; otherwise, backtracking is performed to try other vehicles, selecting from the generated solutions. The initial population is formed by maximizing the objective function value. By applying local search to all individuals, the quality of the solution is improved.

[0049] 2. Crossover operator, using destination inheritance and random selection to generate offspring solutions: from the population Two parent solutions are randomly selected from the middle. , Iterate through all car carriers. If two parent car carriers have the same destination, the child car carrier inherits the destination. Otherwise, it randomly selects a car carrier from the parent car carriers. Based on the new destination, it uses a randomized greedy construction method to reload the car carriers in the child car carrier solution. Iterate through all the car carriers in the child car carrier solution and execute a reversal mechanism. It attempts to remove a car and greedily loads other unassigned cars. If the contribution of the objective function increases, it accepts the change.

[0050] 3. Local search, using a commutative operator for neighborhood search: Destinations are selected from the current solution as follows: and The two car carriers were swapped, their original loading plans were cleared, and they were reloaded for their new destinations. A cancellation mechanism was implemented. Indicates the current solution Use operators Obtain the neighborhood, i.e. ; Change the original destination of any car carrier in the current solution Adjust to a different destination, clear the original loading plan, reload to the new destination, execute the undo mechanism, and use... Indicates the current solution Use operators Obtain the neighborhood, i.e. Each time a new loading scheme is generated, the branch and bound algorithm for single-vehicle optimization is immediately invoked to satisfy all constraints and optimize single-vehicle loading. If the two operators cannot continue to improve the current solution in their respective neighborhoods, the algorithm terminates.

[0051] 4. Population management: Update the population based on the quality and diversity of solutions: Replace locally optimized offspring solutions... Compared with the current population Comparison, if If the objective value is different from all solutions in the population and is better than the worst solution in the population, then use... Replace the worst solution; otherwise discard it. .

[0052] Repeat steps 2-4 until the preset maximum running time is reached.

[0053] IV. Output Data

[0054] The optimal solution of the objective function value found during the search process As the final scheduling scheme, output the destination of each car carrier, the loaded vehicles, and their positions on the deck.

Claims

1. A method for optimizing vehicle logistics loading based on uniform counterweight constraints, characterized in that, include: The set of attributes of the vehicles to be transported and the set of attributes of available car carriers are used as input data. The set of attributes of the vehicles to be transported includes the length, height, weight, value, and destination number of each vehicle. The set of attributes of available car carriers includes whether each car carrier is a full trailer / center axle, the length of the deck, the upper limit of the deck height, the upper limit of the load capacity, and the number of parking spaces. Model parameters and algorithm parameters are set. The model parameters include the center of gravity offset parameter, the cross-regional cost weight, the full load rate penalty weight, and the safety distance. The algorithm parameters include the population size, the maximum running time, and the random greedy parameter. Decision variables and objective functions are set up. The objective function includes three weighted factors: revenue from transporting commercial vehicles, penalty costs for cross-regional detours, and penalty costs for full load rate. A mixed-integer programming model is constructed with uniform weight constraints, including length constraints, height constraints, non-overlapping position constraints, centroid constraints in the height and length directions, destination validity constraints, loading path consistency constraints, and cross-regional delivery logic constraints. The meme algorithm is used to solve the model. After population initialization, crossover operators, local search, and population management are repeatedly performed until the preset maximum running time. The optimal solution of the objective function value is used as the final scheduling scheme. The destination of each car carrier, each loaded commercial vehicle, and its position on the deck are used as output data to maximize transportation revenue.

2. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 1, characterized in that, The revenue from transporting goods vehicles represents the total value of all loaded goods vehicles, giving greater weight to high-value or high-urgency orders and prioritizing core transportation needs; the cross-regional detour penalty cost penalizes car carriers for single missions involving multiple destination areas, suppressing complex cross-regional delivery routes and reducing the additional energy and time costs caused by detours; the full load rate penalty cost penalizes car carriers for actual loading volumes below the rated full load, driving the model to improve vehicle loading rates while satisfying constraints, achieving the optimal solution for economic benefits and transportation capacity utilization efficiency.

3. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 2, characterized in that, The meme algorithm includes: using a branch and bound algorithm for single-vehicle optimization to determine the optimal internal loading scheme for a single car carrier; the branch and bound algorithm uses a depth-first search, recursively enumerating all combinations of vehicle layer loading, and calculating in real time the number of loaded vehicles and the minimum space required by unloaded vehicles. If the remaining space in the current upper layer cannot exceed the optimal solution when loading the remaining vehicles, then the branch is pruned. If a complete loading scheme is obtained, then it is checked whether all constraints are satisfied. If they are satisfied and the remaining space in the upper layer is greater than the current optimal solution, then the optimal solution is updated to obtain a loading scheme with a fixed set of vehicles that satisfies all constraints and maximizes the utilization rate of the upper layer space.

4. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 3, characterized in that, The set of attributes of the vehicles to be transported is as follows: This includes: each commercial vehicle length ,high ,weight ,value Destination number The set of available car carrier attributes is as follows: This includes: each car carrier It is a fully attached / center-axle type, deck type length ,deck Upper limit of height Maximum load capacity Number of parking spaces The model parameters include: centroid offset parameters. Cross-regional cost weighting Full load rate penalty weight Safe distance The algorithm parameters include: population size. Maximum running time Random greedy parameter 。 5. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 4, characterized in that, The decision variables include: 、 、 、 If the commercial vehicle Loaded into car carrier deck Above, then The value is 1, otherwise it is 0; production vehicle In the car carrier deck The horizontal starting position is If car carrier Heading to the destination area ,but The value is 1 if it is a car carrier, and 0 otherwise. Simultaneously heading to the destination area combination ,but The value is 1 if it is not 0 otherwise.

6. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 5, characterized in that, The objective function is set as follows: ,in It is a collection of all combinations of car carriers that can reach their destinations. Destination area Cross-regional costs, It is a car carrier. The full load rate makes Used to calculate the load factor, where This refers to the actual number of vehicles loaded. It specifies the number of fully loaded vehicles. The value is set according to the actual situation and is not used as a loading constraint. If the actual number of loaded vehicles exceeds the specified number, the load factor is set to 1, and the actual value exceeding 1 is discarded.

7. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 6, characterized in that, The length constraint includes: loaded commercial vehicles. length Safe distance The total length does not exceed the deck Effective length ,Right now The height constraint includes: the loaded commercial vehicle. height No more than the deck Maximum loading height ,Right now The non-overlapping position constraint includes: if the commercial vehicle and Meanwhile, in the car carrier deck Above, and exist Previously, The end position plus the safety distance not exceeding The starting position, i.e. This ensures that no two vehicles on the same deck will overlap in physical space. The height-direction center of gravity constraint includes: using the junction of the first deck and the foremost point of the car carrier deck as the starting position, restricting the center of gravity of the entire vehicle in the height direction after loading. The center of gravity of the vehicle on the first deck is half its height, and the center of gravity of the vehicle on the second deck is half its height plus the distance from the second deck to the first deck. ,in It is a commercial vehicle. The center of gravity is positioned at the height of the car carrier to prevent the vehicle from tipping over due to an excessively high center of gravity; the length-direction center of gravity constraint includes: limiting the position of the vehicle's center of gravity along its length after loading, i.e. and ,in It is a commercial vehicle. The center of gravity along the length direction is taken as half the length of the vehicle. It is a car carrier. The deck length is designed to prevent vehicle fishtailing or heavy steering; the destination validity constraints include: if the car carrier... Heading to the destination Then at least one vehicle must be loaded to the destination. The commercial vehicles, namely To prevent car carriers from driving empty to a destination or following a planned route without loading the corresponding vehicle; the loading route consistency constraint includes: if the destination is commercial vehicles Loaded into car carrier Therefore, route planning must include the destination. ,Right now Ensure delivery; the cross-regional delivery logic constraints include: if the car carrier... The path also has a destination area and ,Right now =1 and =1, then the cross-regional indicator variable It must be activated, i.e., its value is 1, and the corresponding cross-regional cost is calculated in the objective function. 。 8. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 7, characterized in that, The population initialization employs a randomized greedy construction method to generate initial feasible solutions: each car carrier is randomly assigned an initial destination, and the population size is generated using the randomized greedy construction method. Given four initial solutions, unassigned vehicles are sorted in descending order of their contribution to the objective function. A random priority method is used to select vehicles, attempting to add the current car carrier. A branch and bound algorithm for single-vehicle optimization is called to verify whether adding the car carrier satisfies all constraints. The internal loading scheme of the car carrier is optimized; otherwise, backtracking is performed to try other vehicles, selecting from the generated solutions. The initial population is formed by maximizing the objective function value. Local search is invoked on all individuals to improve the quality of solutions; the crossover operator uses destination inheritance and random selection to generate offspring solutions: from the population Two parent solutions are randomly selected from the middle. 、 The process iterates through all car carriers. If two parent solutions have the same destination, the child solution inherits that destination; otherwise, a new destination is randomly selected from the parent solutions. Based on the new destination, a randomized greedy construction method is used to reload vehicles into the child solution. The process iterates through all car carriers in the child solution, executing a reversal mechanism: attempting to remove one car and greedily loading other unassigned cars. If the contribution to the objective function increases, the change is accepted. The local search uses a swap operator for neighborhood search: destinations selected from the current solution are... and The two car carriers were swapped, their original loading plans were cleared, and they were reloaded for their new destinations. A cancellation mechanism was implemented. Indicates the current solution Using operators Obtain the neighborhood, i.e. The original destination of any car carrier currently in the solution. Adjust to a different destination, clear the original loading plan, reload to the new destination, execute the undo mechanism, and use... Indicates the current solution Using operators Obtain the neighborhood, i.e. The population management involves updating the population based on the quality and diversity of solutions: locally optimized offspring solutions are... Compared with the current population Comparison, if If the objective value is different from all solutions in the population and is better than the worst solution in the population, then use... Replace the worst solution; otherwise discard it. 。 9. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 7, characterized in that, Also includes: Full-trailer car carriers employ uniform counterweight constraints, while center-axle car carriers calculate the center-of-gravity offset constraints of the tractor and trailer separately in the height direction. Multiple parking spaces are represented as different decks, and the center-of-gravity offset constraints in the length direction of each deck are calculated separately. ,in It is a deck assembly for tractor units. ， It is a collection of center-axle car carriers. The weight of the commercial vehicle loaded on the tractor should not be less than 0.3 times the total weight, to avoid the tractor slipping and spinning due to excessive load.

10. The whole vehicle logistics loading optimization method based on uniform counterweight constraints according to claim 8, characterized in that, The local search includes: each time a new loading scheme is generated, immediately calling the branch and bound algorithm for single-vehicle optimization to satisfy all constraints and optimize single-vehicle loading; if the two operators cannot continue to improve the current solution in their respective neighborhoods, the algorithm terminates.

Images