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A branch and efficiency algorithm for the optimal design of supply chain networks

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  • Konstantinos Petridis

    (University of Macedonia)

  • Prasanta Kumar Dey

    (Aston University)

  • Ali Emrouznejad

    (Aston University)

Abstract

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples.

Suggested Citation

  • Konstantinos Petridis & Prasanta Kumar Dey & Ali Emrouznejad, 2017. "A branch and efficiency algorithm for the optimal design of supply chain networks," Annals of Operations Research, Springer, vol. 253(1), pages 545-571, June.
    • Handle: RePEc:spr:annopr:v:253:y:2017:i:1:d:10.1007_s10479-016-2268-3
    DOI: 10.1007/s10479-016-2268-3
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    Cited by:

    1. Georgios Digkas & Konstantinos Petridis & Alexander Chatzigeorgiou & Emmanouil Stiakakis & Ali Emrouznejad, 2020. "Measuring Spatio-temporal Efficiency: An R Implementation for Time-Evolving Units," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 843-864, December.
    2. Natawat Jatuphatwarodom & Dylan F. Jones & Djamila Ouelhadj, 2018. "A mixed-model multi-objective analysis of strategic supply chain decision support in the Thai silk industry," Annals of Operations Research, Springer, vol. 267(1), pages 221-247, August.
    3. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Mahmoudi, Reza & Shetab-Boushehri, Seyyed-Nader & Hejazi, Seyed Reza & Emrouznejad, Ali & Rajabi, Parisa, 2019. "A hybrid egalitarian bargaining game-DEA and sustainable network design approach for evaluating, selecting and scheduling urban road construction projects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 161-183.
    5. Gafner, Andreas & Loske, Dominic & Klumpp, Matthias, 2021. "Efficiency measurement of grocery retail warehouses with DEA," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 317-348, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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