Computational solution of capacity planning models under uncertainty
Parallel Computing - Special issue on parallel computing in economics, finance and decision-making
Integrated Scheduling of Production and Distribution Operations
Management Science
Expert Systems with Applications: An International Journal
Efficient Production-Distribution System Design
Management Science
Integrating multi-product production and distribution in newspaper logistics
Computers and Operations Research
Computers and Operations Research
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Collaborative production-distribution planning for semiconductor production turnkey service
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
A profit-maximizing supply chain network design model with demand choice flexibility
Operations Research Letters
Supply chain redesign for resilience using simulation
Computers and Industrial Engineering
Designing decision support systems for value-based management: A survey and an architecture
Decision Support Systems
| Hi-index | 0.01 |
This paper considers a supply chain design problem for a new market opportunity with uncertain demand in an agile manufacturing setting. We consider the integrated optimization of logistics and production costs associated with the supply chain members. These problems routinely occur 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 decision 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 path 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.