Abstract Waste generation across various categories such as municipal waste, industrial byproducts, medical discards continues to grow in both quantity and complexity due to population growth, urbanization, and advancements in technology. Improper waste management (WM), driven by human activities, is a major contributor to environmental pollution, making effective waste handling a critical priority for all nations. This study focuses on managing waste by minimizing total transportation cost, restricting carbon emission, and mitigating impacts on public health through a multi-objective optimization (MOO) framework. To address uncertainties in supply and demand fluctuations, the model incorporates type-2 intuitionistic fuzzy sets. Global criterion method (GCM) is applied to obtain Pareto-optimal solutions that balance multiple conflicting objectives. To validate the practicality of the proposed approach, a real-world case study based on Addis Ababa, Ethiopia, is presented, alongside several generated benchmark instances. Furthermore, the performance of GCM is compared with two existing MOO techniques to demonstrate its effectiveness in generating solutions. The study also includes a comparative analysis, sensitivity analysis, and managerial insights to support decision-making.

Abstract Automation is rapidly transforming warehouse operations by improving productivity, safety, and long-term cost performance. However, transitioning from manpower-based systems to automated solutions remains a complex decision-making challenge due to uncertainty and competing performance criteria. To address this challenge, this study applies an integrated fuzzy multi-criteria decision-making (MCDM) framework to compare automated and manpower-based warehousing systems. Four established fuzzy MCDM methods, Fuzzy EDAS, Fuzzy TOPSIS, Fuzzy AHP, and Fuzzy VIKOR, are integrated within a single evaluation framework to provide a more comprehensive perspective than any individual method alone. A further methodological contribution is a targeted sensitivity analysis applied to Fuzzy EDAS and Fuzzy VIKOR, the two methods most responsive to criterion-weight variation. This analysis tests ranking robustness under alternative weighting scenarios and strengthens result credibility. Six core evaluation criteria, productivity, safety, flexibility, initial investment, annual expense, and error rate, are employed, reflecting both operational performance and financial considerations. The results consistently indicate that automated warehousing systems outperform manpower-based alternatives in terms of productivity, safety, and long-term operating expenses, while manual systems retain advantages in flexibility and lower initial investment. Overall, this study contributes to the fuzzy MCDM literature by demonstrating how multi-method integration and structured sensitivity analysis enhance the robustness of decision outcomes, while also offering practical insights for managers evaluating warehouse automation strategies.