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Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm

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Abstract

Demand plays a vital role in designing every closed-loop supply chain network in today’s world. The flow of materials and commodities in the opposite direction of the standard supply chain is inevitable. In this way, this study addresses a new multi-echelon multi-period closed-loop supply chain network to minimize the total costs of the network. The echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a Mixed Integer Linear Programming (MILP) model considering factories’ vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, for the first time, the demand for the products is estimated using an Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the shortage that may happen in the whole supply chain network. Conversely, for solving the proposed model, the GAMS software is utilized in small and medium-size problems, and also, genetic algorithm is applied for large-size problems to obtain initial results of the model. Numerical results show that the proposed model is closer to the actual situation and could reach a reasonable solution in terms of service level, shortage, etc. Accordingly, sensitivity analysis is performed on essential parameters to show the performance of the proposed model. Finally, some potential topics are discussed for future study.

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Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

    Shahab Safaei

  2. Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman

    Peiman Ghasemi

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, WA, 98071, USA

    Fariba Goodarzian

  4. Department of Transportation, Logistics, and Finance, North Dakota State University (NDSU), 58105-6050, Fargo, ND, USA

    Mohsen Momenitabar

Authors
  1. Shahab Safaei
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  2. Peiman Ghasemi
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  3. Fariba Goodarzian
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  4. Mohsen Momenitabar
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Contributions

Shahab Safaei: conceptualization. Peiman Ghasemi: mathematical model, software, investigation, methodology. Fariba Goodarzian: data curation, writing—reviewing and editing. Mohsen Momenitabar: data curation, writing—reviewing and editing.

Corresponding author

Correspondence to Peiman Ghasemi.

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue and has not self-plagiarism. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Furthermore, we hereby transfer the unlimited rights of publication of the above-mentioned paper in whole to “Environmental science and pollution research.” The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

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Safaei, S., Ghasemi, P., Goodarzian, F. et al. Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm. Environ Sci Pollut Res 29, 79754–79768 (2022). https://doi.org/10.1007/s11356-022-19341-5

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  • DOI: https://doi.org/10.1007/s11356-022-19341-5

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