Integrated production–distribution planning in two-echelon systems: a resilience view. Issue 4 (16th February 2017)

• Record Type: Journal Article
• Authors: Khalili, Seyed Mohammad; Jolai, Fariborz; Torabi, Seyed Ali
• Is Part Of: International journal of production research. Volume 55:Issue 4(2017)
• Page Start: 1040
• Page End: 1064
• Publication Date: 2017-02-16
• Subjects: production and distribution planning -- supply chain resilience -- two-stage scenario-based stochastic programming -- possibilistic programming -- conditional value at risk; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1213446
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage's objective function is considered by utilising the conditional value at risk ( CVaR ) measure. The validation and applicability of the proposed model are examined through several numerical experiments.