Gong, Liu, et al._2017_TII_accepted.pdf (553.62 kB)
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Energy and labor aware production scheduling for industrial demand response using adaptive multiobjective memetic algorithm

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journal contribution
posted on 29.05.2018, 12:40 by Xu Gong, Ying Liu, Niels LohseNiels Lohse, Toon De Pessemier, Luc Martens, Wout Joseph
Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common trade-off between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II (NSGA-II) and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common trade-off relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

15

Issue

2

Pages

942-953

Citation

GONG, X. ...et al., 2018. Energy and labor aware production scheduling for industrial demand response using adaptive multiobjective memetic algorithm. IEEE Transactions on Industrial Informatics, 15 (2), pp.942-953.

Publisher

© Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Acceptance date

23/04/2018

Publication date

2018-05-22

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

ISSN

1551-3203

eISSN

1941-0050

Language

en