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Supply Chain Management Under Uncertainties: Lot-sizing and Scheduling Rules

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Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management

Part of the book series: Springer Series in Advanced Manufacturing ((SSAM))

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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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Authors and Affiliations

  1. Centre for Industrial Engineering and Computer Science, Ecole des Mines de Saint Etienne, 158 cours Fauriel, 42023, Saint Etienne cedex 2, France

    A. Dolgui, F. Grimaud & K. Shchamialiova

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  1. A. Dolgui
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  2. F. Grimaud
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  3. K. Shchamialiova
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Correspondence to A. Dolgui .

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Editors and Affiliations

  1. COSTEAM/LGIPM ISGMP Bat. A, INRIA Nancy-Grand Est, Ile du Saulcy, 57000, Metz, France

    Lyes Benyoucef

  2. LGP/ENIT, 47, Avenue d’Azereix, 65016, Tarbes CX, France

    Bernard Grabot

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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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  • DOI: https://doi.org/10.1007/978-1-84996-119-6_7

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  • Print ISBN: 978-1-84996-118-9

  • Online ISBN: 978-1-84996-119-6

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