Cap-and-trade regulation is an effective mechanism to control carbon emissions. The optimization problem for a two-stage supply chain consisting of a manufacturer and a retailer under cap-and-trade regulation was investigated in this paper. Consumers’ low-carbon awareness level was considered in the decision models. Optimal decision policies, corresponding emissions, and profits were calculated for decentralized and centralized decision-making modes. Under a decentralized mode, the two-stage supply-chain optimization problem was formulated as a Stackelberg game model, where the manufacturer and retailer were the leader and follower, respectively. The manufacturer decides the emission-reduction levels per product unit and the retailer decides the retail price per unit product. The optimal decisions are derived using the reverse-solution method. By contrast, the two-stage supply-chain optimization problem under a decentralized mode was formulated as a single-level optimization model. The nonlinear model is handled by KKT optimality conditions. The influence of the regulation parameters (caps and carbon prices) and consumers’ low-carbon awareness on the optimal decision policies, the corresponding emissions, and profits is discussed in detail. A comparison between the two modes implies that the decentralized mode is dominated by the centralized mode in terms of profit and emissions. In order to provoke the decision makers under decentralized modes to make the decisions under the decentralized mode, a profit-sharing contract was designed. This study shows that higher consumer low-carbon awareness and carbon prices can improve the manufacturer-decision flexibility when there exists a profit-sharing contract. Finally, numerical experiments confirmed the analytical results.
A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem
