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Genetic algorithms in supply chain management: A critical analysis of the literature

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Abstract

Genetic algorithms (GAs) are perhaps the oldest and most frequently used search techniques for dealing with complex and intricate real-life problems that are otherwise difficult to solve by the traditional methods. The present article provides an extensive literature review of the application of GA on supply chain management (SCM). SCM consists of several intricate processes and each process is equally important for maintaining a successful supply chain. In this paper, eight processes (where each process has a set of sub-processes) as given by Council of SCM Professionals (CSCMF) are considered. The idea is to review the application of GA on these aspects and to provide the readers a detailed study in this area. The authors have considered more than 220 papers covering a span of nearly two decades for this study. The analysis is shown in detail with the help of graphs and tables. It is expected that such an extensive study will encourage and motivate the fellow researchers working in related area; to identify the gaps and to come up with innovative ideas.

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Acknowledgments

We acknowledge DST, grant number INT/FRG/DAAD/P-251/2015 for the partial financial support provided.

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  1. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Sunil Kumar Jauhar & Millie Pant

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Correspondence to Sunil Kumar Jauhar.

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Jauhar, S.K., Pant, M. Genetic algorithms in supply chain management: A critical analysis of the literature. Sādhanā 41, 993–1017 (2016). https://doi.org/10.1007/s12046-016-0538-z

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  • DOI: https://doi.org/10.1007/s12046-016-0538-z

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