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<p>This study examines the challenges of optimizing production, maintenance, and transportation planning in a multi-site environment, where each site faces unique demands and potential operational failures. The research highlights the importance of integrating these critical functions to enhance system performance. The primary objective is to develop a comprehensive approach that minimizes costs while ensuring high service levels and system reliability. To achieve this, a novel methodology using random search methods and genetic algorithms for production planning is proposed, coupled with a collaborative distribution strategy that mitigates shortages across sites. The study also introduces a preventive maintenance model that considers how production rates affect failure rates, optimizing maintenance planning. The proposed integrated optimization model led to a cost reduction of approximately <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10696_2025_9618_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="44" /> </InlineMediaObject> <EquationSource Format="TEX">\(9.07\text{\%}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>9.07</mn> <mtext>\%</mtext> </mrow> </math></EquationSource> </InlineEquation> in total operational expenses compared to traditional separated planning approaches, demonstrating its effectiveness in balancing production and maintenance planning under stochastic demand conditions. The results demonstrate significant improvements in operational efficiency and cost-effectiveness through the integrated approach. Sensitivity analyses further validate the robustness of the proposed model, showing its adaptability to varying parameters and operational conditions. These findings have important implications for industries seeking to streamline multi-site operations and enhance their competitive advantage.</p>

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Joint optimization of production, maintenance, and distribution planning in a multi-site environment

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Kamar Diaz,
  • Mahfoudh Barhoumi,
  • Mohamed Ali Kammoun,
  • Zied Hajej,
  • Abdelbadiâ Chaker,
  • Sami Bennour

摘要

This study examines the challenges of optimizing production, maintenance, and transportation planning in a multi-site environment, where each site faces unique demands and potential operational failures. The research highlights the importance of integrating these critical functions to enhance system performance. The primary objective is to develop a comprehensive approach that minimizes costs while ensuring high service levels and system reliability. To achieve this, a novel methodology using random search methods and genetic algorithms for production planning is proposed, coupled with a collaborative distribution strategy that mitigates shortages across sites. The study also introduces a preventive maintenance model that considers how production rates affect failure rates, optimizing maintenance planning. The proposed integrated optimization model led to a cost reduction of approximately \(9.07\text{\%}\) 9.07 \% in total operational expenses compared to traditional separated planning approaches, demonstrating its effectiveness in balancing production and maintenance planning under stochastic demand conditions. The results demonstrate significant improvements in operational efficiency and cost-effectiveness through the integrated approach. Sensitivity analyses further validate the robustness of the proposed model, showing its adaptability to varying parameters and operational conditions. These findings have important implications for industries seeking to streamline multi-site operations and enhance their competitive advantage.