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An integer linear‐programming model for machine scheduling

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  • Harvey M. Wagner

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  • Harvey M. Wagner, 1959. "An integer linear‐programming model for machine scheduling," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 6(2), pages 131-140, June.
    • Handle: RePEc:wly:navlog:v:6:y:1959:i:2:p:131-140
    DOI: 10.1002/nav.3800060205
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    Cited by:

    1. Bahman Naderi & Vahid Roshanaei & Mehmet A. Begen & Dionne M. Aleman & David R. Urbach, 2021. "Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2608-2635, August.
    2. Sun, Yige & Chung, Sai-Ho & Wen, Xin & Ma, Hoi-Lam, 2021. "Novel robotic job-shop scheduling models with deadlock and robot movement considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    3. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    4. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    5. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    6. 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).
    7. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
    8. Levorato, Mario & Figueiredo, Rosa & Frota, Yuri, 2022. "Exact solutions for the two-machine robust flow shop with budgeted uncertainty," European Journal of Operational Research, Elsevier, vol. 300(1), pages 46-57.
    9. S.S. Panwalkar & Christos Koulamas, 2015. "Scheduling research and the first decade of NRLQ: A historical perspective," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 335-344, June.
    10. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    11. Xinyu Yao & Karmel S. Shehadeh & Rema Padman, 2024. "Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach," Health Care Management Science, Springer, vol. 27(3), pages 352-369, September.
    12. Shehadeh, Karmel S. & Cohn, Amy E.M. & Epelman, Marina A., 2019. "Analysis of models for the Stochastic Outpatient Procedure Scheduling Problem," European Journal of Operational Research, Elsevier, vol. 279(3), pages 721-731.
    13. Rakovitis, Nikolaos & Li, Dan & Zhang, Nan & Li, Jie & Zhang, Liping & Xiao, Xin, 2022. "Novel approach to energy-efficient flexible job-shop scheduling problems," Energy, Elsevier, vol. 238(PB).
    14. Mario Levorato & David Sotelo & Rosa Figueiredo & Yuri Frota, 2024. "Efficient solutions to the m-machine robust flow shop under budgeted uncertainty," Annals of Operations Research, Springer, vol. 338(1), pages 765-799, July.

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