Bus Route Planning Method for Passenger and Freight Co-Transportation Based on Adaptive Large Neighborhood Search Algorithm

Abstract

The present invention relates to the field of information and communication technologies applicable to logistics management purposes, aiming to provide a passenger and cargo co-transport bus route planning method based on an adaptive large neighborhood search algorithm. It includes: receiving passenger and cargo transportation information through reservation, determining the passenger and cargo co-transport strategy based on different demand characteristics; establishing a route optimization model, and on the premise of meeting all demands, focusing on considering factors such as vehicle capacity and time window constraints to seek the route plan with the lowest cost; solving the route optimization model based on the adaptive large neighborhood search algorithm, starting from the initial solution, using the destruction operation to break the current solution, and using the repair operation to explore a new solution space; continuously iterating to find the route plan that meets the goal of cost minimization. The present invention can achieve the supply-demand balance under the passenger and cargo co-transport mode, taking the priority to meet passenger demands as the core, while taking into account the cargo transportation service, providing a new perspective and solution for the efficient operation of the reservation-based bus system.

Description

Technical Field

[0001] The present invention relates to the field of information and communication technologies applicable to logistics management purposes, and in particular to a reservation-based passenger and freight co-transport bus route planning method based on an adaptive large neighborhood search algorithm. Background Art

[0002] During peak hours of urban traffic, due to the increased travel demand, the traffic volume has increased significantly. Freight vehicles, because of their slow driving speed and large volume, often become the bottleneck of the road, further exacerbating congestion and leading to a sharp increase in carbon emissions. On the contrary, during off-peak hours, the capacity of the urban bus system often has a surplus, which provides an idea for solving the problem of the imbalance between peak and off-peak passenger and freight transport demands - that is, during off-peak hours, make full use of the surplus capacity of the bus system to jointly carry part of the freight demand and achieve an effective integration of passenger and freight co-transport.

[0003] Therefore, how to effectively analyze and integrate passenger and freight demands, and reasonably plan the operation routes of reservation-based buses according to the actual situation, so that they can meet the needs of customers at the lowest cost, is a key problem that needs to be solved urgently. To address the above challenges, prioritize meeting the travel needs of passengers, and at the same time use the remaining capacity to transport goods. By constructing a mixed integer programming model for the vehicle routing problem in the passenger and freight co-transport scenario, and establishing a suitable algorithm to complete the solution.

[0004] Chinese invention patent CN111798067A proposes an autonomous vehicle distribution route planning method based on an adaptive large neighborhood search algorithm, including: calculating the distance between any two points; generating a vehicle route for distributing autonomous vehicles using only car carriers as the initial solution; using an adaptive large neighborhood search algorithm to select neighborhood operators to operate on the feasible solution to form a new feasible solution; evaluating the quality of the new feasible solution and then evaluating the neighborhood operators; using a simulated annealing algorithm to control whether the feasible solution of the adaptive large neighborhood search algorithm is accepted, and outputting a route planning scheme for the coordinated distribution of autonomous vehicles and car carriers until the algorithm reaches the termination condition. This method conducts distribution route planning for the distribution of special goods such as autonomous vehicles that can move their positions by themselves, and refines the mathematical model to solve this problem by adding a splitting operator and a fusion operator on the basis of the Removal operator and the Insertion operator. However, this technical solution is only applicable to the route planning of autonomous vehicles for a single distribution purpose (especially long-distance transportation between long distances), and cannot be used to achieve passenger and freight co-transport between freight vehicles with different distribution purposes and urban public transportation under urban traffic conditions.

[0005] To effectively solve the vehicle routing optimization model in the scenario of combined passenger and freight transportation, the selection of an optimization algorithm is crucial. Exact algorithms such as the branch and bound method and the cutting plane method can ensure obtaining the optimal solution. Through systematic search and pruning strategies, they can theoretically exhaust the solution space to achieve the goal of exact solution. However, due to high computational complexity, such algorithms often require a large amount of time. Especially in the case of a large problem scale, the solution efficiency is significantly affected. Therefore, the present invention aims to propose an algorithm mechanism more suitable for urban complex traffic conditions to improve the computational efficiency, better respond to the changes in passenger and freight demands in a complex dynamic environment, and exhibit superior flexibility and efficiency in practical applications. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies in the prior art and provide a method for planning the bus route of combined passenger and freight transportation based on the adaptive large neighborhood search algorithm.

[0007] To solve the above technical problem, the solution adopted by the present invention is:

[0008] Provide a method for planning the bus route of combined passenger and freight transportation based on the adaptive large neighborhood search algorithm, including the following steps:

[0009] (1) Receive the basic transportation information of passengers and goods through reservation, which should at least include the quantity of passengers and goods, the starting and ending points, and time requirements; determine the combined passenger and freight transportation strategy by obtaining the characteristics of different passenger and freight demands.

[0010] (2) Establish a route optimization model according to the combined passenger and freight transportation strategy, taking into account the freight transportation service while giving priority to meeting the passenger demand; this route optimization model includes two parts: constraint conditions and objective function. The objective function includes the total driving distance and the average in-transit time of passengers; on the premise of meeting all demands, factors such as vehicle capacity and time window constraints are mainly considered to seek the path plan with the lowest cost.

[0011] (3) Solve the route optimization model based on the adaptive large neighborhood search algorithm. Starting from the initial solution, break the current solution using the destruction operation, and then explore the new solution space using the repair operation; through continuous iteration, find the path plan that meets the goal of minimizing the cost or reach the preset number of iterations.

[0012] As a preferred solution of the present invention, the combined passenger and freight transportation strategy includes the following two:

[0013] Strategy 1: Consider the passenger and freight transportation at the same location as one demand, and set that each vehicle only visits each demand point once; when both passenger and freight transportation exist, the passenger and freight demands at this location must be served by the same vehicle.

[0014] Strategy 2: Separate the passenger and freight demands at the same location for processing. Different vehicles can access this location at most twice. When there are both passengers boarding and goods being loaded simultaneously, different vehicles provide services respectively.

[0015] As a preferred embodiment of the present invention, the constraint conditions include demand service constraints, service time window constraints, and vehicle capacity constraints.

[0016] As a preferred embodiment of the present invention, the demand service constraints specifically include:

[0017] ;

[0018] Among them, the symbols in each formula respectively refer to: k is the vehicle number; K is the set of vehicles; i, j are the nodes or locations providing demands or services; A is the set of paths between different nodes, (i, j) ∈ A; P is the set of passenger boarding / pick-up points; D is the set of passenger alighting / delivery points; n is the number of passenger boarding / pick-up points; refers to for any parameter value; x ijk is a 0-1 variable, which is 1 if vehicle k passes through the path (i, j), otherwise 0;

[0019] Formula (1) is used to ensure that all different passenger and freight transportation demands after screening can be met; Formulas (2)-(4) are flow balance constraints, which are used to ensure that the vehicle must depart from the origin transportation yard site and return to this point, and meet the requirements of arriving at and leaving each customer point; Formula (5) is a connection constraint, which is used to ensure that the starting point and the ending point of the same demand must be served by the same vehicle.

[0020] As a preferred embodiment of the present invention, the service time window constraints specifically include:

[0021] ;

[0022] Among them, the symbols in each formula respectively refer to: x ijk is the driving path of the vehicle between demand points i and j; s ik is the starting service time of vehicle k at demand point i; serv i is the service time; t ij is the driving time of the vehicle between demand points i and j; V is the set of all nodes or locations providing demands or services; a i is the earliest starting service time set for demand point i; b i is the latest starting service time set for demand point i;

[0023] Equation (6) ensures that after vehicle k finishes serving demand point i, the time to reach the next service point j through path x ijk shall not be greater than the service start time of that point; Equation (7) is the vehicle transportation priority constraint, that is, for each passenger and freight demand, the service order of its boarding / pickup demand must be earlier than that of its alighting / delivery demand; Equation (8) is the service time window constraint for the demand point, that is, the time s ik when vehicle k arrives at demand point i to start serving shall not be earlier than a i and shall not be later than b i .

[0024] As a preferred solution of the present invention, the vehicle capacity constraint specifically includes:

[0025] ;

[0026] Among them, the symbols in each formula respectively refer to: Q 1 ik is the total passenger load of vehicle k when leaving demand point i; Q 2 ik is the total cargo load of vehicle k when leaving demand point i; , are the passenger demands of demand points i and j; , are the cargo demands of demand points i and j; q is the rated vehicle capacity, that is, the maximum number of passengers and the standard number of cargo pieces that the vehicle is limited to load;

[0027] Equations (9) and (10) respectively ensure that after vehicle k finishes serving demand point j, it reaches the next service point j through path x ijk , and the passenger / cargo load when it leaves point j needs to be equal to the sum of the passenger / cargo load of the vehicle after leaving point i and the passenger / alighting and cargo loading / unloading amounts at point j; Equation (11) ensures that the total load of vehicle k at point i shall not be greater than the rated vehicle capacity.

[0028] As a preferred solution of the present invention, the objective function is specifically as follows:

[0029] ;

[0030] The symbols in the formula: k is the vehicle number; K is the set of vehicles; i and j are the nodes or locations providing demand or service; V is the set of all nodes or locations providing demand or service; t ij is the driving time of the vehicle between demand points i and j; x ijkis the driving route of the vehicle between demand points i and j; s jk and s ik is the time when vehicle k arrives at demand point j and starts serving at i; is the passenger demand at demand point i; T represents the set of passenger pick-up and drop-off point pairs; T + represents the pick-up point; α is the weight coefficient of the total vehicle driving time; β is the weight coefficient of the average passenger in-route time.

[0031] As a preferred solution of the present invention, the solution of the adaptive large neighborhood search algorithm is specifically carried out according to the following steps:

[0032] Step ⅰ: Input a feasible initial path plan and relevant parameters of the algorithm;

[0033] Step ⅱ: Define the destruction operator set and repair operator set corresponding to the destruction operation and repair operation;

[0034] Step ⅲ: Define the neighborhood size set;

[0035] Step ⅳ: Initialize the temperature and the weight of the operation;

[0036] Step ⅴ: Construct an initial solution;

[0037] Step ⅵ: Before reaching the maximum number of iterations, perform the following operations:

[0038] (ⅵ-1) Check the solution pool. If it is empty, add a new solution and the corresponding cost;

[0039] (ⅵ-2) Select a new solution from the solution pool to ensure that each iteration starts from a different initial solution;

[0040] (ⅵ-3) Roulette wheel selection operation;

[0041] (ⅵ-4) Select the neighborhood size and perform the destruction and repair operations;

[0042] (ⅵ-5) Calculate the cost of the new solution and calculate the acceptance probability by the simulated annealing method;

[0043] (ⅵ-6) Accept the new solution as the current solution according to the acceptance criterion;

[0044] (ⅵ-7) Update the optimal solution;

[0045] (ⅵ-8) Update the operation weight;

[0046] (ⅵ-9) Decrease the temperature to make the algorithm transition from the exploration stage to the convergence stage;

[0047] Step ⅶ: When the maximum number of iterations is reached, output the current optimal solution as the final path plan.

[0048] As a preferred embodiment of the present invention, in the adaptive large neighborhood search algorithm, the following nine destruction operators are included: Random Request Removal (RRE), Random Route Removal (RRO), Worst Distance Removal (WDR), Worst Time Removal (WTR), Worst Neighborhood Removal (WNR), Shaw Removal (SR), Proximity-based Removal (PR), Time-based Removal (TR), Request-based Removal (RR).

[0049] As a preferred embodiment of the present invention, in the adaptive large neighborhood search algorithm, the following three repair operators are included: Random Insertion (RAI), Greedy Insertion (GI), Regret Insertion (REI).

[0050] The present invention further provides a computer device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to execute any one of the foregoing passenger and freight co-transportation bus path planning methods based on the adaptive large neighborhood search algorithm.

[0051] The present invention also provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute any one of the foregoing passenger and freight co-transportation bus path planning methods based on the adaptive large neighborhood search algorithm.

[0052] Compared with the prior art, the technical effects of the present invention are:

[0053] 1. Aiming at the problem of uneven utilization of traffic resources caused by the imbalance between passenger and freight flows in the existing urban transportation system, the present invention proposes a reservation-based passenger and freight co-transportation bus path planning method based on the adaptive large neighborhood search algorithm, and provides an efficient vehicle path planning scheme for the passenger and freight transportation problems in the reservation-based bus system according to different transportation demands of passengers and goods (including passenger / freight volume, transportation origin and destination, and transportation time window, etc.). On the basis of comprehensively considering the principle of passenger priority, this scheme can achieve the supply-demand balance in the passenger and freight co-transportation mode, and provides a new perspective and solution for the efficient operation of the reservation-based bus system.

[0054] 2. Due to the particularity of public transportation services, the node visit count constraint conditions commonly used in traditional vehicle routing models do not prioritize passenger factors and are not applicable to the bus passenger and cargo co - transportation mode. The present invention innovatively proposes two passenger and cargo co - transportation strategies, which can be used for bus route planning at stations with different scales and different types of passenger and cargo demand, including stations for passenger boarding and cargo loading, passenger boarding and cargo unloading, passenger unloading and cargo loading, and passenger unloading and cargo unloading. Therefore, it reflects the potential of the passenger - cargo co - transportation mode in public transportation to improve transportation efficiency, and at the same time emphasizes the importance of giving priority to meeting passenger needs in this mode. When needed, Strategy 2 often achieves better results compared to Strategy 1.

[0055] 3. Based on the brand - new passenger and cargo co - transportation strategies, the present invention proposes a vehicle routing optimization model that comprehensively considers the co - transportation needs of passengers and cargo. This model takes giving priority to meeting passenger needs as the core and also takes into account cargo transportation services. In the objective function of the model, the key indicator of passenger in - transit time is specifically introduced, which is realized by the calculation of formula (12) to ensure the optimization of the passenger experience. In addition, the model also comprehensively considers key constraint conditions such as time windows and vehicle capacities, which are crucial for ensuring the practicality and feasibility of the model solution.

[0056] 4. The present invention constructs an improved adaptive large - neighborhood search algorithm, which effectively addresses the complexity problems in passenger and cargo co - transportation bus route planning by dynamically selecting and adjusting destruction and repair operations. The results of the test experiments show that this algorithm has obvious advantages in solving performance compared with the commercial solver Gurobi, mainly reflected in the solving time. Moreover, as the scale of the constraint conditions and decision variables in the model increases, this advantage becomes more significant. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the passenger - cargo co - transportation mode under different strategies of the present invention.

[0058] Figure 2 It is a schematic diagram of the execution process of the solving algorithm of the present invention.

[0059] Figure 3 It is a graph showing the change of weights of different operators of the algorithm of the present invention during the solving process. DETAILED DESCRIPTION OF THE INVENTION

[0060] The following combines the drawings to describe the detailed implementation manners of the present invention in detail.

[0061] First, the passenger and cargo co - transportation bus route planning method based on the adaptive large - neighborhood search algorithm described in the present invention is implemented based on the hardware devices of computer equipment and computer - readable storage media.

[0062] Among them, the computer device includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to execute the passenger-cargo co-transport bus route planning method based on the adaptive large neighborhood search algorithm. The computer-readable storage medium stores computer instructions for causing a computer to execute the passenger-cargo co-transport bus route planning method based on the adaptive large neighborhood search algorithm.

[0063] The passenger-cargo co-transport bus route planning method based on the adaptive large neighborhood search algorithm of the present invention mainly includes three key parts: determining the passenger-cargo co-transport strategy, establishing a route optimization model, and solving the search algorithm. Among them, the formulation of the passenger-cargo co-transport strategy is to divide different transport strategies according to the characteristics of different passenger / cargo demands; the establishment of the route optimization model aims to seek the lowest-cost route plan on the premise of meeting various conditions of the demands; and the solution of the search algorithm is to propose a calculation method suitable for the model structure in response to the computational complexity of large-scale mixed integer programming problems, so as to achieve the accuracy and rapidity of model solution.

[0064] 1. Receive the basic transportation information of passengers and goods in a reserved manner through the reservation system of the public transportation service system, which should at least include the quantity of passengers and goods, the starting and ending points, and the time requirements; determine the passenger-cargo co-transport strategy by obtaining the characteristics of different passenger-cargo demands.

[0065] There are two feasible solutions to the route planning problem of the passenger-cargo co-transport bus of the present invention. The first solution: regard the passenger transportation and freight transportation at the same location as one demand, and set that each vehicle only visits each demand point once. This means that when both passenger transportation and freight transportation exist, the passenger and freight demands at this location must be served by the same vehicle; the second solution: separately process the passenger and freight demands at the same location, and different vehicles can visit this location up to two times, that is, when passengers get on the vehicle and goods are loaded at the same time, they can be served by different vehicles respectively. These two situations correspond to Figure 1 Strategy 1 and Strategy 2 in. Usually, the traditional vehicle routing problem is limited to only being able to visit the same node once to ensure the reasonable utilization of resources and cost minimization. However, in the passenger-cargo co-transport scenario, this definition may lead to an increase in transportation costs. As can be seen from Figure 1 Strategy 1 is different from Strategy 2 in that it allows different vehicles to visit the same node twice.

[0066] The planning method described in the present invention can be used for different optimization objectives, such as minimum time cost, minimum cost, and maximum profit, etc. The path planning method provides suitable transportation solutions for users with different needs, meets the passenger / freight needs of customers, realizes the reasonable allocation of transportation resources, reduces costs and increases efficiency in the transportation system, and proposes a paradigm for the passenger and freight co - transportation mode of reservation - based buses.

[0067] In Table 1 and Table 2, the comparison of objective function values for two strategies is carried out respectively. Among them, in Table 1, it is based on the compact time window constraint conditions of ten demand point cases, and in Table 2, it is based on the loose time window constraint conditions of ten demand point cases.

[0068] Table 1

[0069] ;

[0070] Table 2

[0071] ;

[0072] It can be seen from Table 1 and Table 2 that Strategy 1 performs excellently under tight time windows: when the time window is tight, Strategy 1 performs better in reducing vehicle travel time and total cost. This is mainly because the time window constraint is relatively strict, resulting in an increase in costs due to the prioritized consideration of passenger demand, especially in the case of additional vehicle deployment.

[0073] In addition, Strategy 2 has more advantages under loose time windows: under loose time window conditions, Strategy 2 can better balance vehicle and passenger time and reduce the in - vehicle waiting time of passengers. This shows that when the time flexibility is large, Strategy 2 can more effectively schedule resources and optimize overall operations.

[0074] 2. Establish a path optimization model according to the passenger and freight co - transportation strategy; on the premise of meeting all demands, focus on considering factors such as vehicle capacity and time window constraints, and seek the path plan with the lowest cost.

[0075] The path optimization model of the present invention includes two parts: constraint conditions and objective function. Among them, the constraint conditions mainly include demand service constraints, service time window constraints, and vehicle capacity constraints. And the objective function mainly takes time cost as the optimization objective.

[0076] (1) Demand service constraints:

[0077] Since the transportation task is to meet the demand for the reservation requests agreed after being processed by the platform, it is necessary to ensure that the reservation-based bus can serve all the filtered demands, and the setting of Equation (1) meets this constraint. Equations (2)-(4) are classical flow balance constraints, which ensure that the vehicle must depart from and return to the originating transport yard site, and meet the requirements of arriving at and leaving each customer point. In addition, Equation (5) is a coupling constraint, which ensures that the starting point and the ending point of the same demand must be served by the same vehicle.

[0078] ;

[0079] (2) Service time window constraint:

[0080] To ensure that the vehicle can meet the demand within the desired time period, appropriate time constraints need to be added to the reservation-based bus. Among them, Equation (6) expresses that after vehicle k finishes serving demand point i, the time to reach the next service point j through path x ijk shall not be greater than the service start time of this point. Equation (7) is the vehicle transportation priority constraint, that is, for each passenger and freight demand, the service order of its boarding (pickup) demand must be earlier than that of its alighting (delivery) demand. Equation (8) is the service time window constraint for the demand point, that is, the time s ik when vehicle k arrives at demand point i to start serving, i shall not be earlier than a i .

[0081] ;

[0082] (3) Vehicle capacity constraint:

[0083] As a reservation-based travel tool, the reservation-based bus needs to ensure that there is enough personal space in its carriage (that is, it cannot exceed the rated capacity of the vehicle q , that is, the maximum number of passengers that can be loaded and the number of standard pieces of goods cannot exceed the vehicle's limited capacity). Therefore, in the model, it is necessary to limit the number of passengers and goods in the vehicle. Equations (9) and (10) respectively represent that after vehicle k finishes serving demand point i, through path x ijk it arrives at the next service point j, and the passenger / cargo loading amount when it leaves point j needs to be equal to the sum of the passenger / cargo loading amount after the vehicle leaves point i and the passenger / alighting (loading / unloading) amount at point j. Equation (11) then shows that the total loading amount of vehicle k at point i shall not be greater than the rated capacity of the vehicle.

[0084] ;

[0085] (4) Objective function:

[0086] Considering the realistic scenario where the transportation demands of passengers and goods coexist, the priority of passenger transportation in the bus system is greater than that of goods transportation. Therefore, in addition to including the total driving distance, the objective function in the present invention additionally adds the average in-transit time of passengers to improve the priority of passenger travel in the system. Thus, the objective function is as follows:

[0087] ;

[0088] It should be particularly noted that since the vehicle operation needs to satisfy the time window constraint, the item of the average in-transit time of passengers may further include the time for passengers to wait for the departure at the station.

[0089] The meanings of the parameters and variables in the formulas (1)-(12) of the present invention are specifically shown in Table 3.

[0090] Table 3:

[0091] ;

[0092] refers to for any parameter value (such as k∈K represents for any k belonging to K).

[0093] 3. Solve the path optimization model based on the adaptive large neighborhood search algorithm. Starting from the initial solution, use the destruction operation to break the current solution, and then use the repair operation to explore the new solution space; through continuous iteration, find a path plan that meets the cost minimization objective or reach the preset number of iterations.

[0094] The adaptive large neighborhood algorithm of the present invention belongs to a type of neighborhood search algorithm and can be used to solve the vehicle routing problem with pick-up and delivery and time windows. In recent years, the adaptive large neighborhood algorithm has achieved remarkable results in dealing with large-scale vehicle routing problems, and there have been many publicly available literatures documenting it. Its core mechanism starts from a set of initial solutions and iteratively optimizes them through a series of destruction and repair operations. Among them, the destruction operation breaks the current state by removing a part of the current solution, while the repair operation attempts to reconstruct the solution on the basis of the destruction in order to improve the quality of the solution. Through this iterative process, the adaptive large neighborhood algorithm can gradually approach the optimal or approximate optimal solution of the problem.

[0095] The solution process of the adaptive large neighborhood algorithm of the present invention is specifically as follows:

[0096] Step ⅰ: Input a feasible initial path plan x0 and the relevant parameters of the algorithm;

[0097] It includes: the maximum number of iterations, the maximum neighborhood range percentage maxPercentageNHB, the minimum neighborhood size minSizeNHB, etc.

[0098] Step ii: Define the destruction operator set and the repair operator set corresponding to the destruction operation and the repair operator;

[0099] Among them: The destruction operator set D is mainly responsible for removing a part of the path elements from the current solution, such as vehicle tasks and stations. The repair operator set R is mainly to re-add the path elements removed by the destruction operator to the solution to obtain a new feasible solution.

[0100] Step iii: Define the neighborhood size set.

[0101] Set the neighborhood size set Q = {q min , q1, q2, q3,..., q max}, which is used to control the number of tasks processed in each operation. Among them, q max = max{1, |maxPercentageNHB request quantity|}, which is the maximum size of the neighborhood; and q min is the minimum size of the neighborhood, which is determined by the parameter minSizeNHB. The neighborhood size is used to control the range of the destruction operation and the repair operation, so as to achieve different search depths.

[0102] Step iv: Initialize the temperature and the weight of the operation.

[0103] Set the initial temperature T and the cooling rate φ, which are used to control the acceptance criterion of simulated annealing. Initialize the weight w of each destruction and repair operation.

[0104] Step v: Construct the initial solution.

[0105] Generate the initial solution x0 and set it as the current optimal solution . The initial solution can be generated by random assignment or heuristic method to ensure its feasibility.

[0106] Step vi: Iterative process.

[0107] Before reaching the maximum number of iterations, perform the following operations:

[0108] (vi-1) Check whether the solution pool is empty:

[0109] If the solution pool is empty, call the regret value insertion method to generate a new solution x and the corresponding cost Cost x by maximizing the regret value of the insertion point, and add it to the solution pool.

[0110] (ⅵ-2) Select a new solution from the solution pool:

[0111] If the solution pool is not empty, select a solution x from it and remove it from the solution pool, which can ensure that each iteration starts from a different initial solution.

[0112] (ⅵ-3) Roulette wheel selection operation:

[0113] Perform the roulette wheel operation and randomly select a destruction operator d and a repair operator r according to the weights of the operations. This will make it more likely for better-performing operations to be selected.

[0114] (ⅵ-4) Select the neighborhood size and perform the destruction and repair operations:

[0115] Randomly select a neighborhood size q and perform the destruction and repair operations to generate a new solution x new . The destruction operation first removes some paths from the current solution, and then the repair operation reinserts the removed parts into the solution to ensure the feasibility of the solution.

[0116] (ⅵ-5) Calculate the cost of the new solution:

[0117] Calculate the new solution x new 's cost s new ;

[0118] Meanwhile, calculate the acceptance probability of the new solution x new by the simulated annealing method:

[0119] ;

[0120] Among them, P accept represents the acceptance probability; x new represents the new solution; Δ h(x new ,x) represents the cost difference between the new solution and the current solution; T refers to the current temperature.

[0121] (ⅵ-6) Acceptance criterion:

[0122] Generate a random number in the range of 0 to 1. If this random number is less than the acceptance probability P accept , then accept the new solution x new as the current solution x.

[0123] (ⅵ-7) Update the optimal solution

[0124] (ⅵ-8) Update the operation weights:

[0125] Update the weights w of the destruction and repair operations according to the performance of the operations (i.e., whether the generated solution is accepted or improves the current optimal solution). Generally, operations with good performance increase their weights, while operations with poor performance decrease their weights.

[0126] (ⅵ-9) Cooling:

[0127] After each iteration is completed, the temperature T is reduced by the formula T = φ T to decrease the acceptance probability of the solution gradually, enabling the algorithm to transition from the exploration phase to the convergence phase.

[0128] Step 7: Output the optimal solution ;

[0129] When the maximum number of iterations is reached, the algorithm outputs the current optimal solution , as the final path plan.

[0130] Compared with the conventional algorithm, the adaptive large neighborhood search algorithm used in the present invention has made significant improvements in the operation steps, including introducing an adaptive destruction and repair weight update mechanism, a flexible neighborhood size control method, a solution generation strategy based on regret value, the fusion of simulated annealing and ALNS, and a diversified initial solution selection strategy based on the solution pool. These improvements enhance the intelligence and efficiency of the algorithm, significantly strengthen the exploration ability of the solution space and the local optimization effect, thereby achieving higher solution efficiency, better solution quality, and stronger adaptability and robustness.

[0131] In the adaptive large neighborhood search algorithm used in the present invention, adaptive changes are mainly made to the construction of the destruction operator, including introducing a selection strategy based on task characteristics, dynamically adjusting the destruction range, a destruction operator integrating historical information, diversifying the types of destruction operators, an adaptive weight adjustment mechanism, and a dynamic decision-making combined with the solution state after destruction. These improvements significantly enhance the pertinence and flexibility of the destruction operator, enabling the algorithm to more efficiently explore the solution space, focus on key areas for optimization, and show significant advantages in terms of solution efficiency, adaptability, and robustness.

[0132] In view of the characteristics of the optimization model and the differences in passenger and freight demands, the destruction operators designed in this algorithm of the present invention include the following nine types:

[0133] (1) Random Request Removal (RRE): This operation randomly removes requests from the current solution. It is a basic operator commonly used in neighborhood search algorithms.

[0134] (2) Random Route Removal (RRO): This destruction operation is used to randomly select a route containing requests. By randomly screening invalid routes, it ensures that the selected route contains at least enough requests for the operation.

[0135] (3) Worst Distance Removal (WDR): The core idea of this operation is to remove from the current solution those distance factors that contribute the worst to the overall solution quality. These factors usually significantly increase the cost of the solution or affect the efficiency of the solution.

[0136] (4) Worst Time Removal (WTR): This is a destruction operation considering the worst time, used to identify time window constraint problems. By finding the request with the largest difference between the service start time and the start time of the time window, it can help optimize the time constraints in the route scheduling problem.

[0137] (5) Worst Neighbor Removal (WNR): This operation effectively identifies the request that has the greatest impact on the path cost based on the relative contributions of neighboring requests. By calculating the distance contributions of each request relative to its previous and next positions, the request with the highest percentage contribution to the overall path distance is identified. Subsequently, the entire path can be optimized by removing or re - optimizing this request.

[0138] (6) Shaw Removal (SR): This operation selects the next service request most relevant to the current location through the factors considered in the model (distance, time window, and demand in the present invention).

[0139] (7) Proximity - based Removal (PR): This method identifies the next request based on proximity, selecting the closest location to disrupt the current solution for further optimization.

[0140] (8) Time - based Removal (TR): This is to select the next most suitable request according to the time window difference. Its goal is to minimize the time window difference between requests to meet the time window constraints and optimize the service order in the scheduling problem.

[0141] (9) Request - based Removal (RR): This is a heuristic destruction operation based on demand difference, which can optimize the load balance in the path or scheduling problem by selecting the next location with the closest demand.

[0142] The repair operators used in the adaptive large - neighborhood search algorithm in the present invention include the following three:

[0143] (1) Random Insertion (RAI): This operation generates a new candidate solution by randomly re - introducing the elements removed in the destruction phase into the solution. This repair operation does not prioritize the best placement location but emphasizes randomness to enhance the diversity of the search space and promote a wider exploration of potential solutions.

[0144] (2) Greedy Insertion (GI): The goal of this operation is to improve the quality of the solution with each insertion, or at least prevent it from deteriorating. However, while this approach may be effective in the short term, it can also accelerate the algorithm's convergence to a local optimum, as it prioritizes immediate gains over long-term optimization potential.

[0145] (3) Regret Insertion (REI): The advantage of the regret insertion method is that it takes into account potential future losses, leading to more cautious decision-making. This approach helps prevent placing critical nodes in suboptimal positions, which could otherwise result in significant cost increases.

[0146] Figure 3 It is the weight change graph of different operators of the algorithm of the present invention during the solution process. Among them, (a) shows the weight change in the destruction operation, and (b) shows the weight change in the repair operation.

[0147] In order to demonstrate the technical advantages of the solution of the path optimization model based on the adaptive large neighborhood search algorithm in the present invention, comparative calculations are set for verification.

[0148] The verification conditions set are as follows: In the comparative experiment, the path optimization model is solved using the Gurobi solver and the adaptive large neighborhood search algorithm (ALNS) respectively. For Gurobi, its version 11.0 is called using Python, and the solution time limit is set to 5000 seconds. For ALNS, the specific parameter settings are: the minimum neighborhood size minSizeNHB is 1, the maximum neighborhood range percentage maxPercentageNHB is 5%, the temperature decrease rate φ of simulated annealing is 0.9994, the initial acceptance probability parameter is 0.03, the decay parameter for dynamically adjusting the operator weight is 0.30, and the number of iterations is 2000. Under the same example conditions, the performance differences between the two methods in solving the path optimization problem are evaluated through comparative experiments. The results of the comparative experiment are recorded in Table 4.

[0149] Table 4

[0150] ;

[0151] As can be seen from the content of Table 4, compared with the commercial solver Gurobi, the algorithm of the present invention based on the adaptive large neighborhood search has obvious advantages in solving performance, mainly reflected in the solution time. When the number of demand points doubles, the relatively saved computing time is more obvious, and the solution efficiency is significantly improved.

Claims

1. A passenger-freight bus route planning method based on an adaptive large neighborhood search algorithm, characterized in that: The following steps are involved: (1) Receive basic transportation information of passengers and cargo through reservation, which should at least include the number of passengers and cargo, starting and ending points, and time requirements; determine the passenger and cargo co-transportation strategy by obtaining the characteristics of different passenger and cargo demands; (2) A route optimization model is established based on the passenger-cargo co-transportation strategy, giving priority to satisfying passenger needs while taking into account cargo transportation services. The route optimization model includes two parts: constraints and an objective function, wherein the objective function includes the total driving distance and the average passenger travel time. Under the premise of satisfying all needs, the vehicle capacity and time window restrictions are considered to seek the route solution with the lowest cost. The constraints include demand service constraints, service time window constraints and vehicle capacity constraints; The demand service constraints specifically include: Formula (1) is used to ensure that all the selected passenger and cargo transportation needs can be met; Formulas (2)-(4) are flow balance constraints, which are used to ensure that the vehicle must start from the starting transportation site and return to that point, and meet the requirements of arriving at and leaving each customer point; Formula (5) is a connection constraint, which is used to ensure that the starting point and the end point of the same demand must be served by the same vehicle; The service time window constraints specifically include: Formula (6) ensures that vehicle k can reach the destination point i through path x after completing the service at the destination point i. ijk The time to arrive at the next service point j must not be greater than the service start time of that point; Formula (7) is the vehicle transportation priority constraint, that is, for each passenger and freight demand, the service order of its passenger pick-up demand must be earlier than the passenger drop-off / delivery demand; Formula (8) is the demand point service time window constraint, that is, the time s when vehicle k arrives at demand point i to start service ik , no earlier than a i , and cannot be later than b i ; The objective function is specifically as follows: The symbols in the above formulas refer to: k is the vehicle serial number; K is the vehicle set; i, j are nodes or locations that provide demand or service; A is the set of paths between different nodes, (i, j)∈A; P is the set of pick-up / delivery points; D is the set of drop-off / delivery points; V is the set of all nodes or locations that provide demand or service; n is the number of pick-up / delivery points; It means that for any parameter value; x ijk is the driving path of the vehicle between demand points i and j, specifically a 0-1 variable, which is 1 if vehicle k passes through the path (i, j), otherwise it is 0; serv i Service time;s jk 、s ik is the time when vehicle k arrives at demand point j and starts serving i; t ij is the travel time of the vehicle between demand points i and j; a i Set the earliest service start time for demand point i; b i Set the latest service start time for demand point i; d 1 i is the passenger demand at demand point i; T represents the set of passenger boarding and alighting point pairs; T + represents the boarding point; α is the weight coefficient of the total vehicle travel time; β is the weight coefficient of the average passenger travel time; (3) The path optimization model is solved based on an adaptive large neighborhood search algorithm. Starting from the initial solution, the current solution is broken by a destruction operation, and then a new solution space is explored by a repair operation. Through continuous iteration, a path solution that meets the cost minimization goal is found, or a preset number of iterations is reached.

2. The method according to claim 1, characterized in that The passenger and cargo co-transportation strategies include the following two: Strategy 1: Treat passenger and freight transport at the same location as one demand, and set each vehicle to visit each demand point only once; when passenger and freight transport exist at the same time, the passenger and freight transport demands at the location must be served by the same vehicle; Strategy 2: Separate the passenger and freight demands at the same location. Different vehicles can visit the location at most twice. When passengers are boarding and cargo is being loaded at the same time, different vehicles will provide services for each location.

3. The method according to claim 1, characterized in that The vehicle capacity constraints specifically include: The symbols in each formula refer to: Q 1 ik is the total passenger load of vehicle k when it leaves demand point i; Q 2 ik is the total cargo load of vehicle k when it leaves demand point i; d 1 i d 1 j is the passenger demand at demand points i and j; d 2 i d 2 j is the cargo demand at demand points i and j; q is the rated capacity of the vehicle, i.e., the maximum number of passengers and standard cargo pieces that the vehicle can carry; Formula (9) and Formula (10) respectively ensure that vehicle k can reach the destination through path x after completing the service of demand point j. ijk When arriving at the next service point j, its passenger / cargo load when leaving point j needs to be equal to the sum of the passenger / cargo load after leaving point i and the passenger / cargo loading and unloading volume at point j; formula (11) ensures that the total load of vehicle k at point i shall not be greater than the rated capacity of the vehicle.

4. The method according to claim 1, characterized in that: The method of solving the adaptive large neighborhood search algorithm specifically includes the following steps: Step i: Input a feasible initial path plan and relevant parameters of the algorithm; Step II: define the destruction operator set and the repair operator set corresponding to the destruction operation and the repair operation; Step iii: define the neighborhood size set; Step iv: initialize the temperature and operation weights; Step V: Construct the initial solution; Step ⅵ: Before reaching the maximum number of iterations, perform the following operations: (ⅵ-1) Check the solution pool. If it is empty, add a new solution and the corresponding cost; (ⅵ-2) Select a new solution from the solution pool to ensure that each iteration starts from a different initial solution; (ⅵ-3) roulette wheel selection operation; (ⅵ-4) Select the neighborhood size and perform damage repair operations; (ⅵ-5) Calculate the cost of the new solution and calculate the acceptance probability by simulated annealing method; (ⅵ-6) accepting the new solution as the current solution according to the acceptance criteria; (ⅵ-7) Update the optimal solution; (ⅵ-8) Update operation weights; (ⅵ-9) Cooling down, so that the algorithm transitions from the exploration stage to the convergence stage; Step ⅶ: When the maximum number of iterations is reached, output the current optimal solution as the final path plan.

5. The method according to claim 1, characterized in that The adaptive large neighborhood search algorithm includes the following nine destruction operators: random request removal, random route removal, worst distance removal, worst time removal, worst neighborhood removal, Shaw removal, proximity-based removal, time-based removal, and request-based removal.

6. The method according to claim 1, characterized in that The adaptive large neighborhood search algorithm includes the following three repair operators: random insertion, greedy insertion, and regretful insertion.

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