Improved Ant Colony Algorithm To Solve Complex Green Periodic Vehicle Routing Problem

Periodic vehicle routing problem is an important branch of classical vehicle routing problem.It mainly optimizes the customer pattern and distribution path in the distribution cycle to meet the multi frequency service needs of customers.At present,environmental and energy problems are becoming increasingly serious,every country has more and more exacting requirements for environmental protection.Therefore,the green periodic vehicle routing problem(GPVRP),which takes fuel consumption and carbon emissions into consideration,has very important theoretical value and practical significance.In this paper,the complex green periodic vehicle routing problem and the solution to the related problems are studied:(1)On the basis of the classical PVRP problem,the GPVRP mathematical model with the goal of minimizing the total cost is established by further considering the green energy consumption index and selecting the comprehensive fuel consumption calculation model.At the same time,a hybrid ACO(HACO)is proposed to solve the problem.In order to improve the search efficiency of the algorithm,the adaptive update strategy of volatile factor is introduced in the pheromone update phase of HACO,and the local search of three local strategies is integrated.Finally,simulation results show that HACO is an effective algorithm for GPVRP.(2)On the basis of GPVRP,a multi-objective green periodic vehicle routing problem with time windows(GPVRPTW)mathematical model is established by considering the time window constraints.The optimization objective is to minimize the total transportation time and total consumption at the same time,and an improved ant colony algorithm(IACO)is proposed to solve the problem.Firstly,IACO uses three-dimensional probability matrix to record the high-quality solution information of vehicle routing subproblems with different delivery dates,and designs a pheromone updating mechanism based on information entropy for reasonable learning and accumulation,so as to enhance the guidance of global search.Secondly,in the local search section,a variable neighborhood search including five neighborhood operations is added to make IACO search the solution space more deeply.Finally,the effectiveness of IACO is proved by simulation experiments of various scale examples.(3)On the basis of GPVRPTW,a multi-objective green multi depot periodic vehicle routing problem(GMDPVRP)mathematical model is established by considering the multi-depot constraints,The optimization objective is to minimize the total transportation time and total cost at the same time,and an improved ant colony algorithm combined with clustering decomposition(IACO?CD)is proposed to solve the problem.First,IACO?CD uses binary encoding and decoding rules and improved k-means clustering method to decompose GMDPVRP into a series of GVRPTWs subproblems,which can better realize the decomposition optimization of the problem.Secondly,IACO?CD is proposed solves each GVRPTW subproblem to obtain the solution of the original problem.IACO?CD designs subobjective method and simulated annealing mechanism to control the updating of Pareto solution set,and uses Levy flight formula to dynamically control volatilization factor,so that pheromone can be updated adaptively to enhance the global search ability of the algorithm.At the same time,a three-stage variable neighborhood search based on five neighborhood operations is designed to enhance the local search capability of IACO?CD.Finally,simulation and experimental analysis verify the reliability of IACO?CD.