Tackling Uncertain Performances of Multiple Stakeholders in the Design and Optimization of Decentralized Supply Chains

Abstract

Supply chains are normally managed in a decentralized way by multiple stakeholders pursuing distinct objectives. However, most existing supply chain studies rely on centralized models and neglect the uncertain behaviors of stakeholders in the decision-making process. In this work, a novel game theory based stochastic model is proposed that integrates two-stage stochastic programming with a single-leader-multiple-follower Stackelberg game scheme. The aim is to address the optimization problem of decentralized supply chains considering multiple stakeholders under uncertainty. The resulting model is formulated as a stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover’s linearization method. To illustrate the applicability of proposed modeling framework, a case study of a large-scale shale gas supply chain is presented, which demonstrates the advantages of the proposed modeling framework and efficiency of the solution algorithm.