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Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm

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Abstract

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Seyed Hamid Reza Pasandideh

  2. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran

    Seyed Taghi Akhavan Niaki

  3. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammad Hemmati Far

Authors
  1. Seyed Hamid Reza Pasandideh
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  2. Seyed Taghi Akhavan Niaki
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  3. Mohammad Hemmati Far
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Corresponding author

Correspondence to Seyed Taghi Akhavan Niaki.

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Pasandideh, S.H.R., Niaki, S.T.A. & Hemmati Far, M. Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm. Int J Adv Manuf Technol 71, 365–376 (2014). https://doi.org/10.1007/s00170-013-5476-x

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  • DOI: https://doi.org/10.1007/s00170-013-5476-x

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