TY - JOUR
T1 - Investigating risk and robustness measures for supply chain network design under demand uncertainty
T2 - A case study of glass supply chain
AU - Govindan, Kannan
AU - Fattahi, Mohammad
PY - 2017/1/1
Y1 - 2017/1/1
N2 - This paper addresses a multi-stage and multi-period supply chain network design problem in which multiple commodities should be produced through different subsequent levels of manufacturing processes. The problem is formulated as a two-stage stochastic program under stochastic and highly time-variable demands. To deal with the stochastic demands, a Latin Hypercube Sampling method is applied to generate a fan of scenarios and then, a backward scenario reduction technique reduces the number of scenarios. Weighted mean-risk objectives by using different risk measures and minimax objective are examined to obtain risk-averse and robust solutions, respectively. Computational results are presented on a real-life case study to illustrate the applicability of the proposed approaches. To compare these different decision-making situations, a simulation approach is used. Furthermore, by several test problems, the performance of the stochastic model is investigated and the scenario generation method is evaluated in terms of in-sample and out-of-sample stability. Finally, sensitivity analysis on main parameters of the problem is performed to drive some managerial insights.
AB - This paper addresses a multi-stage and multi-period supply chain network design problem in which multiple commodities should be produced through different subsequent levels of manufacturing processes. The problem is formulated as a two-stage stochastic program under stochastic and highly time-variable demands. To deal with the stochastic demands, a Latin Hypercube Sampling method is applied to generate a fan of scenarios and then, a backward scenario reduction technique reduces the number of scenarios. Weighted mean-risk objectives by using different risk measures and minimax objective are examined to obtain risk-averse and robust solutions, respectively. Computational results are presented on a real-life case study to illustrate the applicability of the proposed approaches. To compare these different decision-making situations, a simulation approach is used. Furthermore, by several test problems, the performance of the stochastic model is investigated and the scenario generation method is evaluated in terms of in-sample and out-of-sample stability. Finally, sensitivity analysis on main parameters of the problem is performed to drive some managerial insights.
KW - Supply chain network design
KW - Stochastic programming
KW - Scenario reduction
KW - Solution׳s robustness
KW - Risk consideration
KW - Simulation
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84950290578&partnerID=MN8TOARS
U2 - 10.1016/j.ijpe.2015.09.033
DO - 10.1016/j.ijpe.2015.09.033
M3 - Article
VL - 183
SP - 680
EP - 699
JO - International Journal of Production Economics
JF - International Journal of Production Economics
SN - 0925-5273
ER -