From Manufacturing Scheduling to Supply Chain Coordination: The Control of Complexity and Uncertainty

With time-based competition and rapid technology advancements, effective manufacturing scheduling and supply chain coordination are critical to quickly respond to changing market conditions. These problems, however, are difficult in view of inherent complexity and various uncertainties involved. A manufacturing scheduling problem is first formulated within the job shop context with order arrivals, processing times, due dates, and part priorities as a separable optimization problem. A solution methodology that combines Lagrangian relaxation, dynamic programming, and heuristics is developed. Method improvements to consider uncertainties and to effectively solve large problems are highlighted. The approach is then extended to coordinate companies in a decentralized supply chain. By relaxing cross-company constraints, the model is decomposed into company-wise subproblems, and a nested optimization structure is developed based on the job shop scheduling results. Coordination is performed through the iterative updating of cross- company prices without accessing other companies' private information or intruding their decision-making authorities.