An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Xiuli Wu & Xianli Shen & Qi Cui, 2018. "Multi-Objective Flexible Flow Shop Scheduling Problem Considering Variable Processing Time due to Renewable Energy," Sustainability, MDPI, vol. 10(3), pages 1-30, March.
- Maroua Nouiri & Abdelghani Bekrar & Abderezak Jemai & Smail Niar & Ahmed Chiheb Ammari, 2018. "An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 603-615, March.
- Emilio Carrizosa & Dolores Romero-Morales, 2001. "Combining Minsum And Minmax: A Goal Programming Approach," Operations Research, INFORMS, vol. 49(1), pages 169-174, February.
- Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
- Sanober Hassan Khattak & Michael Oates & Rick Greenough, 2018. "Towards Improved Energy and Resource Management in Manufacturing," Energies, MDPI, vol. 11(4), pages 1-15, April.
- Jacek Błażewicz & Klaus H. Ecker & Erwin Pesch & Günter Schmidt & Jan Węglarz, 2007. "Handbook on Scheduling," International Handbooks on Information Systems, Springer, number 978-3-540-32220-7, December.
- Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
- Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
- Wichmann, Matthias Gerhard & Johannes, Christoph & Spengler, Thomas Stefan, 2019. "Energy-oriented Lot-Sizing and Scheduling considering energy storages," International Journal of Production Economics, Elsevier, vol. 216(C), pages 204-214.
- Seokgi Lee & Mona Issabakhsh & Hyun Woo Jeon & Seong Wook Hwang & Byung Chung, 2020. "Idle time and capacity control for a single machine scheduling problem with dynamic electricity pricing," Operations Management Research, Springer, vol. 13(3), pages 197-217, December.
- Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
- Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
- Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
- Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.
- Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
- Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
- Shun Jia & Qingwen Yuan & Wei Cai & Qinghe Yuan & Conghu Liu & Jingxiang Lv & Zhongwei Zhang, 2018. "Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes," Energies, MDPI, vol. 11(8), pages 1-16, August.
- Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
- Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
- Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
- Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
- Robson Flavio Castro & Moacir Godinho-Filho & Roberto Fernandes Tavares-Neto, 2022. "Dispatching method based on particle swarm optimization for make-to-availability," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1021-1030, April.
- Rami Naimi & Maroua Nouiri & Olivier Cardin, 2021. "A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives," Sustainability, MDPI, vol. 13(23), pages 1-36, November.
- Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
- Shun Jia & Shang Wang & Jingxiang Lv & Wei Cai & Na Zhang & Zhongwei Zhang & Shuowei Bai, 2021. "Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption," Sustainability, MDPI, vol. 13(24), pages 1-23, December.
- An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
More about this item
Keywords
machining system; machine; energy consumption; energy-efficient mechanism; energy-efficient flexible job shop scheduling; non-dominated sorting genetic algorithm-II;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:179-:d:194181. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.