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Minimizing Tardiness in Stochastic Flexible Job Shop Problem

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Innovations in Bio-Inspired Computing and Applications (IBICA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1372))

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Abstract

In this paper, Flexible Job Shop Scheduling Problem (FJSP) with parallel batch processing machine is investigated. The problem is to find the best solution for assign jobs to machines and batch’s processing sequence to minimizing tardiness. First a Mixed Integer Programming (MIP) formulation is proposed for the first time. Primarily proposes solution method is based on the genetic algorithm to solve the deterministic problem. The results obtained from the computational experiments validate the effectiveness of the proposed method. In addition, as the scheduling is affected by uncertain conditions, a simulation-based optimization algorithm is developed to tackle these uncertainties. This algorithm benefits from the fast computational time and solution quality of the proposed genetic algorithm, combined with simulation technique. The uncertain factors considered include the order arrival time, rework rate. In this approach, a simulation model is used to investigate the effect of possible conditions on the responses of the genetic algorithm.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Yazd University, Yazd, Iran

    Mahsa Nekouei-Shahraki & Mohammd Mehdi Lotfi

  2. Machine Intelligence Research (MIR) Labs, Scientific Network for Innovation and Research Excellence, Washington, USA

    Ajith Abraham

Authors
  1. Mahsa Nekouei-Shahraki
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  2. Ajith Abraham
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  3. Mohammd Mehdi Lotfi
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Corresponding author

Correspondence to Ajith Abraham .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. National Institute of Information and Communications Technology (NICT), Koganei, Tokyo, Japan

    Hideyasu Sasaki

  3. Universidade Federal da Bahia, Salvador, Brazil

    Ricardo Rios

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Institute of Technology and Science, Ghaziabad, Uttar Pradesh, India

    Umang Singh

  6. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Nekouei-Shahraki, M., Abraham, A., Lotfi, M.M. (2021). Minimizing Tardiness in Stochastic Flexible Job Shop Problem. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2020. Advances in Intelligent Systems and Computing, vol 1372. Springer, Cham. https://doi.org/10.1007/978-3-030-73603-3_8

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