Formulation and optimization of a three echelon multi-objective supply chain network design model under scenario based uncertainty

Abstract

Nowadays, rapid economic changes and competitive pressure in the global market make companies pay more attention on supply chain topics. The company whose supply chain network structure is more appropriate has higher competitive advantage. Propounding the supply chain because of its effect on factors of operational efficiency, such as inventory, response and lead time, specific attention is focused on how to create a distribution network. As nowadays living conditions have changed due to increasing world changes, mutually, situations have changed where supply chains are confronted with and influenced by them. The manager is confronted with more unknown conditions and new risks. Customers' demands have been more uncertain and the lead time on their services is very effective. The demand variety can be recognized as one of the important sources of uncertainty in a supply chain. Moreover, operating cost and capacity of the facilities can also be uncertain those can vary depending on the situations. This research is presenting a new multi-objective optimization model for supply chain network designs problem. For the first time, a novel mathematical model is presented considering cost and transportation time minimization as well as customer service level maximization under scenario based uncertainty with the existence of several alternatives of vehicles to transport the products between facilities, and routing of vehicles from plants to distribution centers (DCs) and DCs to customer in a stochastic supply chain system, simultaneously. This problem is formulated as a tri-objective mixed-integer linear programming model. The objective of the thesis includes determining the most appropriate transportation channel in terms of choosing suitable vehicles and routes for the second and third echelon of designed supply chain network. All are done in such a way that network wide cost and transportation time are minimized and customer service level are maximized. To solve the model a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) has been used in Matlab 2013a software after careful analysis of different evolutionary algorithms. This new optimization model is tested on a hypothetical data example, where a multi-stage supply chain design problem is optimized. The results show that the model is presenting the trade-off among different objective functions. Furthermore, the way the model is formulated permits the supply chain to maintain a reasonable higher level of costs, in moments of reducing transportation time and maximizing service level for the customers. Finally, by using the new solving method, the model generated a quality set of Pareto-optimal solutions, which can be used for the decision-maker to evaluate different options for the supply chain network design.