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International Conference on Machine Intelligence and Signal Processing

MISP 2019: Machine Intelligence and Signal Processing pp 215–224Cite as

Bacterial Foraging Optimization in Non-identical Parallel Batch Processing Machines

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1085))

Abstract

The paper presents a modified bacterial foraging optimization (BFO) algorithm to solve non-identical parallel batch processing machine scheduling problems with the objective of minimizing total weighted tardiness. The proposed algorithm combines the shortest position value-based heuristic with the basic BFO algorithm to enhance the solution quality. The computational experimental results based on ninety randomly generated problem instances reveal that the proposed algorithm performs better than the existing particle swarm optimization algorithm.

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Author information

Authors and Affiliations

  1. Mechanical Engineering Department, National Institute of Technology, Agartala, 799046, India

    Arindam Majumder

  2. Mechanical Engineering Department, Jadavpur University, Kolkata, 700032, India

    Dipak Laha

Authors
  1. Arindam Majumder
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  2. Dipak Laha
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Editor information

Editors and Affiliations

  1. Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India

    Sonali Agarwal

  2. Department of Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, India

    Shekhar Verma

  3. Department of Electrical Engineering and Computing Science, University of Cincinnati, Cincinnati, OH, USA

    Dharma P. Agrawal

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Majumder, A., Laha, D. (2020). Bacterial Foraging Optimization in Non-identical Parallel Batch Processing Machines. In: Agarwal, S., Verma, S., Agrawal, D. (eds) Machine Intelligence and Signal Processing. MISP 2019. Advances in Intelligent Systems and Computing, vol 1085. Springer, Singapore. https://doi.org/10.1007/978-981-15-1366-4_17

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