Study On Multi-objective Optimization Of Collaborative Distribution Vehicle Routing Problem

The vehicle routing problem is a research hot topic in operations,graph theory, networkanalysis, applied mathematics, computer application and transportation research fields in therecent20years.It has been proved to be a NP-Hard problems completely with constraints incombinatorial optimization, it is difficult to use conventional method, domestic and foreignscholars attempt to study through the use of intelligent algorithm, such as genetic algorithm,ant colony algorithm, tabu search optimization,and so on.In this thesis, on the basis of the research status at home and abroad, a multi-objectiveoptimization model of synergy of collaborative vehicle routing problem is established. It usesthe multi-objective evolutionary algorithm based on subregion to solve the CVRP, realizes theapplication and software simulation of CVRP based on the subregional multi-objectiveevolutionary algorithm, and verifies the effectiveness of the model and the feasibility of thealgorithm. This thesis main research content:First of all, on the basis of the analysis and study of the collaborative distribution and thevehicle routing problem, the thesis illustrates the collaborative vehicle routing problem is amulti-objective optimization problem. On the basis of that, this thesis studies thecollaborative vehicle routing problem is not only a multi-depot collaborative distributionproblem but also a multi-objective optimization problem. Based on this, the article studies thesynergy distribution vehicle routing problem is not only a multi-center collaborativedistribution problem but also a multi-objective collaborative distribution problem. The articleextracts the shortest path and the service quality optimization as the research targets,collaborative optimization on it, establishes the mathematical model vehicle of themulti-depot collaborative vehicle route problem with car delivery cost minimum as objectivefunction.The second, according to the model s characteristics and complexity, this thesis analysesthe characteristics of genetic algorithm, then uses genetic algorithm that based on thesubregion and minimax strategy to solve the multi-objective optimization problem. Thealgorithm structures a kind of encoding, which can avail to generate feasible solution, andimprove the operation efficiency of the algorithm. Then the thesis verified the establishedmodel which can effectively solve CVRP by an example. The thesis returns a non dominatedsolution set rather than a non dominated solution of single through the adoption ofmulti-objective evolutionary algorithm, that can provide powerful decision support fordecision makers.