DEVELOPMENT OF A SOLUTION APPROACH FOR STOCHASTIC SUPPLY CHAIN NETWORK DESIGN

摘要

This paper proposes a solution strategy for solving supply chain network design problems under uncertainty. The uncertainties are mostly found in the tactical stage because most tactical parameters are not fully known when the strategic decisions have to be made. These parameters will become known with more certainty after the supply chain is in operation. The main uncertain parameters are the customer demands, capacity of the facilities and the operational costs. A sampling strategy with an accelerated Benders' decomposition algorithm will be integrated to quickly compute solutions of stochastic supply chain network design problems. The surrogate constraints method will be utilized to acceleration of the decomposition algorithm. In this method, the main constraints of the master problem in Benders' decomposition will be replaced by the strongest surrogate constraint. A computational study on randomly generated data sets is presented to highlight the efficiency of the proposed solution strategy.