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Solving an Intractable Stochastic Partial Backordering Inventory Problem Using Machine Learning

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Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics (ICETCE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 985))

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Abstract

This paper addresses the intractability of order crossover in a partial backordering inventory problem. Here, the Artificial Neural Network (ANN), which is a machine leaning algorithm is used to solve a stochastic inventory problem. The results for examining order crossover with the back-propagation ANN shows notable reduction in inventory cost in comparison to linear regression method. A numerical study is taken to demonstrate the findings. This paper further draws insight on effectiveness of machine learning in comparison to regression.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, SKIT, Jaipur, India

    Nidhi Srivastav

  2. Department of Mechanical Engineering, SKIT, Jaipur, India

    Achin Srivastav

Authors
  1. Nidhi Srivastav
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  2. Achin Srivastav
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Corresponding author

Correspondence to Nidhi Srivastav .

Editor information

Editors and Affiliations

  1. College of Engineering, Iowa State University, Ames, IA, USA

    Arun K. Somani

  2. National University of Singapore, Singapore, Singapore

    Seeram Ramakrishna

  3. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Anil Chaudhary

  4. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Chothmal Choudhary

  5. Swami Keshvanand Institute of Technology Management and Gramothan, Jaipur, India

    Basant Agarwal

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Srivastav, N., Srivastav, A. (2019). Solving an Intractable Stochastic Partial Backordering Inventory Problem Using Machine Learning. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_20

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  • DOI: https://doi.org/10.1007/978-981-13-8300-7_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8299-4

  • Online ISBN: 978-981-13-8300-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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