A MULTI-OBJECTIVE OPTIMIZATION MODEL FOR SUSTAINABLE KIWI SUPPLY CHAIN IN HIMALAYAN REGIONS – CASE OF SHAMA (BAGESHWAR) TO DELHI

Abstract

Achieving global sustainability in food systems by 2050 requires transformative innovations, particularly in supply chains for perishable high-value crops grown in climate-vulnerable regions. This study develops a multi-objective mixed-integer linear programming (MILP) framework to optimize the kiwi supply chain from Shama, a remote Himalayan production hub in Bageshwar, Uttarakhand, to urban markets in Haldwani and Delhi. The model balances minimization of transportation costs and times (linked to fuel use and emissions), maximization of product freshness (reducing food loss and waste), storage constraints, and demand fulfilment under uncertainty. By incorporating time-dependent perishability decay and route-specific challenges in mountainous terrain, the approach minimizes operational inefficiencies that contribute to post-harvest losses—estimated globally at 13–20% for fresh produce—and supports resilient (FAO,2019), low-carbon Agri-logistics. Results highlight trade-offs among economic viability, freshness retention, and environmental sustainability. The balanced scenario reduces post-harvest losses from 25% to 14.1%, increasing farmer income by ₹6,150 per season. It also offers actionable strategies for smallholder cooperatives and policymakers. This work contributes to sustainable Agri–supply chain innovation by promoting resource-efficient distribution, minimizing post-harvest spoilage, and enabling adaptive solutions that benefit farmers in Uttarakhand. It integrates a multi-objective mixed-integer linear programming (MO-MILP) framework tailored to the unique Himalayan terrain, incorporating terrain-specific perishability factors. The proposed approach is scalable and can be applied to similar remote agricultural systems worldwide, supporting long-term sustainability goals toward 2050