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<p>The distribution of oil palm seedlings (KKS) plays a strategic role in the sustainability of the plantation system but is faced with budget constraints, variations in distribution costs, and inter-regional disparities. This study develops a data driven multi-objective stochastic optimization framework to balance three main dimensions involve coverage, equity, and cost efficiency. Utilizing historical KKS distribution data and designing baseline, high-cost, and low-budget scenarios, this study formulates three approaches: a deterministic model as the baseline, a two-stage risk-neutral (RN) stochastic optimization, and a risk-averse (RA) expansion using Conditional Value-at-Risk (CVaR). The results indicate that the RN model is able to increase national coverage by 0.72 but results in higher inequality by 0.11. Conversely, the RA model reduces inequality by 0.09 with a relatively small decrease in coverage by 0.70 and better cost control. Sensitivity analysis confirms that indicator weights and policy targets strongly influence coverage, while risk penalties have a greater influence on inequality. Under extreme scenarios of cost increases, budget cuts, and decreased efficiency, robustness tests demonstrate that RA is more stable, equitable, and efficient than RN. Beyond KKS distribution, this approach can be applied to other resource allocation systems to support equitable, efficient, and resilient plantation policies.</p>

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An Integrated Data-Driven Multi-Objective Analytics Framework for Sustainable Oil-Palm-Seed Resources Allocation

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Rahmad B. Y. Syah,
  • Retna Astuti Kuswardani,
  • Marischa Elveny

摘要

The distribution of oil palm seedlings (KKS) plays a strategic role in the sustainability of the plantation system but is faced with budget constraints, variations in distribution costs, and inter-regional disparities. This study develops a data driven multi-objective stochastic optimization framework to balance three main dimensions involve coverage, equity, and cost efficiency. Utilizing historical KKS distribution data and designing baseline, high-cost, and low-budget scenarios, this study formulates three approaches: a deterministic model as the baseline, a two-stage risk-neutral (RN) stochastic optimization, and a risk-averse (RA) expansion using Conditional Value-at-Risk (CVaR). The results indicate that the RN model is able to increase national coverage by 0.72 but results in higher inequality by 0.11. Conversely, the RA model reduces inequality by 0.09 with a relatively small decrease in coverage by 0.70 and better cost control. Sensitivity analysis confirms that indicator weights and policy targets strongly influence coverage, while risk penalties have a greater influence on inequality. Under extreme scenarios of cost increases, budget cuts, and decreased efficiency, robustness tests demonstrate that RA is more stable, equitable, and efficient than RN. Beyond KKS distribution, this approach can be applied to other resource allocation systems to support equitable, efficient, and resilient plantation policies.