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Erschienen in: Journal of Intelligent Manufacturing 1/2021

26.03.2020

Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs

verfasst von: Hongliang Zhang, Yujuan Wu, Ruilin Pan, Gongjie Xu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 1/2021

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Abstract

As one of the demand-side programs, time-of-use (TOU) tariffs brings opportunities of maintaining power grid stability for electricity providers and chances of energy conservation for manufacturers, but it also brings challenge for enterprises to optimize scheduling schemes. This paper studies a two-stage parallel machine scheduling problem under TOU to minimize total electricity costs. The two-stage parallel machine system is composed of identical parallel speed-scaling machines at stage 1 and unrelated parallel machines at stage 2. The key issues lie in assigning a group of jobs to a set of parallel machines at each stage and choosing the appropriate processing speed for all jobs at stage 1, and then determining the interval of processing time for jobs on each selected machine. To solve this problem, a new continuous-time mixed-integer linear programming model is formulated. According to the characteristics of this model, a tabu search-greedy insertion hybrid (TS-GIH) algorithm is designed, which realizes job-machine assignment based on load balancing principle, job insertion with greedy mechanism as well as movement and speed adjustment strategies to find more suitable positions for jobs. The effectiveness of the proposed TS-GIH is demonstrated by comparing with CLPEX and improved genetic algorithm (IGA) through real-life and randomly generated instances. The results show that TS-GIH can realize the trade-off between computation time and solution quality. Compared with CLPEX, the computation time of TS-GIH is significantly less, and the solution quality is much better than IGA.

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Literatur
Zurück zum Zitat Lin, C., Luo, W., & Zhang, G. (2011). Approximation algorithms for unrelated machine scheduling with an energy budget. In Frontiers in algorithmics and algorithmic aspects in information and management—Joint international conference, FAW-AAIM 2011, Jinhua, China, May 28–31, 2011. Proceedings. http://doi.org/10.1007/978-3-642-21204-8_27. Lin, C., Luo, W., & Zhang, G. (2011). Approximation algorithms for unrelated machine scheduling with an energy budget. In Frontiers in algorithmics and algorithmic aspects in information and managementJoint international conference, FAW-AAIM 2011, Jinhua, China, May 2831, 2011. Proceedings. http://​doi.​org/​10.​1007/​978-3-642-21204-8_​27.
Zurück zum Zitat Pinedo, M. (2012). Scheduling: Theory, algorithms, and systems. New York: Springer.CrossRef Pinedo, M. (2012). Scheduling: Theory, algorithms, and systems. New York: Springer.CrossRef
Zurück zum Zitat Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563–3573.CrossRef Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563–3573.CrossRef
Metadaten
Titel
Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs
verfasst von
Hongliang Zhang
Yujuan Wu
Ruilin Pan
Gongjie Xu
Publikationsdatum
26.03.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 1/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01561-6

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