Optimization modelling of grain logistics networks: reducing post-harvest losses through multimodal transport in the Narmada basin region
Post-harvest losses remain a serious challenge in the Narmada basin region. Farmers and traders lose significant quantities of grain during storage and movement. Inefficient logistics networks contribute heavily to these losses.
Researchers develop optimization models to address the problem. These models examine grain movement from farms to markets and storage points. They incorporate road, rail, and limited waterway options across the basin.
Furthermore, the models treat transport cost, time, and handling frequency as key variables. High handling points increase spoilage risk. Therefore, the objective function seeks to minimize both total cost and loss probability.
Additionally, analysts use network flow techniques and linear programming. They assign grain volumes to the most efficient multimodal routes. Constraints include vehicle capacity, road conditions, and seasonal river levels.
Meanwhile, the Narmada basin offers specific advantages. Several districts produce surplus wheat, soybean, and coarse grains. Existing railway lines and national highways provide backbone connectivity. However, last-mile road links remain weak in many villages.
As a result, the optimization framework prioritizes multimodal combinations. Grain can travel by truck to the nearest rail head and then move by train to major consumption centers. This approach reduces intermediate loading and unloading.
In addition, the models test the impact of improved storage at nodal points. Better warehousing near rail junctions further lowers wastage. Simulation runs show measurable reductions in overall post-harvest losses.
The analysis also highlights policy implications. Targeted investment in multimodal terminals can strengthen the logistics network. Coordinated planning between road and rail agencies becomes essential.
Overall, optimization modelling demonstrates a practical path forward. Multimodal transport reduces handling stages. It also improves speed and reliability. The Narmada basin stands to gain from such data-driven logistics redesign.