Multi-Objective Chili Pepper Production Optimization Using HSA Under Price and Rainfall Variability

Abstract

Chili pepper production faces uncertainty associated with price dynamics and agroclimatic conditions, particularly rainfall variability. This study develops a multi-objective optimization model based on the Harmony Search Algorithm (HSA) to determine chili pepper production levels while considering gross revenue, exposure to rainfall deviations, and total production. The analysis uses 36 monthly periods from January 2022 to December 2024. Three objective functions are formulated: maximizing gross revenue, minimizing exposure to rainfall deviations, and maximizing total production. The reported HSA run, using 1,000 iterations, produced a fitness value of 0.6537, with gross revenue of Rp462.463.225.492.496,44, rainfall-deviation exposure of 804.788.341,60, and total production of 9.158.801,66 tons. The corresponding normalized objective values were 0.8165, 0.3476, and 0.7969. A post-hoc sensitivity analysis of the objective weights shows that the aggregated fitness changes when greater emphasis is placed on revenue, rainfall exposure, or production, indicating that the equal-weight solution should be interpreted as one compromise specification rather than a unique optimum. Because annual FAOSTAT production was evenly disaggregated into monthly baseline values, the production series has limited temporal variation (CV 0.56%); consequently, correlation and regression results should be interpreted as exploratory rather than as evidence of actual monthly causal relationships. The HSA was executed once in the original analysis; therefore, no statistical claim of algorithmic superiority or solution stability is made. The proposed model provides a framework for integrating economic and agroclimatic considerations into chili production planning, subject to these data and computational limitations.

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