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Abstract
This review analyzes the strategic and tactical optimization frameworks that enhance cost reduction and resilience in food grain storage and transportation logistics. It identifies that sustainable efficiency depends on maximizing systemic value and minimizing Post-Harvest Loss (PHL) rather than merely reducing variable costs. The study compares traditional cost-minimization models with value-maximizing approaches that integrate Opportunity Cost (OC) within Integrated Network Design, particularly the Location-Inventory-Routing Problem (LIRP). Findings show that OC-based models deliver superior long-term economic and resource efficiency compared to conventional transportation cost–focused frameworks.
Four strategic imperatives are emphasized:
(1) Infrastructure consolidation and automation to achieve economies of scale;
(2) Integrated optimization using OC to guide infrastructure investment;
(3) Dynamic technology deployment through IoT, AI, and ML to prevent losses and improve forecasting accuracy; and
(4) Backhaul maximization to lower costs and reduce emissions.
A comparative evaluation of advanced optimization methods—including fuzzy mixed-integer programming, shuttle train economics, and OC-based trade optimization—demonstrates complementary strengths in tactical and strategic applications.
The paper identifies key research gaps, including limited OC integration in LIRP frameworks, insufficient AI validation for quality management, inadequate modeling of dynamic resilience, and a lack of structured training programs for human capital development. It concludes that combining OC-driven strategic planning with practical operational models forms a robust foundation for sustainable, cost-efficient, and resilient grain logistics systems.
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References
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