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Adaptive genetic algorithm for advanced planning in manufacturing supply chain

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Abstract

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach.

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Authors and Affiliations

  1. Department of Information & Industrial Engineering, Hanyang University, Ansan, 425-791, Korea

    Chiung Moon

  2. Department of Industrial Systems & Information Engineering, Korea University, #1 Anamdong 5-ga, Sungbukku, Seoul, 136-713, Korea

    Yoonho Seo

  3. School of Business Administration, Chosun University, Gwangju, 501-759, Korea

    Youngsu Yun

  4. Graduate School of Information, Production & Systems, Waseda University, Kitakyushu, Japan

    Mitsuo Gen

Authors
  1. Chiung Moon
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  2. Yoonho Seo
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  3. Youngsu Yun
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  4. Mitsuo Gen
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Corresponding author

Correspondence to Yoonho Seo.

Additional information

Received: June 2005 / Accepted: December 2005

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Moon, C., Seo, Y., Yun, Y. et al. Adaptive genetic algorithm for advanced planning in manufacturing supply chain. J Intell Manuf 17, 509–522 (2006). https://doi.org/10.1007/s10845-005-0010-0

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  • DOI: https://doi.org/10.1007/s10845-005-0010-0

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