A hyper-heuristic optimization and communication framework for sustainable agricultural decision-making: A case study on Lebanese farms

Agricultural systems in Lebanon face intertwined constraints, water scarcity, fragmented landholdings, first-mile logistics frictions, and climatic variability, that limit the effectiveness of single-technique optimizers. We propose a selection-based hyper-heuristic decision-support framework that learns to choose among diverse low-level heuristics via a multi-armed bandit policy (softmax exploration–exploitation) with a simulated-annealing-inspired move acceptance. Using a real-world dataset from 15 smallholder farms in the Beqaa Valley across three seasons (120 decision variables; 50 constraints), we jointly address crop planning, irrigation scheduling, and first-mile routing formulated as capacitated vehicle routing with time windows (CVRPTW). Empirically, the framework outperforms Genetic Algorithm, Simulated Annealing, and Tabu Search with 9.7–15.8% higher normalized objective values across problems, trading a modest 18.5% average runtime increase versus the fastest baseline (SA). The gains materialize through concrete mechanisms that directly affect decisions: (i) higher water-use efficiency by prioritizing parcels with the largest marginal yield response under scarcity; (ii) resilient crop mixes that balance water-intensive and drought-tolerant varieties across fragmented parcels; and (iii) clustered farm pickups that reduce first-mile distance and distribution cost. These outputs translate into actionable seasonal crop-mix plans, weekly irrigation calendars, and daily pickup routes. The learned policies exhibit interpretable patterns aligned with agronomic intuition, improving trust and adoption potential. While developed in Lebanon, the framework is directly transferable to smallholder, water-stressed contexts with heterogeneous parcels and volatile logistics, offering an adaptive, interpretable, and scalable pathway toward more sustainable resource allocation and yield-oriented decisions. • Traditional optimization methods often struggle to adapt to dynamic agricultural systems. • A novel hyper-heuristic optimization framework for agricultural decision-making is proposed. • Our framework develops a scalable solution to optimize key agricultural operations. • The framework tackles problems such as crop planning, irrigation optimization, and distribution logistics under uncertainty.