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Order Acceptance Policy for Make-To-Order Supply Chain

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Data Management and Analysis

Part of the book series: Studies in Big Data ((SBD,volume 65))

Abstract

This paper explores a dynamic order acceptance policy of firms in a decentralized supply chain (SC) to improve the profits of an SC by using the machine learning method. The dynamic arrival and due date orders in SC were divided into three types according to the profit that the SC can obtain. Two echelons of the SC, in which a supplier that cooperate with other firms in SC will receive orders in and out of the SC, are employed in this study. Capturing four order characteristics in make-to-order SC, we examine whether this model can make a higher profit by using a simulation model of Support Vector Machines (SVMs) rather than First Come First Serve (FCFS) and Artificial Neural Network (ANN). The experimental results indicate that SVMs is an efficient tool for firms in a dynamic SC to improve the performance of the SC. A numerical example is used to validate the results.

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Acknowledgments

This research was financially supported by a project grant from the Discovery Grant from the Natural Science and Engineering Research Council of Canada (NSERC) and the Humanities and Social Sciences of Ministry of Education Planning Fund (No: 16YJC630085) in China.

Author information

Authors and Affiliations

  1. School of Management, Shenyang University of Technology, Shenyang, Liaoning, People’s Republic of China

    Jun Ma

  2. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada

    Jun Ma & Yiliu Tu

  3. School of Mechanical Engineering, Yangtze University, Jingzhou, Hubei, People’s Republic of China

    Ding Feng

Authors
  1. Jun Ma
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  2. Yiliu Tu
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  3. Ding Feng
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Corresponding author

Correspondence to Yiliu Tu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Department of Computer Engineering Istanbul Medipol University Istanbul, Turkey, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Mohammad Moshirpour

  3. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

    Behrouz Far

Appendix

Appendix

Table 4 A partial list of test data
Full size table
Table 5 A partial list of training data
Full size table

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Ma, J., Tu, Y., Feng, D. (2020). Order Acceptance Policy for Make-To-Order Supply Chain. In: Alhajj, R., Moshirpour, M., Far, B. (eds) Data Management and Analysis. Studies in Big Data, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-030-32587-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-32587-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32586-2

  • Online ISBN: 978-3-030-32587-9

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