Grid-based Multi-objective Evolutionary Algorithm For Solving Cold Chain Logistics Vehicle Routing Problem

With the development of economy and society,the diet structure has become more and more abundant,and people’s demand for stable high frequency of fresh products has made the distribution and quality of fresh products more and more important.The perishable and easy-to-loss characteristics of fresh produce determine the severe test it faces in the distribution process.In order to ensure the quality of fresh products in the distribution process and reduce the loss of fresh products,the cold chain logistics logistics industry should emerge.The problems studied in this paper consider the problem of vehicle routing of multi-objective fresh-keeping products with cold-chain logistics with time window constraints in the distribution process of fresh products,including the following aspects:(1)Established a multi-objective optimization model with the lowest total distribution cost,the smallest vehicle operating time difference and the highest customer satisfaction.The total distribution cost includes transportation cost,cargo damage cost and penalty cost.The difference of vehicle operation time takes into account the difference of working time of different vehicle drivers,which avoids the decay and deterioration of the fresh goods in the compartment caused by the long-time operation of the vehicle.The balance of working hours of vehicle drivers;customer satisfaction is determined by the relationship between the arrival time of the vehicle and the time window specified by the customer.(2)A multi-objective artificial bee colony algorithm based on adaptive grid is designed.The grid is used to divide the target solution space,so that the Pareto optimal solution can be located and evaluated in the grid to ensure the diversity of the solution set.Through the adaptive updating of the grid boundary and the number of grids,the search direction of the artificial bee colony algorithm is guided and the algorithm search efficiency is improved.Secondly,a new information sharing mechanism between the employed bees and unemployed bees is designed for the artificial bee colony algorithm,which balances the contradiction between the diversity and convergence of the Pareto optimal solution set.The unemployed bees position update method is improved,so that the unemployed bees can balance the global search and the local search,and improve the accuracy of the understanding set.Taking the standard test cases ZDT and DTLZ as examples,the comparison with other multi-objective evolutionary algorithms on the evaluation indexes IGD and Spread confirms that The feasibility of the algorithm for solving continuous multi-objective problems.(3)Based on the improved artificial bee colony algorithm based on grid,the matrix-based coding method is used to assign real weights to all vehicles and customer points.According to the problem characteristics,the working hours of the vehicle drivers can be balanced.Heuristic information.The comparison experiments show that the multi-objective evolutionary algorithm in this paper can also obtain a better quality solution set for solving discrete problems,which provides a basis for decision makers.(4)Using a test as a practical example to verify the rationality of the model.Under the premise of meeting the customer’s time window requirements and vehicle capacity restrictions,we will design a distribution route for the distribution center that is more in line with the needs of the enterprise.It is proved that the multi-objective artificial bee colony algorithm based on grid can solve the superiority of a set of high quality solution sets.