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A Multi-Objective Systems Engineering Framework for Agricultural Logistics Under Operational and Social Complexity

Amir Karbassi Yazdi

Published 2026 in Mathematics

ABSTRACT

Background: Agricultural logistics in arid, geographically dispersed areas require complex trade-offs among efficiency, equity, and robustness under uncertainty. Standard multi-objective vehicle routing problem (VRP) formulations, which primarily focus on cost or environmental parameters, do not explicitly account for social equity or transparency in decision-making. However, existing work seldom combines the objective of social equity as an endogenous optimization objective with robustness and interpretability within a unified mathematical framework. Methods: In this paper, we present a systems engineering decision-support framework informed by a multi-objective mixed-integer linear programming formulation for agricultural logistics planning. Economic, environmental, operational, and social equity goals are combined through ε-constraint to create trade-offs that can be interpreted at the policy level. We assess robustness against demand and travel-time uncertainty using the Bertsimas–Sim framework. A staged activation strategy separates conceptual model completeness from numerical implementation, and sensitivity analyses are conducted by perturbing vital operational parameters. Results: An illustrative situation in Northern Chile shows that this framework produces stable decision regimes and clear trade-offs in practice. The results show that meaningful improvements in workload balance and service equity can be achieved with negligible changes in operational efficiency. As we have learned in sensitivity experiments, assignment structures and qualitative trade-off patterns are robust under realistic parameter variations, and structural changes occur only beyond known threshold regimes. Conclusions: The major contribution of this work is the formulation of a systems engineering framework that extends traditional multi-objective VRP formulations and integrates social equity, robustness, and decision transparency as core design principles. Instead of focusing only on numerical optimization performance, the framework encourages auditable planning decisions in the face of uncertainty. The numerical analysis results are for a proof-of-concept scale only; however, the framework can be extended to larger agricultural networks using decomposition and/or hybrid solutions.

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