Abstract—In this paper, we propose a stochastic
programming approach to manage supply chain disruptions of
an enterprise with an emphasis on demand and disruptions
uncertainty. The supply chain considered here is multi-product,
multi-agent in nature. However, the model considers purchasing
cost, inventory cost and emergency ordering cost. Decisions such
as ordering quantities in pre-disruptions and post-disruptions
situation are taken into consideration. On the other hand,
quality and delivery performance requirements are also
included in the proposed analytical framework. We use Monte
Carlo sampling approach for the purpose of sampling for a
given probability distribution of stochastic parameters. In
addition, we consider a disruptions planning case study and
apply Benders decomposition (BD)/L-shaped algorithm to solve
the model. The model is coded on GAMS 24.1.3 and run by
CPLEX (12.5.1.0) and DECIS solver. We minimize total cost
that includes first stage cost and second stage disruptions
scenario cost. Several test instances with different disruptions
scenarios are considered. We then compare the total costs under
several disruptions scenarios. We hope that the model could be
used as an effective tool to analyze and decide on supply chain
disruptions planning and management of an enterprise thus it
could contribute in continuity of manufacturing/business
operations and therefore help in building a resilient supply
chain.
Index Terms—Stochastic programming, supply chain
disruptions, benders decomposition, Monte Carlo simulation.
The authors are with the Department of Architecture, Civil Engineering
and Industrial Management Engineering in Nagoya Institute of Technology,
Nagoya, Japan (e-mail: syed.mithun@gmail.com, nakade@nitech.ac.jp).
[PDF]
Cite:A Stochastic Programming Approach to Supply Chain Disruptions Planning and Management, "S. M. Ali and K. Nakade," International Journal of Materials, Mechanics and Manufacturing vol. 2, no. 2, pp. 139-145, 2014.
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