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Artificial neural network models for lot-sizing problem: a case study

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Abstract

Lot-sizing is one of the most difficult problems in production planning. The main purpose of this study is to propose a new lot-sizing based on artificial neural network (ANN), which may lead to a better performance than commonly used lot-sizing heuristics (SM, EOQ, PPB, LUC, and LTC). The data obtained are the results of years 2004 thru 2009 for 186 different types of stock items from the 2nd Air Supply and Maintenance Centre Command, a state-funded factory in Kayseri, Turkey. Factual data were applied under the coverage of the study, and the system from which the data have been obtained is still in live and active status. In the study, the purchasing costs, holding costs, and set-up costs were taken into consideration. These data were obtained from the administration data system of the enterprise. The solutions of this lot-sizing heuristics were found by WinQSB software accordingly. The ANN was constituted by using the NeuroSolutions software. The criterion of deviation from the optimum solution and the criterion of percentage of times obtaining the optimum order pattern were taken into account for the comparison purposes. The performance values of 400 ANNs were compared to lot-sizing methods. MS Excel and Visual Basic Macro were utilized for all calculations applied after this stage. The results showed that the proposed ANN-based method outperformed all lot-sizing methods taken into account in this study.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Ercan Şenyiğit

  2. Ground System Department, 2nd Air Supply and Maintenance Centre Command, 38050, Melikgazi, Kayseri, Turkey

    Uğur Atici

Authors
  1. Ercan Şenyiğit
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  2. Uğur Atici
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Correspondence to Ercan Şenyiğit.

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Şenyiğit, E., Atici, U. Artificial neural network models for lot-sizing problem: a case study. Neural Comput & Applic 22, 1039–1047 (2013). https://doi.org/10.1007/s00521-012-0863-z

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  • DOI: https://doi.org/10.1007/s00521-012-0863-z

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