Opportunistic supply chain design in agile manufacturing: Models and heuristics
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The characteristics of today's competitive environment, such as the speed with which products are designed, manufactured, and distributed, and the need for higher production efficiency and lower operational cost, are forcing companies to search for innovative ways to do business. The concept of agile manufacturing has been proposed in response to these challenges for companies. This dissertation presents formulations and methodologies for a series of problems to form a virtual organization based on the integrated optimization of logistics and production costs along the supply chain. Three problems are considered: (i) a robust supply chain design under an uncertain demand; (ii) a multi-echelon supply chain network design; (iii) a supply chain network design under a supplier capacity disruption risk. The first problem routinely occurs in a wide variety of industries including semiconductor manufacturing, multi-tier automotive supply chains, and consumer appliances to name a few. There are two types of decisions variables: binary variables for selection of companies to form the supply chain and continuous variables associated with production planning. A scenario approach is used to handle the uncertainty of demand. The formulation is a robust optimization model with three components in the objective function: expected total costs, cost variability due to demand uncertainty, and expected penalty for demand unmet at the end of the planning horizon. The increase of computational time with the numbers of echelons and members per echelon necessitates a heuristic. A heuristic based on a k -shortest paths algorithm is developed by using a surrogate distance to denote the effectiveness of each member in the supply chain. The heuristic can find an optimal solution very quickly in some small- and medium-size cases. For large problems, a “good” solution with a small gap relative to our lower bound is obtained in a short computational time. In the second problem, we consider a supply chain network design problem with multiple echelons and multiple periods with consideration of the bill-of-materials for a single end product under a situation where multiple customers have heavy demands. Decisions in this problem are similar with those in the first problem. Due to the computational complexity of the problem, a solution approach based on Lagrangean relaxation is developed. The Lagrangean relaxation problem provides a lower bound, while a feasible solution is generated by a greedy backward heuristic based on the solution of subproblems at each iteration. Computational results indicate the high quality of feasible solutions provided by the solution approach. In the third problem, we consider a similar problem taking into account the supplier disruption risk. Corporations and researchers have recognized a variety of risks associated with supply chain, such as disruption arising due to natural disasters and labor strikes. Although the possibility of these risks occurring is small, the consequence can be catastrophic for a company. Two states of capacity at a company site in a period are considered: UP and DOWN. A large-scale two-stage stochastic program is developed with a huge number of scenarios that represent the capacity states for all unreliable companies in all periods during a finite planning horizon. The number of scenario increases rapidly with the numbers of unreliable companies and time periods in the planning horizon, which motivates to develop an efficient solution methodology. A series of analysis are performed followed by some policies suggested based on analysis.