A multi-objective supply chain model for reducing carbon emissions and food losses in a multi-period mixed product environment

Environmental sustainability is increasingly crucial in supply chain management due to global warming. This presents challenges in optimizing supply chains for profitability while addressing carbon emissions and food loss. Cold chain technologies, while reducing food loss, often increase carbon emissions due to energy use. Strategies like market differentiation and reprocessing can mitigate food losses. This study develops a multi-period supply chain model that accounts for product quality differentiation, specifically focusing on mixed products comprising both processed and fresh food items. The model employs multi-objective mixed-integer linear programming to simultaneously optimize total profit and carbon dioxide emissions. It incorporates the aspect of diminishing food quality and addresses practical supply chain issues like batch production, inventory management, and remanufacturing. The study shows that two multi-objective planning approaches, LP-metrics and the ε-constraint method, can successfully reduce carbon emissions, albeit with a slight decline in profits. The ε-constraint method proves to be the most effective, achieving a 27.1 % emissions reduction with a 20.16 % profit decrease. In contrast, LP-metrics achieve a 20.32 % reduction in emissions with a smaller profit decrease of 12.34 %. Additionally, the research examines how carbon pricing policies affect supply chain planning. The results show that setting the carbon tax at 100 NTD leads to a modest decrease in carbon emissions but comes with a significant reduction in profit. Evaluating the outcomes of two assessment methods suggests that the recommended carbon pricing falls between 150 and 225 NTD. However, it may still be necessary to implement other complementary measures.