Multi-objective optimization for a green forward-reverse meat supply chain network design under uncertainty: Utilizing waste and by-products

The essential requirement for food stands as a pivotal human need, exerting significant ecological impact from production to consumption. Meat, a key dietary component, offers essential nutrients vital for human health. This paper presents a bi-objective two-stage stochastic optimization model for a green forward-reverse meat supply chain network design, addressing both economic and environmental concerns throughout the chain. In the forward flow, the supply chain manages various meat products consist of fresh, processed, and frozen meat products, ensuring their eco-efficient production and distribution. Meanwhile, in the reverse flow, waste and by-products generated during the production process are repurposed and reused. The study promotes environmental sustainability by repurposing waste, utilizing by-products, and minimizing carbon emissions. The proposed model is solved using popular exact ε-constraint method for smaller instances and Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Strength Pareto Evolutionary Algorithm 2 (SPEA2) meta-heuristic algorithms are employed for larger instances. SPEA2 outperforms both MOPSO and NASG-II, demonstrating less average gap that is 0.38% and 0.76%, respectively. Additionally, the findings reveals that an average 2.5% reduction in environmental impacts associated with an average 5% decrease in profit. The noteworthy outcomes of the research empower managers to navigate the implications of fluctuating demand effectively. Moreover, it is crucial to underscore the effects of conversion rates, particularly those associated with the manufacturing process. An excessively high conversion rate can negatively impact profitability and worsen environmental issues. Conversely, lowering the conversion rate can enhance profitability and mitigate environmental impacts.