Optimizing supply chain configuration with low carbon emission

We study a new supply chain configuration problem to optimize the amount of carbon emission in the context of a service guarantee modelling framework, called supply chain configuration problem with low carbon emission (SCCP-LCE). A novel feature of our addressed problem is the explicit consideration of carbon emission cap and trading price in the supply chain configuration setting with operating capacity. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model, and optimally solved by a custom designed dynamic programming algorithm. A case study and computational experiment are performed to examine the behaviour of optimal SCCP-LCE configurations, and the effects of key input parameters: carbon emission cap, trading price, and operating capacity. Our results suggest that government-imposed carbon emission policies, in terms of emission cap and trading price, have significant impacts and interactive effects on the optimal supply chain configuration and performance, including the safety stock cost and carbon emission cost. Our model and methodology offer a new analytical framework to prescribe data-driven decision support for both firms and governmental/environmental agencies to control carbon emission, while achieving optimal business and social benefits.