A hybrid scenario-based fuzzy stochastic model for closed-loop dry port network design with multiple robustness measures

Dry ports are crucial to marine logistics, providing significant economic, environmental, and social benefits. Effective network design is key to their success, and decisions on location and capacity must consider both objective data and expert opinions to avoid costly long-term consequences. This research presents an integrative framework that combines a fuzzy multi-criteria decision-making method with fuzzy stochastic chance-constrained optimization, forms an integrated multi-objective model, and solves it using the epsilon-constraint approach. The demands for imports and exports are presented in a scenario-based manner, and in each scenario, demands are expressed as trapezoidal fuzzy numbers. To guarantee the level of confidence in satisfying demands, a fuzzy chance constraint method has been used and the stochastic objective function has been transformed into a deterministic counterpart using a novel robust optimization technique. The paper is also among the first to consider reverse logistics from inland to seaports through dry ports to highlight their role in marine export. Since the output of the model is highly dependent on the parameter settings, the mechanism of the parameters' impact on the profit maximization objective function is investigated through a full factorial experiment. The results of the mathematical model proposed in this research, which has been tested on an Iranian case study suggest that high-capacity dry ports should be established in the cities of Sirjan and Tehran, while smaller dry ports should be built in the cities of Isfahan, and Ahvaz. Also, the model suggests that the seaports of Chabahar and Anzali should be connected to Iran's national railway network so that the transportation of goods from them to dry ports on the hinterlands in Sirjan and Tehran can be done at a lower cost.