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Constraint programming for project-driven manufacturing

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  • Banaszak, Z.A.
  • Zaremba, M.B.
  • Muszynski, W.

Abstract

Project-driven manufacturing, based on the make-to-order or the build-to-order principle and predominant in small and medium-size enterprises (SMEs), calls for an efficient solution of large combinatorial problems, especially in such areas as task scheduling or resource management. This paper addresses the problem of finding a computationally effective approach to scheduling a new project subject to constraints imposed by a multi-project environment. A constraint programming (CP) modeling framework is discussed in the context of an efficient decomposition of the constraint satisfaction problem (CSP) and the evaluation of strategies for pruning the search tree. The proposed approach is illustrated through examples of its application to the evaluation of a new production order.

Suggested Citation

  • Banaszak, Z.A. & Zaremba, M.B. & Muszynski, W., 2009. "Constraint programming for project-driven manufacturing," International Journal of Production Economics, Elsevier, vol. 120(2), pages 463-475, August.
    • Handle: RePEc:eee:proeco:v:120:y:2009:i:2:p:463-475
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    References listed on IDEAS

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    1. Zbigniew A. Banaszak, 2006. "CP-Based Decision Support for Project Driven Manufacturing," International Series in Operations Research & Management Science, in: Joanna Józefowska & Jan Weglarz (ed.), Perspectives in Modern Project Scheduling, chapter 0, pages 409-437, Springer.
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    6. Józefowska, J. & Zimniak, A., 2008. "Optimization tool for short-term production planning and scheduling," International Journal of Production Economics, Elsevier, vol. 112(1), pages 109-120, March.
    7. Lova, Antonio & Maroto, Concepcion & Tormos, Pilar, 2000. "A multicriteria heuristic method to improve resource allocation in multiproject scheduling," European Journal of Operational Research, Elsevier, vol. 127(2), pages 408-424, December.
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    Cited by:

    1. Relich Marcin, 2016. "Computational Intelligence for Estimating Cost of New Product Development," Foundations of Management, Sciendo, vol. 8(1), pages 21-34, January.
    2. Ramírez Palencia, Alberto E. & Mejía Delgadillo, Gonzalo E., 2012. "A computer application for a bus body assembly line using Genetic Algorithms," International Journal of Production Economics, Elsevier, vol. 140(1), pages 431-438.
    3. Mohamed Kriouich & Hicham Sarir, 2024. "Artificial Intelligence Application in Production Scheduling Problem Systematic Literature Review: Bibliometric Analysis, Research Trend, and Knowledge Taxonomy," SN Operations Research Forum, Springer, vol. 5(2), pages 1-24, June.

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