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<p>Global trade imbalance and policy inconsistency dynamically challenge many manufacturers around the world to improve its flexibility and efficiency. Multiple time-period layout design approaches of multiple-product manufacturing under dynamic demand scenarios can alternatively be implemented to deal with demand fluctuations and facility relocation constraints. This paper presents robust and flexible approaches for designing machine layout under multiple time-period stochastic demand scenarios. Various state-of-the-art optimization algorithms including its modification, adaptation, and hybridization were proposed to solve the problem. Flower pollination algorithm, which has never been applied for designing the multiple time-period layout, was developed by incorporating more strategic features including reformed mechanism, dynamic parameters, and competitive learning. A sequential computational experiment was designed and carried out using 11 benchmarking problem instances. The statistical analysis on the numerical results demonstrated the superiority of the performance of the proposed methods compared with other conventional optimization algorithms.</p>

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Multiple time-period layout design for multi-product manufacturing under stochastic demand scenarios

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Srisatja Vitayasak,
  • Thatchai Thepphakorn,
  • Pupong Pongcharoen

摘要

Global trade imbalance and policy inconsistency dynamically challenge many manufacturers around the world to improve its flexibility and efficiency. Multiple time-period layout design approaches of multiple-product manufacturing under dynamic demand scenarios can alternatively be implemented to deal with demand fluctuations and facility relocation constraints. This paper presents robust and flexible approaches for designing machine layout under multiple time-period stochastic demand scenarios. Various state-of-the-art optimization algorithms including its modification, adaptation, and hybridization were proposed to solve the problem. Flower pollination algorithm, which has never been applied for designing the multiple time-period layout, was developed by incorporating more strategic features including reformed mechanism, dynamic parameters, and competitive learning. A sequential computational experiment was designed and carried out using 11 benchmarking problem instances. The statistical analysis on the numerical results demonstrated the superiority of the performance of the proposed methods compared with other conventional optimization algorithms.