ENHANCED NEIGHBORHOOD STRUCTURE FOR MULTI PERIODS INVENTORY ROUTING PROBLEM MODEL WITH TIME VARYING DEMAND

The problem addressed in this study is a many-to-one distribution network consisting of an assembly plant and man y geographically dispersed suppliers where each supplier supplies distinct product to the assembly plant. It is assumed that the product is ready for collection when the vehicle arrives and that all demand must be met without backlogging. The inventory holding cost at the assembly plant is assumed to be product specific and a fleet of capacitated homogeneous vehicles, housed at a depot, transport products from the suppliers to meet the demand specified by the assembly plant in each period. The problem is formulated as a mixed integer programming problem and will be solved to get the lower bound for each problem considered. We propose a solution method based on the Variable Neighborhood Search (VNS) where several heuristics are incorporated at various stages of the algorithm. The algorithm constructs the neighborhood using a giant tour and clusters are built using Dijkstra’s algorithm for finding the shortest path. The algorithm embeds interchange heuristics as local search. The algorithm is run on several problems from the literature and it is observed the algorithm produces competitive results in a relatively shorter time. The lower bounds fail to give any significant results for large problems.