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A Genetic Algorithm for 1,5 Dimensional Assortment Problems with Multiple Objectives

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Developments in Applied Artificial Intelligence (IEA/AIE 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2718))

Abstract

In most studies on cutting stock problems, it is assumed that the sizes of stock materials are known and the problem is to find the best cutting pattern combinations. However, the solution efficiency of the problem depends strongly on the size of stock materials. In this study, a two-step approach is developed for a 1,5 dimensional assortment problem with multiple objectives. Cutting patterns are derived by implicit enumeration in the first step. The second step is used to determine the optimum sizes of stock materials by applying a genetic algorithm. The object is to find stock material sizes that minimize the total trim loss and also the variety of stock materials. Specialized crossover operator is developed to maintain the feasibility of the chromosomes. A real-life problem with 41 alternative stock materials, 289 order pieces and 1001 patterns is solved.

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Authors and Affiliations

  1. Department of Industrial Engineering, Osmangazi University, 26030, Bademlik Eskisehir, Turkey

    Tuğba Saraç & Müjgan Sağır Özdemir

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  1. Tuğba Saraç
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  2. Müjgan Sağır Özdemir
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Editors and Affiliations

  1. Dept. of Computer Science, Loughborough University, Loughborough, LE11 3TU, England

    Paul W. H. Chung  & Chris Hinde  & 

  2. Dept. of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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Saraç, T., Özdemir, M.S. (2003). A Genetic Algorithm for 1,5 Dimensional Assortment Problems with Multiple Objectives. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_5

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  • DOI: https://doi.org/10.1007/3-540-45034-3_5

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