Computational intelligence techniques to optimize a constrained distribution operations

Abstract

With the rapid development of Information Technology (IT) in recent past, number of unsolved real-world complex problems benefited immensely since solutions could be found within considerable time period. This is mainly due to the computational intelligence techniques which can deliver a reasonably good result within a shorter time period by performing complex calculations with numbers of iterations. Distribution operation is a common problem in the area of supply chain management which got the attention for many years. However, up to now only simplified versions of distribution operations such as Vehicle Routing Problem (VRP), Travelling Salesman Problem (TSP) have being considered by many researches. Some of the researchers adopted with heuristic approaches such as Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS) and etc. with number of assumptions. However, the standard problems are far away from the real world problem. Furthermore, when the problem size (or scale) increases, the computational time to finds the optimal results increase exponentially, hence these problems are categorized as +P-hard problems in mathematical terms. To bridge the gap between standard problems in distribution and the real world problems, in this research, the standard VRP problem is extended to multi depot environment with split delivery option and tries to investigate the applicability of Simulated Annealing (SA) and Tabu Search (TS) to find solutions to this complex problem within considerable time frame. +umbers of simulation studies are carried out with both of these techniques and results revealed that both these techniques can be adapted to complex Multi Depot Vehicle Routing Problem with Time Windows and Split Delivery (MDVRPTWSD) problem and TS out performances in solution quality.