A hybrid L-shaped method to solve a bi-objective stochastic transshipment-enabled inventory routing problem

Recently, ‘greenness’ has become a very much needed condition in the transportation industry. In this study we develop a ‘green’, transshipment-enabled model for the Inventory Routing Problem (IRP), in a many-to-one distribution network where demand for each product is realistically assumed to be uncertain. The proposed framework is a bi-objective stochastic programming model. The first objective function aims to minimize the expected value of the supply chain costs including inevitable shortage costs. The second objective function aims to minimize the total quantity of the greenhouse gas (GHG) emission produced by the vehicles and disposed products. We introduce a very practical innovative application of transshipment option to control transportation cost, reduce GHG emissions and absorb the uncertainty. In order to solve the proposed model an efficient hybrid algorithm combining L-shaped method (a sort of decomposition approach for stochastic optimization) and compromise programming (a well-known approach for multi-objective optimization) is proposed. The results show that how companies can make a reasonable tradeoff between the cost and environmental concerns and emphasize the role of transshipment option as a lever to improve both economic and environmental performance and absorb the demand fluctuations.