Abstract
This chapter addresses the problem of optimal lot-sizing and sequencing of manufacturing items under uncertainties. A state of the art is presented and a case study is developed. For the case study, the following factors are taken into account: processing and set-up times, random machine breakdowns and rejects. The goal is to minimize the cost and maximize the probability of a desired output for a given period. For the considered case, first, a mathematical model of the problem and some heuristic and metaheuristics approaches are discussed. The first approach decomposes the initial task in two sub-problems: travelling salesman and knapsack problems. An iterative optimization procedure is proposed, based on this decomposition. Then, a genetic algorithm is suggested and detailed. Several test and simulation results are reported.
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Keywords
- Supply Chain Management
- Economic Order Quantity
- Defective Item
- Random Yield
- Economic Production Quantity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Dolgui, A., Grimaud, F., Shchamialiova, K. (2010). Supply Chain Management Under Uncertainties: Lot-sizing and Scheduling Rules. In: Benyoucef, L., Grabot, B. (eds) Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management. Springer Series in Advanced Manufacturing. Springer, London. https://doi.org/10.1007/978-1-84996-119-6_7
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