Advancing Sustainable Development Goals Through Intelligent Port Logistics: A Multi-Objective Optimization Framework for Social, Environmental, and Economic Sustainability

This study develops a multi-objective optimization framework for sustainable container truck dispatching in port logistics, addressing the limited joint consideration of environmental compliance, worker-sensitive assignment, and operational efficiency in traditional dispatching practice. The problem is formulated as a constrained assignment-and-scheduling model under time-window, compliance, capacity, and service requirements. To balance optimality and real-time responsiveness, a dual-path solution strategy is proposed, combining a mixed-integer linear programming (MILP) model for small-scale instances with a Priority-Based Constructive Heuristic with Conflict Resolution (PBCH-CR) for medium-to-large-scale scenarios. Computational experiments on scenario-based synthetic instances calibrated to empirical port-operation distributions show that PBCH-CR maintains 100% environmental compliance for assigned orders, improves familiarity-oriented matching relative to the FCFS baseline, and sustains strong emergency-response performance within sub-minute computation times. Sensitivity analysis further shows that improving urgency-oriented performance entails a reduction in freight-revenue-oriented performance. Overall, the framework provides a practical approach to balancing environmental compliance, operational efficiency, and worker-sensitive dispatching, with relevance to Sustainable Development Goals 11 and 13 and to SDG 8-related objectives.