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.
