Optimizing Agricultural Logistics Networks: Evidence from the Mekong Delta, Vietnam

Abstract

Agricultural supply chains (ASC) in the Mekong Delta face significant challenges in balancing economic efficiency, social development, and environmental sustainability. This study develops a multi-objective mixed-integer linear programming (MILP) model to optimize the citrus supply chain (CSC) by simultaneously minimizing total costs, maximizing employment, and reducing CO? emissions. The augmented?-constraint method was applied to generate Pareto-optimal solutions and to identify trade-offs among competing objectives. The results indicate that within a predictable range, moderate increases in employment can be achieved with stable marginal costs and emissions. However, beyond a critical knee point, additional social benefits come at disproportionately high economic costs with limited environmental gains. The optimal trade-off solution was identified at v = 4.75, yielding a balanced outcome of 3,304.02 million VND in cost, 718 jobs, and 2,811.87 kg of CO? emissions. This research contributes to theory by extending multi-objective optimization in agri-food logistics, provides methodological insights through the use of the augmented?-constraint approach, and offers practical implications for policymakers and managers in designing sustainable supply chain strategies in the Mekong Delta.