Two parallel-machine scheduling with maximum waiting time for an emergency job

References

• Colin, E. C., and R. C. Quinino. 2005. “An Algorithm for Insertion of Idle Time in the Single-Machine Scheduling Problem with Convex Cost Functions.” Computers & Operations Research 32 (9): 2285–2296. https://doi.org/10.1016/j.cor.2004.03.003.
   Web of Science ®Google Scholar
• Daldoul, D., I. Nouaouri, H. Bouchriha, and H. Allaoui. 2018. “A Stochastic Model to Minimize Patient Waiting Time in An Emergency Department.” Operations Research for Health Care 18:16–25. https://doi.org/10.1016/j.orhc.2018.01.008.
   Web of Science ®Google Scholar
• Detti, P., G. Nicosia, A. Pacifici, and G. Z. M. D. Lara. 2016. “Robust Single Machine Scheduling with External-Party Jobs.” IFAC-Papers Online 49 (12): 1731–1736. https://doi.org/10.1016/j.ifacol.2016.07.832.
   Google Scholar
• Essen, J. T. V., E. W. Hans, J. L. Hurink, and A. Oversberg. 2012. “Minimizing the Waiting Time for Emergency Surgery.” Operations Research for Health Care 1 (2-3): 34–44. https://doi.org/10.1016/j.orhc.2012.05.002.
   Google Scholar
• Gorman, M. F., and D. G. Conway. 2018. “A Tutorial of Integrating Duality and Branch and Bound in Earliness-Tardiness Scheduling with Idle Insertion Time Problems.” International Journal of Production Research 56 (1–2): 262–277. https://doi.org/10.1080/00207543.2017.1397794.
   Web of Science ®Google Scholar
• Jung, K. S., M. Pinedo, C. Sriskandarajah, and V. Tiwari. 2019. “Scheduling Elective Surgeries with Emergency Patients At Shared Operating Rooms.” Production and Operations Management 28 (6): 1407–1430. https://doi.org/10.1111/poms.2019.28.issue-6.
   Web of Science ®Google Scholar
• Liou, J. 2020. “Dominance Conditions Determination Based on Machine Idle Times for the Permutation Flowshop Scheduling Problem.” Computers & Operations Research 122:104964. https://doi.org/10.1016/j.cor.2020.104964.
   Web of Science ®Google Scholar
• Mahnam, M., G. Moslehi, and S. M. T. F. Ghomi. 2013. “Single Machine Scheduling with Unequal Release Times and Idle Insert for Minimizing the Sum of Maximum Earliness and Tardiness.” Mathematical and Computer Modelling 57 (9–10): 2549–2563. https://doi.org/10.1016/j.mcm.2013.01.007.
   Google Scholar
• Rahmani, D., and M. Heydari. 2014. “Robust and Stable Flow Shop Scheduling with Unexpected Arrivals of New Jobs and Uncertain Processing Times.” Journal of Manufacturing Systems 33 (1): 84–92. https://doi.org/10.1016/j.jmsy.2013.03.004.
   Web of Science ®Google Scholar
• Rahmani, D., and R. Ramezanian. 2016. “A Stable Reactive Approach in Dynamic Flexible Flow Shop Scheduling with Unexpected Disruptions: A Case Study.” Computers & Industrial Engineering 98: 360–372. https://doi.org/10.1016/j.cie.2016.06.018.
   Web of Science ®Google Scholar
• Schaller, J., and J. M. S. Valente. 2019. “Heuristics for Scheduling Jobs in a Permutation Flow Shop to Minimize Total Earliness and Tardiness with Unforced Idle Time Allowed.” Expert Systems with Applications 119:376–386. https://doi.org/10.1016/j.eswa.2018.11.007.
   Web of Science ®Google Scholar
• Schaller, J., and J. M. S. Valente. 2022. “Scheduling in a No-Wait Flow Shop to Minimise Total Earliness and Tardiness with Additional Idle Time Allowed.” International Journal of Production Research 60 (18): 5488–5504. https://doi.org/10.1080/00207543.2021.1962017.
   Web of Science ®Google Scholar
• Tavakkoli-Moghaddam, R., G. Moslehi, M. Vasei, and A. Azaron. 2015. “Optimal Scheduling for a Single Machine to Minimize the Sum of Maximum Earliness and Tardiness Considering Idle Insert.” Applied Mathematics & Computation 167 (2): 1430–1450. https://doi.org/10.1016/j.amc.2004.08.022.
   Google Scholar
• Waldherr, S., S. Knust, and D. Briskorn. 2016. “Synchronous Flow Shop Problems: How Much Can We Gain by Leaving Machines Idle?.” Omega 72:15–24. https://doi.org/10.1016/j.omega.2016.10.006.
   Web of Science ®Google Scholar
• Wang, Z., and R. Liu. 2022. “Managing Appointments of Outpatients Considering the Presence of Emergency Patients: The Combination of the Analytical and Data-Driven Approach.” International Journal of Production Research 60 (13): 4214–4228. https://doi.org/10.1080/00207543.2021.2007425.
   Web of Science ®Google Scholar
• Wang, S., R. Wu, F. Chu, and J. Yu. 2020. “Identical Parallel Machine Scheduling with Assurance of Maximum Waiting Time for An Emergency Job.” Computers & Operations Research 118:104918. https://doi.org/10.1016/j.cor.2020.104918.
   Web of Science ®Google Scholar
• Wang, D., Y. Yin, and T. C. E. Cheng. 2018. “Parallel-Machine Rescheduling with Job Unavailability and Rejection.” Omega 81:246–260. https://doi.org/10.1016/j.omega.2018.04.008.
   Web of Science ®Google Scholar
• Wen, J., N. Geng, and X. Xie. 2020. “Real-Time Scheduling of Semi-Urgent Patients Under Waiting Time Targets.” International Journal of Production Research 58 (4): 1127–1143. https://doi.org/10.1080/00207543.2019.1612965.
   Web of Science ®Google Scholar
• Yang, B., and J. Geunes. 2008. “Predictive-Reactive Scheduling on a Single Resource with Uncertain Future Jobs.” European Journal of Operational Research 189 (3): 1267–1283. https://doi.org/10.1016/j.ejor.2006.06.077.
   Web of Science ®Google Scholar
• Zheng, F., M. L. Pinedo, K. Lee, M. Liu, and Y. Xu. 2019. “Towards Robustness of Response Times: Minimising the Maximum Inter-Completion Time on Parallel Machines.” International Journal of Production Research 57 (1): 182–199. https://doi.org/10.1080/00207543.2018.1466063.
   Web of Science ®Google Scholar
• Zheng, F., Y. Sui, and Y. Xu. 2021. “Three Parallel Machines Scheduling with Minimizing the Maximum Inter-Completion Time.” Systems Engineering Theory & Practice 41 (4): 1025–1036.
   Google Scholar
• Zheng, F., Y. Sui, E. Zhang, Y. Xu, and M. Liu. 2020. “Two Parallel Identical Machines Scheduling to Minimise the Maximum Inter-Completion Time.” International Journal of Production Research 58 (22): 6811–6825. https://doi.org/10.1080/00207543.2019.1685707.
   Web of Science ®Google Scholar