Research On Vehicle Routing Problem In Logistics Distribution

With the development of economy, distribution systems have become increasingly complex, and more and more important influence on economicy, so researchers pay attention to them at home and abroad. Vehicle routing problem(VRP) is an important content in supply chain field. Selecting appropriate vehicle routing method can accelerate response to customers demand, raise service quality, improve satifsfactoriness of customers with supply chain and reduce service running cost. Therefore, vehicle routing problem is always a research hot spot in field of operational research, management and computer applications etc. The main content of this dissertation is as follows:1) The existing research on VRP focuses mainly on deterministic demand and the heuristic algorithm that applied is just single. In this dissertation vehicle routing problem with stochastic demand is studied, and a transient chaotic neural network (TCNN) is proposed and developed by introducing a time-varying parameter to control chaotic situation. By making full use of coexistence of randomicity with deterministic property in TCNN, this dissertation proposes a novel solution to a combinatorial optimization problem. For a class of vehicle routing problems with stochastic demands, a model is first set up to solve the problem and the solution is then compared with those obtained by the existing Hopfield neural network and simulated annealing approach. Results from case studies show that the proposed algorithm can avoid getting stuck in local minima and has better convergence property as well as time property.2) The vehicle routing problem with stochastic customers and demands is studied too. The standard simulated algorithm has been applied to vehicle routing problem, and it has the common defects of slow convergence and easily being trapped into local minima. In this dissertation, a new stochastic approach called the simulated annealing genetic algorithm is proposed to solve stochastic vehicle routing problems and the solution is then compared with that from simulated algorithm. Results from case studies show that the proposed algorithm can avoid getting stuck in local minima and has better convergence property and find the optimal or near-optimal solution effectively as well as fast convergence property.3) The existing research for vehicle routing problem with time windows(VRPTW). To overcome some drawbacks of VRPTW, two goals of number vehicle numbers and vehicle running costs are simultaneously considered in mathematical model. The standard Genetic Algorithm is applied into vehicle routing problem with common defects of early convergence and easily falling into local minima. Therefore, a new stochastic approach called the genetic simulated annealing algorithm (GSAG) is proposed to solve vehicle routing problems and the solution is then compared with that from simulated algorithm. By making full use of the locally searching powerful capability of the simulated annealing methon, GSAG avoids effectively the common defects of early convergence. Results from case studies show that the proposed algorithm is an efficient method for vehicle routing problem.4) For hot issues in reverse logistics, the research on vehicle routing problem with simultaneous delivery and pick-up is carried out. Then, the neighborhood structure of a tabu search algorithm is designed and used to solve some problems. Results from case studies show that the proposed algorithm is an efficient method.