Issue
RAIRO-Oper. Res.
Volume 55, Number 1, January-February 2021
Operations Research and Mathematical Programming (dedicated to Prof. Alain Quilliot)
Page(s) 27 - 50
DOI https://doi.org/10.1051/ro/2020055
Published online 03 March 2021
  • J.B. Abikarram, K. McConky and R. Proano, Energy cost minimization for unrelated parallel machine scheduling under real time and demand charge pricing. J. Clean. Prod. 208 (2019) 232–242. [Google Scholar]
  • F. Ahmadizar and M.H. Farahani, A novel hybrid genetic algorithm for the open shop scheduling problem. Int. J. Adv. Manuf. Technol. 62 (2012) 775–787. [Google Scholar]
  • A. Al-Ahmari, A. Ur-Rehman and S. Ali, Decision support system for the selection of advanced manufacturing technologies. J. Eng. Res. 4 (2016) 130–150. [Google Scholar]
  • D. Alisantoso, L.P. Khoo and P.Y. Jiang, An immune algorithm approach to the scheduling of a flexible PCB flow shop. Int. J. Adv. Manuf. Technol. 22 (2003) 819–827. [Google Scholar]
  • A. Allahverdi and T. Aldowaisan, No-wait flowshops with bicriteria of makespan and total completion time. J. Oper. Res. Soc. 53 (2002) 1004–1015. [Google Scholar]
  • A. Allahverdi, H. Aydilek and A. Aydilek, No-wait flowshop scheduling problem with two criteria; total tardiness and makespan. Eur. J. Oper. Res. 269 (2018) 590–601. [Google Scholar]
  • A. Allahverdi, H. Aydilek and A. Aydilek, No-wait flowshop scheduling problem with separate setup times to minimize total tardiness subject to makespan. Appl. Math. Comput. 365 (2020) 124688. [Google Scholar]
  • H. Allaou and A. Artiba, Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints. Comput. Ind. Eng. 47 (2004) 431–450. [Google Scholar]
  • S. Ashour, A branch-and-bound algorithm for flow shop scheduling problems. J. AIIE Trans. 2 (1970) 172–176. [Google Scholar]
  • M. Babaei, M. Mohammadi and S.M.T.F. Ghomi, A genetic algorithm for the simultaneous lot sizing and scheduling problem in capacitated flow shop with complex setups and backlogging. Int. J. Adv. Manuf. Technol. 70 (2014) 125–134. [Google Scholar]
  • D. Bai, J. Liang, B. Liu, M. Tang and Z.H. Zhang, Permutation flow shop scheduling problem to minimize nonlinear objective function with release dates. Comput. Ind. Eng. 112 (2017) 336–347. [Google Scholar]
  • D. Bai, M. Tang, Z.H. Zhang and D.R.S.G. Ernesto, Flow shop learning effect scheduling problem with release dates. Omega 78 (2018) 21–38. [Google Scholar]
  • J. Behnamian, Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function, Int. J. Adv. Manuf. Technol. 74 (2014) 267–283. [Google Scholar]
  • I. Benkalai, D. Rebaine, C. Gagné and P. Baptiste, The migrating birds optimization metaheuristic for the permutation flow shop with sequence-dependent setup times. IFAC-PapersOnLine 49 (2016) 408–413. [Google Scholar]
  • M. Bessedik, F.B.S. Tayeb, H. Cheurfi and A. Blizak, An immunity-based hybrid genetic algorithms for permutation flowshop scheduling problems. Int. J. Adv. Manuf. Technol. 85 (2016) 2459–2469. [Google Scholar]
  • L.A. Bewoor, V.C. Prakash and S.U. Sapkal, Production scheduling optimization in foundry using hybrid particle swarm optimization algorithm. Proc. Manuf. 22 (2017) 57–64. [Google Scholar]
  • A. Biele and L. Monch, Decomposition methods for cost and tardiness reduction in aircraft manufacturing flow lines. Comput. Oper. Res. 103 (2019) 134–147. [Google Scholar]
  • W. Bozejko, M. Uchronski and M. Wodecki, Parallel metaheuristics for the cyclic flow shop scheduling problem. Comput. Ind. Eng. 95 (2016) 156–163. [Google Scholar]
  • W. Bozejko, Z. Hejducki and M. Wodecki, Flowshop scheduling of construction processes with uncertain parameters. Arch. Civil Mech. Eng. 19 (2019) 194–204. [Google Scholar]
  • A.P.J. Brown and Z.A. Lomnicki, Some applications of the branch-and-bound algorithm to the machine scheduling problem. Oper. Res. Soc. 17 (1966) 173–186. [Google Scholar]
  • M. Bultmann, S. Knust and S. Waldherr, Synchronous flow shop scheduling with pliable jobs. Eur. J. Oper. Res. 270 (2018) 943–956. [Google Scholar]
  • H.G. Campbell, R.A. Dudek and M.L. Smith, A heuristic algorithm for the n Job, m machine sequencing problem. Manage. Sci. 16 (1970) 630–637. [Google Scholar]
  • C.Y. Cheng, K.C. Ying, S.F. Li and Y.C. Hsieh, Minimizing makespan in mixed no-wait flowshops with sequence-dependent setup times. Comput. Ind. Eng. 130 (2019) 338–347. [Google Scholar]
  • H.M. Cho and I.J. Jeong, A two-level method of production planning and scheduling for bi-objective reentrant hybrid flow shop. Comput. Ind. Eng. 106 (2017) 174–181. [Google Scholar]
  • R.W. Conway, W.L. Maxwell and L.W. Miller, Theory of Scheduling. Addison-Wesley Publishing, Reading, MA (1967). [Google Scholar]
  • F.D. Croce, A. Grosso and F. Salassa, Minimizing total completion time in the two-machine no-idle no-wait flow shop problem. J. Heuristics (2019) 1–15. DOI: 10.1007/s10732-019-09430-z. [Google Scholar]
  • R.L. Daniels and J.B. Mazzola, A tabu-search heuristic for the flexible-resource flow shop scheduling problem. Ann. Oper. Res. 41 (1993) 207–230. [Google Scholar]
  • G. Deng and X. Gu, A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion. Comput. Oper. Res. 39 (2012) 2152–2160. [Google Scholar]
  • J. Deng and L. Wang, A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm Evol. Comput. 32 (2017) 121–131. [Google Scholar]
  • E. Dhouib, J. Teghem and T. Loukil, Non-permutation flowshop scheduling problem with minimal and maximal time lags: theoretical study and heuristic. Ann. Oper. Res. 267 (2018) 101–134. [Google Scholar]
  • J.Y. Ding, S. Song and C. Wu, Carbon-efficient scheduling of flow shops by multi-objective optimization. Eur. J. Oper. Res. 248 (2016) 758–771. [Google Scholar]
  • M. Dios, V. Fernandez-Viagas and J.M. Framinan, Efficient heuristics for the hybrid flow shop scheduling problem with missing operations. Comput. Ind. Eng. 115 (2018) 88–99. [Google Scholar]
  • R. Dubey and A. Gunasekaran, Strategic Management of Sustainable Manufacturing Operation. IGI Global, Hersheys, PE (2016). [Google Scholar]
  • R.G. Dyson, Maximin programming, fuzzy linear programming and multi-criteria decision making. J. Oper. Res. Soc. 31 (1980) 263–267. [Google Scholar]
  • M. Ebrahimi, S.M.T.F. Ghomi and B. Karimi, Hybrid flow shop scheduling with sequence-dependent family setup time and uncertain due dates. Appl. Math. Model. 38 (2013) 2490–2504. [Google Scholar]
  • O. Engin and A. Guclu, A new hybrid ant colony optimization algorithm for solving the no-wait flow shop scheduling problems. Appl. Soft Comput. 72 (2018) 166–176. [Google Scholar]
  • C. Fan, Y. Song and Q. Pei, Project schedule with alternative activities and relationships. J. Eng. Res. 5 (2017) 30–49. [Google Scholar]
  • P. Fattahi, S.M.H. Hosseini and F. Jolai, A mathematical model and extension algorithm for assembly flexible flow shop scheduling problem. Int. J. Adv. Manuf. Technol. 65 (2013) 787–802. [Google Scholar]
  • A. Ferrer, D. Guimarans, H. Ramalhinho and A.A. Juan, A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs. Expert Syst. App. 44 (2016) 177–186. [Google Scholar]
  • G.B. Fonseca, T.H. Nogueira and M.G. Ravetti, A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem. Eur. J. Oper. Res. 275 (2019) 139–154. [Google Scholar]
  • Y. Fu, H. Wang, G. Tian, Z. Li and H. Hu, Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm. J. Intell. Manuf. 30 (2018) 2257–2272. [Google Scholar]
  • J. Fung, Y. Zinder and G. Singh, Flexible flow shop with storage: complexity and optimisation methods. IFAC-PapersOnLine 49 (2016) 237–242. [Google Scholar]
  • K. Govindan, R. Balasundaram, N. Baskar and P. Asokan, A hybrid approach for minimizing makespan in permutation flowshop scheduling. J. Syst. Sci. Syst. Eng. 26 (2017) 50–76. [Google Scholar]
  • J. Grabowski and J. Pempera, New block properties for the permutation flow shop problem with application in tabu search. J. Oper. Res. Soc. 52 (2001) 210–220. [Google Scholar]
  • J. Grabowski, E. Skubalska and C. Smutnicki, On flow shop scheduling with release and due dates to minimize maximum lateness. J. Oper. Res. Soc. 34 (1983) 615–620. [Google Scholar]
  • R.L. Graham, E.L. Lawler, J.K. Lenstra and A.H.G. Kan, Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Disc. Math. 5 (1979) 287–326. [Google Scholar]
  • A. Gunasekaran, T. Martikainen and P. Yli-Olli, Flexible manufacturing systems: an investigation for research and applications. Eur. J. Oper. Res. 66 (1993) 1–26. [Google Scholar]
  • J.N.D. Gupta, Flowshop schedules with sequence dependent setup times. J. Oper. Res. 29 (1986) 206–219. [Google Scholar]
  • J.N.D. Gupta and A.M.A. Hariri, Two-machine flowshop scheduling to minimize the number of tardy jobs. J. Oper. Res. Soc. 48 (1997) 212–220. [Google Scholar]
  • W. Han and P. Dejax, An efficient heuristic based on machine workload for the flowshop scheduling problem with setup and removal. Ann. Oper. Res. 50 (1994) 263–279. [Google Scholar]
  • Y. Han, D. Gong, Y. Jin and Q.K. Pan, Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time. Appl. Soft Comput. 42 (2016) 229–245. [Google Scholar]
  • A. Janiak and M.C. Portmann, Genetic algorithm for the permutation flow-shop scheduling problem with linear models of operations. Ann. Oper. Res. 83 (1998) 95–114. [Google Scholar]
  • S.M. Johnson, Optimal two and three stage production schedules with setup times included. Nav. Res. Logist. 1 (1953) 61–68. [Google Scholar]
  • S. Karabati, P. Kouvelis and A.S. Kiran, Games, critical paths and assignment problems in permutation flow shops and cyclic scheduling flow line environments. J. Oper. Res. Soc. 43 (1992) 241–258. [Google Scholar]
  • D. Khorasanian and G. Moslehi, Two-machine flow shop scheduling problem with blocking, multi-task flexibility of the first machine, and preemption. Comput. Oper. Res. 79 (2017) 94–108. [Google Scholar]
  • H. Kia, S.H. Ghodsypour and H. Davoudpour, New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times. J. Ind. Eng. Int. 13 (2017) 297–306. [Google Scholar]
  • H.J. Kim and J.H. Lee, Three-machine flow shop scheduling with overlapping waiting time constraints. Comput. Oper. Res. 101 (2019) 93–102. [Google Scholar]
  • J.M. Kim, Y.D. Zhou and D.H. Lee, Priority scheduling to minimize the total tardiness for remanufacturing systems with flow-shop-type reprocessing lines. Int. J. Adv. Manuf. Technol. 91 (2017) 3697–3708. [Google Scholar]
  • G.M. Komaki, E. Teymourian, V. Kayvanfar and Z. Booyavi, Improved discrete cuckoo optimization algorithm for the three-stage assembly flowshop scheduling problem. Comput. Ind. Eng. 105 (2017) 158–173. [Google Scholar]
  • P. Kouvelis, R.L. Daniels and G. Vairaktarakis, Robust scheduling of a two-machine flow shop with uncertain processing times. IIE Trans. 32 (2000) 421–432. [Google Scholar]
  • M. Kurdi, Ant colony system with a novel non-daemonactions procedure for multiprocessor task scheduling in multistage hybrid flow shop. Swarm Evol. Comput. 44 (2019) 987–1002. [Google Scholar]
  • J.L. Lalitha, N. Mohan and V.M. Pillai, Lot streaming in [N − 1](1) + N(m) hybrid flow shop. J. Manuf. Syst. 44 (2017) 12–21. [Google Scholar]
  • J.Y. Lee, Y.D. Kim and T.E. Lee, Minimizing total tardiness on parallel machines subject to flexible maintenance. Int. J. Ind. Eng.: Theory App. Pract. 25 (2018) 472–489. [Google Scholar]
  • K. Lee, F. Zheng and M.L. Pinedo, Online scheduling of ordered flow shops. Eur. J. Oper. Res. 272 (2019) 50–60. [Google Scholar]
  • D. Lei and Y. Zheng, Hybrid flow shop scheduling with assembly operations and key objectives: a novel neighborhood search. Appl. Soft Comput. 61 (2017) 122–128. [Google Scholar]
  • Y. Li and G. Hu, Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency. Comput. Ind. Eng. 111 (2017) 263–271. [Google Scholar]
  • X. Li, Z. Yang, R. Ruiz, T. Chen and S. Sui, An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Inf. Sci. 453 (2018) 408–425. [Google Scholar]
  • C.J. Liao, Minimizing the number of machine idle intervals with minimum makespan in a flow-shop. J. Oper. Res. Soc. 44 (1993) 817–824. [Google Scholar]
  • C.J. Liao, E. Tjandradjaja and T.P. Chung, An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Appl. Soft Comput. 12 (2012) 1755–1764. [Google Scholar]
  • G.E. Liepins and M.R. Hilliard, Genetic algorithms: foundations and applications. Ann. Oper. Res. 21 (1989) 31–57. [Google Scholar]
  • K.S. Lin, Hybrid algorithm for sequencing with bicriteria. J. Optim. Theory App. 39 (1983) 105–124. [Google Scholar]
  • S.W. Lin and K.C. Ying, Optimization of makespan for no-wait flowshop scheduling problems using efficient matheuristics. Omega 64 (2016) 115–125. [Google Scholar]
  • J. Lin, Z.J. Wang and X. Li, A Backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem. Swarm Evol. Comput. 36 (2017) 124–135. [Google Scholar]
  • Y. Liu and Z. Feng, Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times. Comput. Ind. Eng. 75 (2014) 170–175. [Google Scholar]
  • G. Liu, P.B. Luh and R. Resch, Scheduling permutation flow shops using the Lagrangian relaxation technique. Ann. Oper. Res. 70 (1997) 171–189. [Google Scholar]
  • G.S. Liu, Y. Zhou and H.D. Yang, Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. J. Clean. Prod. 147 (2017) 470–484. [Google Scholar]
  • W. Liu, Y. Jin and M. Price, New meta-heuristic for dynamic scheduling in permutation flowshop with new order arrival. Int. J. Adv. Manuf. Technol. 98 (2018) 1817–1830. [Google Scholar]
  • Z.A. Lomnicki, A branch-and-bound algorithm for the exact solution of the three-machine scheduling problem. J. Oper. Res. Soc. 16 (1965) 89–100. [Google Scholar]
  • C. Low, J.Y. Yeh and K.I. Huang, A robust simulated annealing heuristic for flow shop scheduling problems. Int. J. Adv. Manuf. Technol. 23 (2004) 762–767. [Google Scholar]
  • M.K. Marichelvam, O. Tosun and M. Geetha, Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time. Appl. Soft Comput. 55 (2017) 82–92. [Google Scholar]
  • M.K. Marichelvam, M. Geetha and O. Tosun, An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors – a case study. Comput. Oper. Res. 114 (2020) 104812. [Google Scholar]
  • O. Masmoudi, A. Yalaoui, Y. Ouazene and H. Chehade, Multi-item capacitated lot-sizing problem in a flow-shop system with energy consideration. IFAC Papers Proc. 49 (2016) 301–306. [Google Scholar]
  • G.B. McMohan and P.G. Burton, Flow-shop scheduling with the branch-and-bound method. Oper. Res. 15 (1967) 373–589. [Google Scholar]
  • R.G.R. Mercado and J.F. Bard, A branch-and-bound algorithm for permutation flow shops with sequence-dependent setup times. IIE Trans. 31 (1999) 721–731. [Google Scholar]
  • R.G.R. Mercado and J.F. Bard, An enhanced TSP-based heuristic for makespan minimization in a flow shop with a setup times. J. Heuristics 5 (1999) 53–70. [Google Scholar]
  • G. Minella, R. Ruiz and M. Ciavotta, A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. Informs J. Comput. 20 (2008) 451–471. [Google Scholar]
  • Y. Minyi, H. Jiye and C. Yongmao, Elimination conditions and lower bounds for the permutation flow shop scheduling problem. Acta Math. Appl. Sin. 2 (1985) 321–331. [Google Scholar]
  • M. Mirabi, A novel hybrid genetic algorithm to solve the sequence-dependent permutation flow-shop scheduling problem. Int. J. Adv. Manuf. Technol. 71 (2014) 429–437. [Google Scholar]
  • A. Mishra and D. Shrivastava, A TLBO and a Jaya heuristics for permutation flow shop scheduling to minimize the sum of inventory holding and batch delay costs. Comput. Ind. Eng. 124 (2018) 509–522. [Google Scholar]
  • H.H. Miyata, M.S. Nagano and J.N.D. Gupta, Integrating preventive maintenance activities to the no-wait flow shop scheduling problem with dependence-sequence setup times and makespan minimization. Comput. Ind. Eng. 135 (2019) 79–104. [Google Scholar]
  • V. Modrak, P. Semanco and P. Knuth, Alternative constructive heuristic algorithm for permutation flow shop scheduling problem with makespan criterion. Int. J. Ind. Eng.: Theory App. Pract. 19 (2012) 289–296. [Google Scholar]
  • K. Momaya, Strategic flexibility for competitiveness. Global J. Flexible Syst. Manage. 3 (2002) III. [Google Scholar]
  • M.F. Morais, M.G. Filho and T.J.P. Boiko, Hybrid flow shop scheduling problems involving setup considerations: a literature review and analysis. Int. J. Ind. Eng.: Theory App. Pract. 20 (2013) 614–630. [Google Scholar]
  • S.M. Mousavi, M. Mousakhani and M. Zandieh, Bi-objective hybrid flow shop scheduling: a new local search. Int. J. Adv. Manuf. Technol. 64 (2013) 933–950. [Google Scholar]
  • M.S. Nagano and J.V. Moccellin, A high-quality solution constructive heuristic for flow shop sequencing. J. Oper. Res. Soc. 53 (2002) 1374–1379. [Google Scholar]
  • M.S. Nagano, A.A. Silva and L.A.N. Lorena, An evolutionary clustering search for the no-wait flow shop problem with sequence dependent setup times. Expert Syst. App. 41 (2014) 3628–2633. [Google Scholar]
  • M.S. Nagano, F.L. Rossi and C.P. Tomazella, A new efficient heuristic method for minimizing the total tardiness in a no-idle permutation flow shop. Prod. Eng. 11 (2017) 523–529. [Google Scholar]
  • A. Nagar, S.S. Heragu and J. Haddock, A branch-and-bound approach for a two-machine flowshop scheduling problem. J. Oper. Res. Soc. 46 (1995) 721–734. [Google Scholar]
  • A. Nagar, S.S. Heragu and J. Haddock, A combined branch-and-bound and genetic algorithm based approach for a flowshop scheduling problem. Ann. Oper. Res. 63 (1996) 397–414. [Google Scholar]
  • J. Navaei, S.M.T.F. Ghomi, F. Jolai and A. Mozdgir, Heuristics for an assembly flow-shop with non-identical assembly machines and sequence dependent setup times to minimize sum of holding and delay costs. Comput. Oper. Res. 44 (2014) 52–65. [Google Scholar]
  • Z.H.A.O. Ning, Y.E. Song, L.I. Kaidian and C.H.E.N. Siyu, Effective iterated greedy algorithm for flow-shop scheduling problems with time lags. Chin. J. Mech. Eng. 30 (2017) 652–662. [Google Scholar]
  • N. Nouri and T. Ladhari, Evolutionary multi-objective optimization for the multi-machine flow shop scheduling problem under blocking. Ann. Oper. Res. 267 (2017) 413–430. [Google Scholar]
  • C. Ozturk and M.A. Ornek, Optimization and constraint based heuristic methods for advanced planning and scheduling systems. Int. J. Ind. Eng.: Theory App. Pract. 23 (2016) 26–48. [Google Scholar]
  • D.S. Palmer, Sequencing jobs through a multi-stage process in the minimum total time – a quick method of obtaining a near optimum. J. Oper. Res. Soc. 16 (1965) 101–107. [Google Scholar]
  • Q.K. Pan and L. Wang, No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm. Int. J. Adv. Manuf. Technol. 39 (2008) 796–807. [Google Scholar]
  • Q.K. Pan, P.N. Suganthan, J.J. Liang and M.F. Tasgetiren, A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem. Expert Syst. App. 38 (2011) 3252–3259. [Google Scholar]
  • Q.K. Pan, L. Wang and L. Gao, A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers. Appl. Soft Comput. 11 (2011) 5270–5280. [Google Scholar]
  • Q.K. Pan, K. Gao, L. Wang, J. Liang and X.Y. Li, Effective heuristics and meta-heuristics to minimize total flowtime for the distributed permutation flowshop problem. Expert Syst. App. 124 (2019) 309–324. [Google Scholar]
  • S.S. Panwalkar, Scheduling of a two-machine flowshop with travel time between machines. J. Oper. Res. Soc. 42 (1991) 609–613. [Google Scholar]
  • S.S. Panwalkar and C.R. Woollam, Ordered flow shop problems with no in-process waiting. J. Oper. Res. Soc. 31 (1980) 1039–1043. [Google Scholar]
  • L.S. Pessoa and C.E. Andrade, Heuristics for a flowshop scheduling problem with stepwise job objective function. Eur. J. Oper. Res. 266 (2018) 950–962. [Google Scholar]
  • M.L. Pinedo, Scheduling: Theory, Algorithms, and Systems. 3rd edition, edited by M.L. Pinedo. Springer, New York, NY (2008). [Google Scholar]
  • M.L. Pinedo, Scheduling: Theory, Algorithms, and Systems. 4th edition, edited by M.L. Pinedo. Springer, New York, NY (2012). [Google Scholar]
  • C.N. Potts, An adaptive branching rule for the permutation flow-shop problem. Eur. J. Oper. Res. 5 (1980) 19–25. [Google Scholar]
  • R. Pugazhenthi and A. Xavior, A genetic algorithm applied heuristic to minimize the makespan in a flow shop. Proc. Eng. 97 (2014) 1735–1744. [Google Scholar]
  • S. Pugazhendhi, S. Thiagarajan, C. Rajendran and N. Anantharaman, Generating non-permutation schedules in flowline-based manufacturing systems with sequence-dependent setup times of jobs: a heuristic approach. Int. J. Adv. Manuf. Technol. 23 (2004) 64–78. [Google Scholar]
  • H. Qin, P. Fan, H. Tang, P. Huang, B. Fang and S. Pan, An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Comput. Ind. Eng. 128 (2019) 458–476. [Google Scholar]
  • G. Rabadi, Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edited by G. Rabadi, In Vol 236 of International Series in Operation Research and Management Science, Springer, New York, NY (2016) 127–140. [Google Scholar]
  • C. Rajendran, A no-wait flow shop scheduling heuristic to minimize makespan. J. Oper. Res. Soc. 45 (1994) 472–478. [Google Scholar]
  • R. Ramezanian, S. Mohammadi and A. Cheraghalikhani, Toward an integrated modeling approach for production and delivery operations in flow shop system: trade-off between direct and routing delivery methods. J. Manuf. Syst. 44 (2017) 79–92. [Google Scholar]
  • S.S. Reddi and C.V. Ramamoorthy, On the flow-shop scheduling problem with no-wait in process. J. Oper. Res. Soc. 23 (1972) 323–331. [Google Scholar]
  • V. Riahi and M. Kazemi, A new hybrid ant colony algorithm for scheduling of no-wait flowshop. Oper. Res. 18 (2016) 55–74. [Google Scholar]
  • I. Ribas, R. Companys and X. Tort-Martorell, Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Syst. App. 74 (2017) 41–54. [Google Scholar]
  • I. Ribas, R. Companys and X.T. Martorell, An iterated greedy algorithm for solving the total tardiness parallel blocking flow shop scheduling problem. Expert Syst. App. 121 (2019) 347–361. [Google Scholar]
  • J. Riezebos, G.J. Gaalman and J.N. Gupta, Flow shop scheduling with multiple operations and time lags. J. Intell. Manuf. 6 (1995) 105–115. [Google Scholar]
  • H. Rock, Some new results in flow shop scheduling. Z. Oper. Res. 28 (1984) 1–16. [Google Scholar]
  • M.A. Salido, J. Escamilla, A. Giret and F. Barber, A genetic algorithm for energy-efficiency in job-shop scheduling. Int. J. Adv. Manuf. Technol. 85 (2016) 1303–1314. [Google Scholar]
  • H. Samarghandi and M. Behroozi, An enumeration algorithm for the no-wait flow shop problem with due date constraints. IFAC-PapersOnLine 49 (2016) 1803–1808. [Google Scholar]
  • H. Samarghandi and M. Behroozi, On the exact solution of the no-wait flow shop problem with due date constraints. Comput. Oper. Res. 81 (2017) 41–159. [Google Scholar]
  • S.U. Sapkal and D. Laha, A heuristic for no-wait flow shop scheduling. Int. J. Adv. Manuf. Technol. 68 (2013) 1327–1338. [Google Scholar]
  • O. Shahvari and R. Logendran, Hybrid flow shop batching and scheduling with a bi-criteria objective. Int. J. Prod. Econ. 179 (2016) 239–258. [Google Scholar]
  • O. Shahvari and R. Logendran, A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning effect. Int. J. Prod. Econ. 195 (2018) 227–248. [Google Scholar]
  • W. Shao and D. Pi, A self-guided differential evolution with neighborhood search for permutation flow shop scheduling. Expert Syst. App. 51 (2016) 161–176. [Google Scholar]
  • W. Shao, D. Pi and Z. Shao, A hybrid discrete optimization algorithm based on teaching–probabilistic learning mechanism for no-wait flow shop scheduling. Knowl. Based Syst. 107 (2016) 219–234. [Google Scholar]
  • W. Shao, D. Pi and Z. Shao, An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem. Appl. Soft Comput. 61 (2017) 193–210. [Google Scholar]
  • W. Shao, D. Pi and Z. Shao, Memetic algorithm with node and edge histogram for no-idle flow shop scheduling problem to minimize the makespan criterion. Appl. Soft Comput. 54 (2017) 164–182. [Google Scholar]
  • Z. Shao, D. Pi and W. Shao, Self-adaptive discrete invasive weed optimization for the blocking flow-shop scheduling problem to minimize total tardiness. Comput. Ind. Eng. 111 (2017) 331–351. [Google Scholar]
  • Z. Shao, D. Pi, W. Shao and P. Yuan, An efficient discrete invasive weed optimization for blocking flow-shop scheduling problem. Eng. App. Artif. Intell. 78 (2019) 124–141. [Google Scholar]
  • Z. Shao, D. Pi and W. Shao, A novel multi-objective discrete water wave optimization for solving multi-objective blocking flow-shop scheduling problem. Knowl. Based Syst. 165 (2019) 110–131. [Google Scholar]
  • O.P. Sharma and P. Sushil, Issues in managing manufacturing flexibility. Global J. Flexible Syst. Manage. 3 (2002) 11–29. [Google Scholar]
  • M. Sheikhalishahi, N. Eskandari, A. Mashayekhi and A. Azadeh, Multi-objective open shop scheduling by considering human error and preventive maintenance. Appl. Math. Model. 67 (2019) 573–587. [Google Scholar]
  • A. Sioud and C. Gagne, Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times. Eur. J. Oper. Res. 264 (2018) 66–73. [Google Scholar]
  • H. Singh, J.S. Oberoi and D. Singh, Decision-making approaches and heuristics algorithms for multi-objective flow shop scheduling. Int. J. Res. Eng. App. Manage. 5 (2019) 60–66. [Google Scholar]
  • H. Singh, J.S. Oberoi and D. Singh, Optimizing a multi-objective flow shop scheduling problem by meta-heuristic approach. Int. J. Res. Eng. App. Manage. 5 (2019) 20–26. [Google Scholar]
  • R.D. Smith and R.A. Dudek, A general algorithm for solution of the n-job, M-machine sequencing problem of the flow shop. Oper. Res. 15 (1967) 71–82. [Google Scholar]
  • C. Smutnicki, A two-machine permutation flow shop scheduling problem with buffers. OR Spectr. 20 (1998) 229–235. [Google Scholar]
  • Y.N. Sotskov, T. Tautenhahn and F. Werner, Heuristics for permutation flow shop scheduling with batch setup times. OR Spectr. 18 (1996) 67–80. [Google Scholar]
  • V.A. Strusevich and C.M. Zwaneveld, On non-permutation solutions to some two machine flow shop scheduling problems. Z. Oper. Res. 39 (1994) 305–319. [Google Scholar]
  • W. Sukkerd and T. Wuttipornpun, Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations. Comput. Ind. Eng. 97 (2016) 157–169. [Google Scholar]
  • W. Szwarc, The clustered flow-shop problem. J. Oper. Res. 32 (1988) 315–322. [Google Scholar]
  • W. Szwarc and J.J. Liu, An approximate solution of the flow-shop problem with sequence dependent setup times. J. Oper. Res. 33 (1989) 439–451. [Google Scholar]
  • B. Tadayon and N. Salmasi, A two-criteria objective function flexible flow shop scheduling problem with machine eligibility constraint. Int. J. Adv. Manuf. Technol. 64 (2013) 1001–1015. [Google Scholar]
  • M. Tandon, P.T. Cummings and M.D. LeVan, Flowshop sequencing with non-permutation schedules. Comput. Chem. Eng. 15 (1991) 601–607. [Google Scholar]
  • M.K. Tiwari and N.K. Vidyarthi, Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach. Int. J. Prod. Res. 38 (2000) 3357–3384. [Google Scholar]
  • V. T’kindt and J.C. Billaut, Multicriteria Scheduling: Theory, Models and Algorithms, 2nd edition, edited by V. T’kindt and J.C. Billaut. Springer-Verlag, Berlin-Heidelberg (2005). [Google Scholar]
  • V. Tkindt, N. Monmarche, F. Tercinet and D. Laugt, An ant colony optimization algorithm to solve a 2-machine bicriteria flowshop scheduling problem. Eur. J. Oper. Res. 142 (2002) 250–257. [Google Scholar]
  • M. Torkashvand, B. Naderi and S.A. Hosseini, Modelling and scheduling multi-objective flow shop problems with interfering jobs. Appl. Soft Comput. 54 (2017) 221–228. [Google Scholar]
  • M. Urgo, A branch-and-bound approach to schedule a no-wait flow shop to minimize the CVaR of the residual work content. Comput. Ind. Eng. 129 (2019) 67–75. [Google Scholar]
  • J.A.A. Veen and R. Dal, Solvable cases of the no-wait flow shop scheduling problem. J. Oper. Res. Soc. 22 (1991) 971–980. [Google Scholar]
  • S. Wang and M. Liu, A genetic algorithm for two-stage no-wait hybrid flow shop scheduling problem. Comput. Oper. Res. 40 (2013) 1064–1075. [Google Scholar]
  • S.Y. Wang, L. Wang, M. Liu and Y. Xu, An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. Int. J. Prod. Econ. 145 (2013) 387–396. [Google Scholar]
  • X.Y. Wang, Z. Zhou, X. Zhang, P. Ji and J.B. Wang, Several flow shop scheduling problems with truncated position-based learning effect. Comput. Oper. Res. 40 (2013) 2906–2929. [Google Scholar]
  • X. Wu and A. Che, Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search. Omega 94 (2019) 102117. [Google Scholar]
  • Y. Xiao, Y. Yuan, R.Q. Zhang and A. Konak, Non-permutation flow shop scheduling with order acceptance and weighted tardiness. Appl. Math. Comput. 270 (2015) 312–333. [Google Scholar]
  • B. Yagmahan and M.M. Yenisey, A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Syst. App. 37 (2010) 1361–1368. [Google Scholar]
  • J. Yan, L. Li, F. Zhao, F. Zhang and Q. Zhao, A multi-level optimization approach for energy-efficient flexible flowshop scheduling. J. Clean. Prod. 137 (2016) 1543–1552. [Google Scholar]
  • S. Yanai and T. Fujie, A three-machine permutation flow-shop problem with minimum makespan on the second machine. J. Oper. Res. Soc. 57 (2006) 460–468. [Google Scholar]
  • H. Ye, W. Li and A. Abedini, An improved heuristic for no-wait flow shop to minimize makespan. J. Manuf. Syst. 44 (2017) 273–279. [Google Scholar]
  • K.C. Ying, Solving non-permutation flowshop scheduling problems by an effective iterated greedy heuristic. Int. J. Adv. Manuf. Technol. 38 (2008) 348–354. [Google Scholar]
  • K.C. Ying and S.W. Lin, Multi-heuristic desirability ant colony system heuristic for non-permutation flowshop scheduling problems. Int. J. Adv. Manuf. Technol. 33 (2007) 793–802. [Google Scholar]
  • K.C. Ying and S.W. Lin, Minimizing makespan for no-wait flowshop scheduling problems with setup times. Comput. Ind. Eng. 121 (2018) 73–81. [Google Scholar]
  • Y. Yip, C.Y. Cheng and C. Low, Sequencing of an M machine flow shop with setup, processing and removal times separated. Int. J. Adv. Manuf. Technol. 30 (2006) 286–296. [Google Scholar]
  • A.J. Yu and J. Seif, Minimizing tardiness and maintenance costs in flow shop scheduling by a lower-bound-based GA. Comput. Ind. Eng. 97 (2016) 26–40. [Google Scholar]
  • C. Yu, Q. Semeraro and A. Matta, A genetic algorithm for the hybrid flow shop scheduling with unrelated machines and machine eligibility. Comput. Oper. Res. 100 (2018) 211–229. [Google Scholar]
  • Y. Zhai, K. Biel, F. Zhao and J.W. Sutherland, Dynamic scheduling of a flow shop with on-site wind generation for energy cost reduction under real-time electricity pricing. CIRP Ann. Manuf. Technol. 66 (2017) 41–44. [Google Scholar]
  • F. Zhao, Y. Liu, Y. Zhang, W. Ma and C. Zhang, A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems. Eng. App. Artif. Intell. 65 (2017) 178–199. [Google Scholar]
  • F. Zhao, H. Liu, Y. Zhang, W. Ma and C. Zhang, A discrete water wave optimization algorithm for no-wait flow shop scheduling problems. Expert Syst. App. 91 (2018) 347–363. [Google Scholar]
  • F. Zhao, L. Zhang, H. Liu, Y. Zhang, W. Ma, C. Zhang and H. Song, An improved water wave optimization algorithm with the single wave mechanism for the no-wait flow-shop scheduling problem. J. Eng. Optim. 51 (2018) 1727–1742. [Google Scholar]
  • F. Zhao, S. Qin, G. Yang, W. Ma, C. Zhang and H. Song, A factorial based particle swarm optimization with a population adaptation mechanism for the no-wait flow shop scheduling problem with the makespan objective. Expert Syst. App. 126 (2019) 41–53. [Google Scholar]
  • F. Zhao, S. Qin, Y. Zhang, W. Ma, C. Zhang and H. Song, A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem. Expert Syst. App. 126 (2019) 321–339. [Google Scholar]
  • H. Zhonghua, Z. Boqiu, L. Hao and G. Wei, Bat algorithm for flexible flow shop scheduling with variable processing time. In: Vol. 690 of Part of the Advances in Intelligent Systems and Computing Book Series. In ICMIR’ 17: Proceedings of International Conference on Mechatronics and Intelligent Robotics. (AISC) 1 (2017) 164–171. https://link.springer.com/conference/icmir. [Google Scholar]
  • A. Ziaeifar, R. Tavakkoli-Moghaddam and K. Pichka, Solving a new mathematical model for a hybrid flow shop scheduling problem with a processor assignment by a genetic algorithm. Int. J. Adv. Manuf. Technol. 61 (2012) 339–349. [Google Scholar]
  • G.I. Zobolas, C.D. Tarantilis and G. Ioannou, Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm. Comput. Oper. Res. 36 (2009) 1249–1267. [Google Scholar]
  • H. Zohali, B. Naderi, M. Mohammadi and V. Roshanaei, Reformulation, linearization, and a hybrid iterated local search algorithm for economic lot-sizing and sequencing in hybrid flow shop problems. Comput. Oper. Res. 104 (2019) 127–138. [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.