Integrated supply chain network design through multi-objective optimization considering transient demand and cost uncertainty

Abstract

A supply chain is a set of facilities, supplies, customers, products and methods of controlling inventory, purchasing, and distribution. The chain links suppliers and customers, beginning with the production of raw material by a supplier, and ending with the consumption of a product by the customer. In a supply chain, the flow of goods between a supplier and customer passes through several stages, and each stage may consist of many facilities. In recent years, the supply chain network (SCN) design problem has been gaining importance due to increasing competitiveness introduced by the market globalization. Firms are obliged to maintain high customer service levels while at the same timethey are forced to reduce cost and maintain profit margins. 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. The manager is confronted with more unknown conditions and new risks. Customers' demands have been more uncertain and various, 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 design.For the first time, a novel mathematical model is presented considering cost and network flow time minimization as well as demand satisfaction level maximizationconsidering transient uncertainty. This problem is formulated as a four objective mixed-integer linear programming model with multiple products where not only demand but also associated costs are assumed to be uncertain. The uncertainty is incorporated as scenarioon the basis of historical data.The objective of the thesis is to provide strategic and tactical level decision on production rate, material flow rate and inventory level of different echelons.The objectives are achieved in such a way that total supply chain cost and networkflow time are minimized and demand satisfaction level and volume flexibility 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 example.The results show that the model is presenting the trade-off among different objective vii functions. Furthermore, the way of the model is formulated, it permits the supply chain to maintain a reasonable higher level of costs, in moments of reducing network flow time and maximizing demand satisfaction level for the customers. Again it also shows a trade-offin between demand satisfaction level and volume flexibility which increases with the decrease of demand satisfaction level. Finally, by using the new solving method, the model is able to generate a quality set of Pareto-optimal solutions, which can be used for the decision-maker to evaluate different options for the supply chain designon the basis of priority.