LAUSR

Abstract Manufacturing systems are one of the largest emitters of carbon emissions. Governments enforce environmental regulations to control and reduce such emissions. Cap-and-trade is one of the most efficient and widely-used regulations in which a tradable initial allowance is given to each emitter to sell or buy in the carbon market. In this study, we address the trading and production planning problem for unreliable manufacturing systems under cap-and-trade regulation. Regarding the carbon price which randomly changes in the market, it is challenging and complex for companies to know how to determine their production rate and trading quantity to take advantage of the carbon market. We aim to develop a new joint production and trading control policy for unreliable manufacturing systems considering the stochastic and dynamic context. Our objective is to guide the managers to jointly determine when to buy/sell allowances or increase/decrease the production rate to minimize the total cost and reduce carbon emissions of the system. The policy is specifically designed for the cap-and-trade regulation to minimize total costs consisting of emission, backlog, inventory, trading, and transaction costs. Simulation modelling, experimental design, and response surface methodology are implemented to optimize parameters of our proposed control policy. Finally, sensitivity analysis as well as comparative study are carried out to show how our proposed joint policy brings significant cost saving and emissions reduction in comparison with existing policies adapted from the literature.