Free Access
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S1165 - S1193
Published online 02 March 2021
  • Aryanezhad, M. B., Deljoo, V., & Mirzapour Al-e-Hashem, S. M. J., Dynamic cell formation and the worker assignment problem: a new model. Int. J. Adv. Manuf. Tech. 41 (2009) 329. [Google Scholar]
  • Askin, R. G., & Huang, Y., Forming effective worker teams for cellular manufacturing. Int. J. Prod. Res. 39 (2001) 2431–2451. [Google Scholar]
  • A. Banharnsakun, B. Sirinaovakul and T. Achalakul, Job shop scheduling with the best-so-far ABC. Eng. App. Artif. Intell. 25 (2012) 583–593. [Google Scholar]
  • A. Baykasoğlu, A. Hamzadayi and S.Y. Köse, Testing the performance of teaching–learning based optimization (TLBO) algorithm on combinatorial problems: flow shop and job shop scheduling cases. Inf. Sci. 276 (2014) 204–218. [Google Scholar]
  • J.C. Chen, C.-C. Wu, C.-W. Chen and K.-H. Chen, Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm. Expert Syst. App. 39 (2012) 10016–10021. [Google Scholar]
  • T.-K. Dao, T.-S. Pan and J.-S. Pan, Parallel bat algorithm for optimizing makespan in job shop scheduling problems. J. Intell. Manuf. 29 (2018) 451–462. [Google Scholar]
  • A. Delgoshaei and C. Gomes, A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Appl. Soft Comput. 49 (2016) 27–55. [Google Scholar]
  • A. Delgoshaei, M.K. Ariffin, B. Baharudin and Z. Leman, A backward approach for maximizing net present value of multi-mode pre-emptive resource-constrained project scheduling problem with discounted cash flows using simulated annealing algorithm. Int. J. Ind. Eng. Manage. 5 (2014) 151–158. [Google Scholar]
  • A. Delgoshaei, M. Ariffin, B. Baharudin and Z. Leman, Minimizing makespan of a resource-constrained scheduling problem: a hybrid greedy and genetic algorithms. Int. J. Ind. Eng. Comput. 6 (2015) 503–520. [Google Scholar]
  • Y. Demir and S.K. İşleyen, Evaluation of mathematical models for flexible job-shop scheduling problems. Appl. Math. Modell. 37 (2013) 977–988. [Google Scholar]
  • K.-Z. Gao, P.N. Suganthan, Q.-K. Pan, T.J. Chua, T.X. Cai and C.-S. Chong, Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Inf. Sci. 289 (2014) 76–90. [Google Scholar]
  • H. Garg, A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274 (2016) 292–305. [Google Scholar]
  • H. Garg, Performance analysis of an industrial system using soft computing based hybridized technique. J. Braz. Soc. Mech. Sci. Eng. 39 (2017) 1441–1451. [Google Scholar]
  • H. Garg, A hybrid GSA-GA algorithm for constrained optimization problems. Inf. Sci. 478 (2019) 499–523. [Google Scholar]
  • H. Garg and S. Sharma, Multi-objective reliability-redundancy allocation problem using particle swarm optimization. Comput. Ind. Eng. 64 (2013) 247–255. [Google Scholar]
  • A. Hamidinia, S. Khakabimamaghani, M.M. Mazdeh and M. Jafari, A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Comput. Ind. Eng. 62 (2012) 29–38. [Google Scholar]
  • S.K. Hasan, R. Sarker, D. Essam and I. Kacem, A DSS for job scheduling under process interruptions. Flexible Serv. Manuf. J. 23 (2011) 137. [Google Scholar]
  • B. Jinsong, H. Xiaofeng and J. Ye, A genetic algorithm for minimizing makespan of block erection in shipbuilding. J. Manuf. Technol. Manage. 20 (2009) 500–512. [Google Scholar]
  • R.M. Karp, Reducibility among combinatorial problems. In: 50 Years of Integer Programming 1958–2008. Springer, Berlin-Heidelberg (2010) 219–241. [Google Scholar]
  • S. Karthikeyan, P. Asokan, S. Nickolas and T. Page, A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems. Int. J. Bio-Inspired Comput. 7 (2015) 386–401. [Google Scholar]
  • D. Lei, Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling. Appl. Soft Comput. 12 (2012) 2237–2245. [Google Scholar]
  • J.-Q. Li, Q.-K. Pan and M.F. Tasgetiren, A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Appl. Mathe. Modell. 38 (2014) 1111–1132. [Google Scholar]
  • J. Li, Q. Pan and S. Xie, An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl. Math. Comput. 218 (2012) 9353–9371. [Google Scholar]
  • H. Luo, G.Q. Huang, Y. Zhang, Q. Dai and X. Chen, Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm. Robotics and Computer-Integrated Manufacturing 25 (2009) 962–971. [Google Scholar]
  • S. Mahdavi, H. Kermanian and A. Varshoei, Comparison of mechanical properties of date palm fiber-polyethylene composite. BioResources 5 (2010) 2391–2403. [Google Scholar]
  • I. Mahdavi, A. Aalaei, M.M. Paydar and M.A. Solimanpur, A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. J. Manuf. Syst. 31 (2012) 214–223. [Google Scholar]
  • S. Malve and R. Uzsoy, A genetic algorithm for minimizing maximum lateness on parallel identical batch processing machines with dynamic job arrivals and incompatible job families. Comput. Oper. Res. 34 (2007) 3016–3028. [Google Scholar]
  • S. Meeran and M. Morshed, A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. J. Intell. Manuf. 23 (2012) 1063–1078. [Google Scholar]
  • S. Nguyen, M. Zhang, M. Johnston and K.C. Tan, Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Trans. Evol. Comput. 18 (2014) 193–208. [Google Scholar]
  • M. Nouiri, A. Bekrar, A. Jemai, S. Niar and A.C. Ammari, An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29 (2018) 603–615. [Google Scholar]
  • R.S. Patwal, N. Narang and H. Garg, A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units. Energy 142 (2018) 822–837. [Google Scholar]
  • B. Peng, Z. Lü and T. Cheng, A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput. Oper. Res. 53 (2015) 154–164. [Google Scholar]
  • S.H.A. Rahmati and M. Zandieh, A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 58 (2012) 1115–1129. [Google Scholar]
  • A. Rossi, Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships. Int. J. Prod. Econ. 153 (2014) 253–267. [Google Scholar]
  • M. Saidi-Mehrabad, S. Dehnavi-Arani, F. Evazabadian and V. Mahmoodian, An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput. Ind. Eng. 86 (2015) 2–13. [Google Scholar]
  • S.M. Sajadi, A. Alizadeh, M. Zandieh and F. Tavan, Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach. Int. J. Math. Oper. Res. 14 (2019) 268–289. [Google Scholar]
  • H. Shah, N. Tairan, H. Garg and R. Ghazali, Global Gbest guided-artificial bee colony algorithm for numerical function optimization. Computers 7 (2018) 69. [Google Scholar]
  • X. Shao, W. Liu, Q. Liu and C. Zhang, Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 67 (2013) 2885–2901. [Google Scholar]
  • X.-N. Shen and X. Yao, Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Inf. Sci. 298 (2015) 198–224. [Google Scholar]
  • G.A. Suer and A.A. Cedeño, A configuration-based clustering algorithm for family formation. Comput. Ind. Eng. 31 (1996) 147–150. [Google Scholar]
  • Y. Wang, A new hybrid genetic algorithm for job shop scheduling problem. Comput. Oper. Res. 39 (2012) 2291–2299. [Google Scholar]
  • L. Wang, G. Zhou, Y. Xu, S. Wang and M. Liu, An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 60 (2012) 303–315. [Google Scholar]
  • Y. Xu, L. Wang, S.-Y. Wang and M. Liu, An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148 (2015) 260–268. [Google Scholar]
  • Y. Yuan and H. Xu, Multiobjective flexible job shop scheduling using memetic algorithms. IEEE Trans. Autom. Sci. Eng. 12 (2015) 336–353. [Google Scholar]
  • Y. Yuan, H. Xu and J. Yang, A hybrid harmony search algorithm for the flexible job shop scheduling problem. Appl. Soft Comput. 13 (2013) 3259–3272. [Google Scholar]
  • R. Zhang and R. Chiong, Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. J. Cleaner Prod. 112 (2016) 3361–3375. [Google Scholar]
  • X. Zhang, Y. Deng, F.T. Chan, P. Xu, S. Mahadevan and Y. Hu, IFSJSP: a novel methodology for the job-shop scheduling problem based on intuitionistic fuzzy sets. Int. J. Prod. Res. 51 (2013) 5100–5119. [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.