Open Access
Issue |
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
|
|
---|---|---|
Page(s) | 4589 - 4605 | |
DOI | https://doi.org/10.1051/ro/2024173 | |
Published online | 21 October 2024 |
- S. Afkhami, A.H. Kashan and B. Ostadi, Effective league championship algorithm and lower bound procedure for scheduling a single batch-processing machine with non-identical job sizes and job rejection. RAIRO:RO 57 (2023) 1453–1479. [CrossRef] [EDP Sciences] [Google Scholar]
- S. Aminzadegan, M. Tamannaei and M. Rasti-Barzoki, Multi-agent supply chain scheduling problem by considering resource allocation and transportation. Comput. Ind. Eng. 137 (2019) 106003. [CrossRef] [Google Scholar]
- O.A. Arik, M. Schutten and E. Topan, Weighted earliness/tardiness parallel machine scheduling problem with a common due date. Expert Syst. Appl. 187 (2022) 115916. [CrossRef] [Google Scholar]
- P.S. Biçakci, I. Kara and M. Sagir, Single-machine order acceptance and scheduling problem considering setup time and release date relations. Arab. J. Sci. Eng. 46 (2021) 1549–1559. [CrossRef] [Google Scholar]
- C. Bierwirth and J. Kuhpfahl, Extended GRASP for the job shop scheduling problem with total weighted tardiness objective. Eur. J. Oper. Res. 261 (2017) 835–848. [CrossRef] [Google Scholar]
- M. Boukedroun, D. Duvivier, A. Ait-el-Cadi, V. Poirriez and M. Abbas, A hybrid genetic algorithm for stochastic job-shop scheduling problems. RAIRO:RO 57 (2023) 1617–1645. [CrossRef] [EDP Sciences] [Google Scholar]
- J. Branke and C.W. Pickardt, Evolutionary search for difficult problem instances to support the design of job shop dispatching rules. Eur. J. Oper. Res. 212 (2011) 22–32. [CrossRef] [Google Scholar]
- I.A. Chaudhry and P.R. Drake, Minimizing total tardiness for the machine scheduling and worker assignment problems in identical parallel machines using genetic algorithms. Int. J. Adv. Manuf. Technol. 42 (2009) 581–594. [CrossRef] [Google Scholar]
- Y.C. Chen and J.Y. Wang, A lower bound for minimizing waiting time in coexisting virtual and physical worlds. IEEE Access 12 (2024) 73470–73480. [CrossRef] [Google Scholar]
- J.C. Chen, Y.Y. Chen, T.L. Chen and Y.H. Kuo, Applying two-phase adaptive genetic algorithm to solve multi-model assembly line balancing problems in TFT-LCD module process. J. Manuf. Syst. 52 (2019) 86–99. [CrossRef] [Google Scholar]
- R.W. Cheng, M.S. Gen and T. Tozawa, Minmax earliness tardiness scheduling in identical parallel machine system using genetic algorithms. Comput. Ind. Eng. 29 (1995) 513–517. [CrossRef] [Google Scholar]
- T.H. Cormen, C.E. Leiserson, R.L. Rivest and C. Stein, Introduction to Algorithms. MIT Press (2009). [Google Scholar]
- T. Eren, A note on minimizing maximum lateness in an machine scheduling problem with a learning effect. Appl. Math. Comput. 209 (2009) 186–190. [MathSciNet] [Google Scholar]
- A. Esenam, Overview of digital agriculture: making growers lives more productive. Int. Sugar J. 119 (2017) 466–470. [Google Scholar]
- C. Firth, K. Dunn, M.H. Haeusler and Y. Sun, Anthropomorphic soft robotic end-effector for use with collaborative robots in the construction industry. Autom. Constr. 138 (2022) 104218. [CrossRef] [Google Scholar]
- X.N. Geng, X.Y. Sun, J.Y. Wang and L. Pan, Scheduling on proportionate flow shop with job rejection and common due date assignment. Comput. Ind. Eng. 181 (2023) 109317. [CrossRef] [Google Scholar]
- F.P. Golneshini and H. Fazlollahtabar, Meta-heuristic algorithms for a clustering-based fuzzy bi-criteria hybrid flow shop scheduling problem, Soft Comput. 23 (2019) 12103–12122. [CrossRef] [Google Scholar]
- I. González-Rodríguez, J. Puente, J.J. Palacios and C.R. Vela, Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problems. Soft Comput. 24 (2020) 16291–16302. [CrossRef] [Google Scholar]
- J. Grobler, A.P. Engelbrecht, S. Kok and S. Yadavalli, Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time. Ann. Oper. Res. 180 (2010) 165–196. [Google Scholar]
- J.G. Kim, S. Song and B. Jeong, Minimising total tardiness for the identical parallel machine scheduling problem with splitting jobs and sequence-dependent setup times. Int. J. Prod. Res. 58 (2020) 1628–1643. [CrossRef] [Google Scholar]
- J. Kuhpfahl and C. Bierwirth, A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective. Comput. Oper. Res. 66 (2016) 44–57. [CrossRef] [MathSciNet] [Google Scholar]
- M. Kumar and S.C. Sharma, Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput. Electr. Eng. 69 (2018) 395–411. [CrossRef] [Google Scholar]
- W.C. Lee, A note on single-machine scheduling with general learning effect and past-sequence-dependent setup time. Comput. Math. Appl. 62 (2011) 2095–2100. [CrossRef] [MathSciNet] [Google Scholar]
- W.C. Lee and J.Y. Wang, A three-agent scheduling problem for minimizing the flow time on two machines. RAIRO:RO 54 (2020) 307–323. [CrossRef] [EDP Sciences] [Google Scholar]
- Z. Li, R.Y. Zhong, A.V. Barenji, J.J. Liu, C.X. Yu and G.Q. Huang, Bi-objective hybrid flow shop scheduling with common due date. Oper. Res. 21 (2021) 1153–1178. [Google Scholar]
- K. Li, H. Zhang, C.B. Chu, Z.H. Jia and Y. Wang, A bi-objective evolutionary algorithm for minimizing maximum lateness and total pollution cost on non-identical parallel batch processing machines. Comput. Ind. Eng. 172 (2022) 108608. [CrossRef] [Google Scholar]
- C.F. Liu, A hybrid genetic algorithm to minimize total tardiness for unrelated parallel machine scheduling with precedence constraints. Math. Probl. Eng. 2013 (2013) 537127. [Google Scholar]
- G. Lucarelli, B. Moseley, N.K. Thang, A. Srivastav and D. Trystram, Online non-preemptive scheduling on unrelated machines with rejections. ACM Trans. Parallel Comput. 8 (2021) 1–22. [CrossRef] [Google Scholar]
- L. Luo, Z. Zhang and Y. Yin, Modelling and numerical analysis of seru loading problem under uncertainty. Eur. J. Ind. Eng. 11 (2017) 185–204. [CrossRef] [Google Scholar]
- B. Mor, Single machine scheduling problems involving job-dependent step-deterioration dates and job rejection. Oper. Res. 23 (2023) 10. [Google Scholar]
- G. Mosheiov and A. Sarig, A common due-date assignment problem with job rejection on parallel uniform machines. Int. J. Prod. Res. 62 (2023) 2083–2092. [Google Scholar]
- F. Pargar, M. Zandieh, O. Kauppila and J. Kujala, The effect of worker learning on scheduling jobs in a hybrid flow shop: a bi-objective approach. J. Syst. Sci. Syst. Eng. 27 (2018) 265–291. [CrossRef] [Google Scholar]
- S.G. Ponnambalam, V. Ramkumar and N. Jawahar, A multiobjective genetic algorithm for job shop scheduling. Prod. Plan. Control 12 (2001) 764–774. [CrossRef] [Google Scholar]
- V. Portougal and D. Trietsch, Setting due dates in a stochastic single machine environment. Comput. Oper. Res. 33 (2006) 1681–1694. [CrossRef] [Google Scholar]
- M. Reisi-Nafchi and G. Moslehi, Two-agent order acceptance and scheduling to maximise total revenue. Eur. J. Ind. Eng. 9 (2015) 664–691. [CrossRef] [Google Scholar]
- A.P. Rifai, H.T. Nguyen and S.Z.M. Dawal, Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling. Appl. Soft Comput. 40 (2016) 42–57. [CrossRef] [Google Scholar]
- J.E. Schaller, Minimizing total tardiness for scheduling identical parallel machines with family setups. Comput. Ind. Eng. 72 (2014) 274–281. [CrossRef] [Google Scholar]
- D. Shabtay, N. Gaspar and L. Yedidsion, A bicriteria approach to scheduling a single machine with job rejection and positional penalties. J. Comb. Optim. 23 (2012) 395–424. [CrossRef] [MathSciNet] [Google Scholar]
- Y.R. Shiau, M.S. Tsai, W.C. Lee and T.C.E. Cheng, Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time. Comput. Ind. Eng. 87 (2015) 580–589. [CrossRef] [Google Scholar]
- S.A. Slotnick, Order acceptance and scheduling: a taxonomy and review. Eur. J. Oper. Res. 212 (2011) 1–11. [CrossRef] [Google Scholar]
- M. Stevenson, Y. Huang and L.C. Hendry, The development and application of an interactive end-user training tool: part of an implementation strategy for workload control. Prod. Plan. Control 20 (2009) 622–635. [CrossRef] [Google Scholar]
- C.H. Su and J.Y. Wang, A branch-and-bound algorithm for minimizing the total tardiness of multiple developers. Mathematics 10 (2022) 1200. [CrossRef] [Google Scholar]
- C.H. Su and J.Y. Wang, A makespan minimization problem for versatile developers in the game industry. RAIRO:RO 56 (2022) 3895–3913. [CrossRef] [EDP Sciences] [Google Scholar]
- X.Y. Tang, Y. Liu, Z. Zeng and B. Veeravalli, Service cost effective and reliability aware job scheduling algorithm on cloud computing systems. IEEE Trans. Cloud Comput. 11 (2023) 1461–1473. [CrossRef] [Google Scholar]
- S. Thevenin, N. Zufferey and M. Widmer, Order acceptance and scheduling with earliness and tardiness penalties. J. Heuristics 22 (2016) 849–890. [CrossRef] [Google Scholar]
- M.D. Toksari and B. Atalay, Some scheduling problems with job rejection and a learning effect. Comput. J. 66 (2023) 866–872. [CrossRef] [MathSciNet] [Google Scholar]
- J.Y. Wang, Algorithms for minimizing resource consumption over multiple machines with a common due window. IEEE Access 7 (2019) 172136–172151. [CrossRef] [Google Scholar]
- J.Y. Wang, A branch-and-bound algorithm for minimizing the total tardiness of a three-agent scheduling problem considering the overlap effect and environment protection. IEEE Access 7 (2019) 5106–5123. [CrossRef] [Google Scholar]
- Y. Yin, K.E. Stecke, M. Swink and I. Kaku, Lessons from seru production on manufacturing competitively in a high cost environment. J. Oper. Manag. 49–51 (2017) 67–76. [CrossRef] [Google Scholar]
- R. Zhang, S.J. Song and C. Wu, A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem. Int. J. Adv. Manuf. Technol. 67 (2013) 5–17. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.