Multi-Objective Simulation Optimization Integrated with Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution for Pollution Routing Problem

Routing is one of the most important components of logistics and has a vital role in economic growth. Inefficient routing has also contributed to troubling pollutants that have adverse environmental effects worldwide. Over the years, operations research practitioners have therefore drawn considerable attention to the routing problem. In this paper, a pollution routing problem (PRP) with time window constraints and capacitated vehicles is considered to integrate environmental issues in routing for pollution reduction. This work introduces a simulation optimization methodology that includes the realization of important parameters in real-world applications to minimize the total lateness cost and fuel consumption. The proposed method was developed with Simio simulation software and non-dominated sorting genetic algorithm-II (NSGA-II). It evaluates the dynamic system operations over time and performs a comprehensive analysis. In addition, a two-step hierarchical multi-criteria decision-making (MCDM) method is employed to choose the best strategy for PRP. Results of the two-step hierarchical MCDM method demonstrated that the strategy considering the total cost of supply chain is the best alternative for the proposed system. The use of MCDM methods after simulation can be a more robust and effective way to find solutions to such complex problems.