Danang Setiawan, Hilmi Akbar Wijaya

Optimization of Broiler Chicken Harvest Scheduling using Integer Linear Programming: A Case Study

Introduction

Optimization of broiler chicken harvest scheduling using integer linear programming: a case study. Optimize broiler chicken harvest scheduling using Integer Linear Programming to boost farm profit by 12.41%. A Central Java case study shows IDR 40M/year additional revenue.

Abstract

An optimized scheduling system is essential for poultry farming to balance fluctuating market prices with fluctuating feed prices. In poultry farms, input costs account for over 90% of total expenses, with 66% allocated to feed and 25% to day-old chick purchases. This article presents an optimization model for chicken harvesting, using broiler chicken farming as a case study. A broiler chicken farm in Central Java is currently operating below optimal profitability because of a lack of a systematic harvest scheduling strategy. The Integer Linear Programming model was developed to obtain an optimal harvest schedule to maximize income with respect to operational constraints. The optimization indicates that the proposed schedule increases farm profit by 12.41% per production cycle, leading to an estimated additional yearly revenue of IDR 40,062,029. Furthermore, the sensitivity analysis indicates that market price and feed cost are the most significant factors that influence profitability, with market price fluctuations impacting profit by up to 100.17% and feeding cost changes affecting profitability by 66.10%. Minimizing the price of 5% feed can lead to an increase of 66% in profit and vice versa. This paper presents a framework of structured decision-making for poultry farms to obtain the highest possible profit and minimize loss occurrence by utilizing an optimal harvesting schedule.

Review

The paper, "Optimization of Broiler Chicken Harvest Scheduling using Integer Linear Programming: A Case Study," presents a highly relevant and impactful study addressing a critical challenge in poultry farming: optimizing harvest schedules to enhance profitability. It accurately identifies the substantial impact of input costs, particularly feed, on overall expenses and highlights the existing gap in systematic decision-making that leads to sub-optimal farm income. By proposing an Integer Linear Programming (ILP) model, the authors offer a structured and quantitative approach to overcome this deficiency, representing a significant contribution to both applied operations research and practical agricultural management. The abstract effectively sets the stage by articulating the problem, the chosen methodology, and the promise of substantial improvements. A key strength of this work is its grounded, case-study application, demonstrating the ILP model's utility within a real-world broiler farm in Central Java. The reported 12.41% increase in profit per production cycle, translating to over IDR 40 million in additional yearly revenue, provides compelling evidence of the model's tangible benefits. Furthermore, the inclusion of a sensitivity analysis is particularly commendable, offering crucial insights into the most significant factors influencing profitability. Highlighting market price and feed cost as dominant drivers, with their respective dramatic impacts on profit (up to 100.17% and 66.10% for small fluctuations), underscores the practical value of the model in identifying key risk areas and informing strategic procurement and sales decisions. This analysis moves beyond pure optimization to provide actionable intelligence for farm managers. While the abstract makes a strong case for the model's effectiveness, a more detailed exposition on the specific "operational constraints" incorporated into the ILP would enhance the paper's transparency and replicability in the full manuscript. Understanding precisely how factors such as pen capacity, market demand fluctuations, slaughterhouse processing limits, or labor availability are mathematically formulated would provide crucial context for readers. Additionally, while the profit increase is significant, the abstract implicitly assumes deterministic inputs for the optimization. Future research could benefit from exploring the model's robustness to real-time, unforeseen market shifts or operational disruptions (e.g., disease outbreaks) through stochastic programming or dynamic approaches. Expanding the study to multiple farms or different geographical regions would also strengthen the generalizability of the proposed framework.

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