A decision support system of green inventory-routing problem

Abstract

Purpose \n \n \n \n \nThe purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm. \n \n \n \n \nDesign/methodology/approach \n \n \n \n \nA GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution. \n \n \n \n \nFindings \n \n \n \n \nSeveral examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level. \n \n \n \n \nOriginality/value \n \n \n \n \nThe most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.