| Abstrakt: |
This study presents a comprehensive multi-objective transportation model aimed at optimizing complex vehicle routing problems, which are nondeterministic polynomial time NP-hard due to spatial, temporal, and capacity constraints. In this study, the multi-objective transportation model integrates decisionmaker preferences with hybrid optimization techniques, including the approximatecombinatorial method, ant colony optimization and evolutionary algorithms. it seeks to minimize transportation costs, time, and emissions while accounting for real-world constraints such as fleet composition, customer demand, and servicelevel agreements. The techniques like multi-criteria decision-making methods are employed to refine the solution set, balancing objectives like cost, time, environmental impact, and service level. The novel optimization model is applied to a fuel distribution case study involving 18 customers and a heterogeneous fleet, where it optimizes vehicle routes to meet delivery requirements efficiently. The multiobjective transportation framework generates multiple feasible solutions, which are further narrowed down using decision-making frameworks to ensure alignment with organizational goals and decision-maker preferences. The integration of quantitative optimization techniques with qualitative decision-making processes makes this model robust and scalable, offering a practical tool for enhancing operational efficiency in transportation systems. This approach effectively addresses real-world logistics challenges, demonstrating significant improvements in route efficiency, cost savings, and environmental sustainability. [ABSTRACT FROM AUTHOR] |
