Eco-friendly and Cost-effective Resource Allocation in Multi-factory Settings: A Possibilistic Approach to Integrated Supply Chain Planning

Studies on multi-factory resource allocation within integrated supply chain (SC) planning (MRA-ISCP) are notably scarce, particularly in relation to supply and production eco-friendly capacity portfolios. The challenge of determining these portfolios is exacerbated under fluctuating demand and further complicated by the inherent imprecision of input parameters. These complex yet practical scenarios require focused attention to ensure SC profitability and sustainability. This research addresses this significant gap in MRA-ISCP by developing a multi-objective possibilistic model to manage fluctuating demand and imprecise input parameters. The model incorporates realistic constraints and utilizes a triangular possibility distribution function to minimize expected imprecise costs, reduce the risk of higher costs, and enhance collective environmental sustainability. A key aspect of this study is the integration of VIKORSORT to establish an eco-friendly supply portfolio, balancing cost efficiency with sustainability. Validation through a case study in the electric transmission industry demonstrates the model’s effectiveness, showing a substantial improvement in decision-maker satisfaction from 0.61 to 0.92, alongside a significant reduction of risk of achieving higher costs. The model outperforms traditional deterministic approaches by capturing data imprecision and providing robust, cost-effective solutions under varying conditions. The study also includes a comparative analysis, evaluating the performance of the proposed approach against a well-known metaheuristic method. Sensitivity analysis further reveals its adaptability, particularly in optimizing satisfaction levels while minimizing risks. This study offers crucial managerial insights into cost-risk mitigation, eco-friendly supplier decisions, and the strategic management of multi-factory capacity and supply portfolios, making it highly relevant for imprecise SC environments.