Title: A robust stochastic programming approach for agile and responsive logistics under operational and disruption risks

Authors: Reza Babazadeh; Jafar Razmi

Addresses: Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-45632, Tehran, Iran. ' Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-45632, Tehran, Iran

Abstract: High turbulences and fluctuations of today's competitive business environments have put organisations under pressure to move toward efficient competitive strategy and seek competent approach to design their supply chain network under uncertainty. At the same line, this paper presents an efficient mixed integer linear programming (MILP) model that is able to consider the key characteristics of agile supply chain, which is the best competitive strategy for high turbulent environments, such as direct shipments, outsourcing, different transportation modes, discount, alliance (process and information integration) between opened facilities and maximum waiting time of customers for deliveries. Additionally, the robust stochastic programming approach is applied to handle both operational and disruption risks of the agile supply chain network. Computational results show that the robust model is capable to result efficient solutions under scenario realisations with low cost variability contrary to deterministic model.

Keywords: robust optimisation; supply chain networks; network design; SCM; supply chain management; uncertainty; outsourcing; stochastic programming; agile logistics; responsive logistics; operational risks; disruption risks; competitiveness; business environments; competitive strategies; MILP; mixed integer linear programming; agile supply chains; high turbulent environments; direct shipments; transportation modes; discounts; alliances; process integration; information integration; opened facilities; maximum waiting times; customers; deliveries; scenario realisations; low cost variability; deterministic models; logistics systems; logistics management.

DOI: 10.1504/IJLSM.2012.050158

International Journal of Logistics Systems and Management, 2012 Vol.13 No.4, pp.458 - 482

Published online: 10 Dec 2014 *

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