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Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach

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Abstract

Disruptions rarely occur in supply chains, but their negative financial and technical impacts make the recovery process very slow. In this paper, we propose a capacitated supply chain network design (SCND) model under random disruptions both in facility and transportation, which seeks to determine the optimal location and types of distribution centers (DC) and also the best plan to assign customers to each opened DC. Unlike other studies in the extent literature, we use new concepts of reliability to model the strategic behavior of DCs and customers at the network: (1) Failure of DCs might be partial, i.e. a disrupted DC might still be able to serve with a portion of its initial capacity (2) The lost capacity of a disrupted DC shall be provided from a non-disrupted one and (3) The lost capacity fraction of a disrupted DC depends on its initial investment amount in the design phase.

In order to solve the proposed model optimally, a modified version of Benders’ Decomposition (BD) is applied. This modification tackles the difficulties of the BD’s master problem (MP), which ultimately improves the solution time of BD significantly. The classical BD approach results in low density cuts in some cases, Covering Cut Bundle (CCB) generation addresses this issue by generating a bundle of cuts instead of a single cut, which could cover more decision variables of the MP. Our inspiration to improve the CCB generation led to a new method, namely Maximum Density Cut (MDC) generation. MDC is based on the observation that in some cases CCB generation is cumbersome to solve in order to cover all decision variables of the MP rather than to cover part of them. Thus the MDC method generates a cut to cover the remaining decision variables which are not covered by CCB. Numerical experiments demonstrate the practicability of the proposed model to be promising in the SCND area, also the modified BD approach decreases the number of BD iterations and improves the CPU times, significantly.

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Notes

  1. Except Peng et al. (2011).

  2. Lim et al. (2010) defined that disruptions occur at unreliable DCs, and reliable DCs are the outsourcing ones which are secured against disruption and assumed to be uncapacitated.

  3. For example, consider a company which intends to supply its customers located in three different area zones. This must be planned in three particular modes: truck, rail and air in which the experience shows that many accidents (disruptions) have happened so far. Thus, this transportation strategy seems excessively risky. One proper safe strategy is to get assistance from outsources (similar to reliable DCs in Lim et al. 2010) in order to prevent the massive cost that company may be charged with; this is what we call safe transportation mode.

  4. If safe transportation mode in the primary assignment is used, there will be no need to use the secondary assignment, because the safe mode is also safe in disruption situation.

  5. The CCB method which is presented in this subsection is for the case of optimally cut, for feasibility cut refer to Saharidis et al. (2010).

  6. It should be noted that the literature of SCND under random disruption risks which utilize scenario-based approach (see Table 1, e.g. Peng et al. 2011 and Snyder and Daskin 2006), generate up to 65 scenarios for their instances.

  7. Hereafter we compile our investigation for medium and large sizes 80 and 120 by means of instances P24 and P28 in Table 2, respectively.

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Authors and Affiliations

  1. Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

    Nader Azad & Hamid Davoudpour

  2. Kathikas Institute of Research & Technology, Columbus, MO, USA

    Georgios K. D. Saharidis

  3. University of Thessaly, Leoforos Athinon, Pedion Areos, 38834, Volos, Greece

    Georgios K. D. Saharidis

  4. School of Industrial Engineering, Islamic Azad University–South Tehran Branch, Tehran, Iran

    Hooman Malekly

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

    Seyed Alireza Yektamaram

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  1. Nader Azad
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Correspondence to Georgios K. D. Saharidis.

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Azad, N., Saharidis, G.K.D., Davoudpour, H. et al. Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Ann Oper Res 210, 125–163 (2013). https://doi.org/10.1007/s10479-012-1146-x

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