How to cite this paper
Tasgetiren, M., Kizilay, D & Kandiller, L. (2024). Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms.Journal of Project Management, 9(2), 85-100.
Refrences
Aqil, S., & Allali, K. (2021). Two efficient nature inspired meta-heuristics solving blocking hybrid flow shop manufacturing problem. Engineering Applications of Artificial Intelligence, 100, 104196. https://doi.org/10.1016/j.engappai.2021.104196
Birgin, E. G., Ferreira, J. E., & Ronconi, D. P. (2020). A filtered beam search method for the m-machine permutation flowshop scheduling problem minimizing the earliness and tardiness penalties and the waiting time of the jobs. Computers and Operations Research, 114, 104824. https://doi.org/10.1016/j.cor.2019.104824
Blazewicz, J., H. Ecker, K., Pesch, E., Schmidt, G., & Wȩglarz, J. (2007). Handbook on scheduling. From theory to applications. International Handbook on Information Systems. https://doi.org/10.1007/978-3-540-32220-7
Caraffa, V., Ianes, S., P. Bagchi, T., & Sriskandarajah, C. (2001). Minimizing makespan in a blocking flowshop using genetic algorithms. International Journal of Production Economics, 70(2), 101–115. https://doi.org/10.1016/S0925-5273(99)00104-8
Carlier, J., Haouari, M., Kharbeche, M., & Moukrim, A. (2010). An optimization-based heuristic for the robotic cell problem. European Journal of Operational Research, 202(3), 636–645. https://doi.org/10.1016/j.ejor.2009.06.035
Chen, H., Zhou, S., Li, X., & Xu, R. (2014). A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking. International Journal of Production Research, 52(19), 5714–5734. https://doi.org/10.1080/00207543.2014.910625
Cheng, C.-Y., Ying, K.-C., Chen, H.-H., & Lu, H.-S. (2019). Minimising makespan in distributed mixed no-idle flowshops. International Journal of Production Research, 57(1), 48–60. https://doi.org/10.1080/00207543.2018.1457812
Dubois-Lacoste, J., Pagnozzi, F., & Stützle, T. (2017). An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers & Operations Research, 81, 160–166. https://doi.org/10.1016/J.COR.2016.12.021
Elmi, A., & Topaloglu, S. (2013). A scheduling problem in blocking hybrid flow shop robotic cells with multiple robots. Computers and Operations Research, 40(10), 2543–2555. https://doi.org/10.1016/j.cor.2013.01.024
Fernandez-Viagas, V., Ruiz, R., & Framinan, J. M. (2017). A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. European Journal of Operational Research, 257(3), 707–721. https://doi.org/10.1016/J.EJOR.2016.09.055
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The Complexity of Flowshop and Jobshop Scheduling. Math. Oper. Res., 1(2), 117–129. https://doi.org/10.1287/moor.1.2.117
Gilmore, P. C., Lawler, E. L., & Shmoys, D. B. (1984). Well-solved Special Cases of the Traveling Salesman Problem. University of California at Berkeley.
Glover, F. (1990). Tabu search—part II. ORSA Journal on Computing, 2, 4–32. https://doi.org/10.1287/ijoc.2.1.4
Gong, D., Han, Y., & Sun, J. (2018). A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowledge-Based Systems, 148, 115–130. https://doi.org/10.1016/j.knosys.2018.02.029
Gong, H., Tang, L., & Duin, C. W. (2010). A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times. Computers and Operations Research, 37(5), 960–969. https://doi.org/10.1016/j.cor.2009.08.001
Hall, N. G., & Sriskandarajah, C. (1996). A Survey of Machine Scheduling Problems with Blocking and No-Wait in Process. Oper. Res., 44(3), 510–525. https://doi.org/10.1287/opre.44.3.510
Han, Y.-Y., Gong, D., & Sun, X. (2015). A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Engineering Optimization, 47(7), 927–946. https://doi.org/10.1080/0305215X.2014.928817
Han, Y.-Y., Liang, J. J., Pan, Q.-K., Li, J.-Q., Sang, H.-Y., & Cao, N. N. (2013). Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem. The International Journal of Advanced Manufacturing Technology, 67(1), 397–414. https://doi.org/10.1007/s00170-012-4493-5
Han, Y.-Y., Pan, Q.-K., Li, J.-Q., & Sang, H. (2012). An improved artificial bee colony algorithm for the blocking flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 60(9), 1149–1159. https://doi.org/10.1007/s00170-011-3680-0
Han, Y., Gong, D., Li, J., & Zhang, Y. (2016). Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm. International Journal of Production Research, 54(22), 6782–6797. https://doi.org/10.1080/00207543.2016.1177671
Han, Y., Li, J., Sang, H., Liu, Y., Gao, K., & Pan, Q. (2020). Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time. Applied Soft Computing Journal, 93, 106343. https://doi.org/10.1016/j.asoc.2020.106343
Johnson, S. M. (1954). Optimal Two and Three Stage Production Schedules With Set-Up Time Included. Naval Research Logistics Quarterly, 1, 61–68. https://doi.org/10.1002/nav.3800010110
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. J. Artif. Int. Res., 4(1), 237–285.
Karimi-Mamaghan, M., Mohammadi, M., Pasdeloup, B., & Meyer, P. (2022). Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.03.054
Kizilay, D., Tasgetiren, M. F., Pan, Q. K., & Gao, L. (2019). A variable block insertion heuristic for solving permutation flow shop scheduling problem with makespan criterion. Algorithms, 12(5). https://doi.org/10.3390/a12050100
Mccormick, S., Pinedo, M., J. Shenker, S., & Wolf, B. (1989). Sequencing in an Assembly Line With Blocking to Minimize Cycle Time. Operations Research, 37, 925–935. https://doi.org/10.1287/opre.37.6.925
Merchan, A. F., & Maravelias, C. T. (2016). Preprocessing and tightening methods for time-indexed MIP chemical production scheduling models. Computers and Chemical Engineering, 84, 516–535. https://doi.org/10.1016/j.compchemeng.2015.10.003
Miyata, H. H., & Nagano, M. S. (2019). The blocking flow shop scheduling problem: A comprehensive and conceptual review. In Expert Systems with Applications (Vol. 137, pp. 130–156). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2019.06.069
Naderi, B., & Ruiz, R. (2010). The distributed permutation flowshop scheduling problem. Computers & Operations Research, 37(4), 754–768. https://doi.org/10.1016/J.COR.2009.06.019
Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91–95. https://doi.org/10.1016/0305-0483(83)90088-9
Newton, M. A. H., Riahi, V., Su, K., & Sattar, A. (2019). Scheduling blocking flowshops with setup times via constraint guided and accelerated local search. Computers & Operations Research, 109, 64–76. https://doi.org/10.1016/J.COR.2019.04.024
Osman, I., & N. Potts, C. (1989). Simulated Annealing for Permutation Flow-Shop Scheduling. Omega, 17, 551–557. https://doi.org/10.1016/0305-0483(89)90059-5
Öztop, H, Tasgetiren, M. F., Kandiller, L., & Pan, Q.-K. (2020). A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185556
Öztop, Hande, Tasgetiren, M. F., Kandiller, L., & Pan, Q.-K. (2022). Metaheuristics with restart and learning mechanisms for the no-idle flowshop scheduling problem with makespan criterion. Computers & Operations Research, 138, 105616. https://doi.org/https://doi.org/10.1016/j.cor.2021.105616
Pan, Q.-K., & Ruiz, R. (2012). An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega, 40(2), 166–180. https://doi.org/10.1016/J.OMEGA.2011.05.002
Ramezanian, R., Vali-Siar, M. M., & Jalalian, M. (2019). Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study. International Journal of Production Research, 57(10), 3311–3333. https://doi.org/10.1080/00207543.2019.1581955
Riahi, V., Newton, M. A. H., Su, K., & Sattar, A. (2019). Constraint guided accelerated search for mixed blocking permutation flowshop scheduling. Computers & Operations Research, 102, 102–120. https://doi.org/10.1016/J.COR.2018.10.003
Ribas, I., & Companys, R. (2015). Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization. Computers & Industrial Engineering, 87, 30–39. https://doi.org/https://doi.org/10.1016/j.cie.2015.04.013
Ribas, I., Companys, R., & Tort-Martorell, X. (2015). An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization. Expert Systems with Applications, 42(15), 6155–6167. https://doi.org/https://doi.org/10.1016/j.eswa.2015.03.026
Ribas, I., Companys, R., & Tort-Martorell, X. (2017). Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Systems with Applications, 74, 41–54. https://doi.org/10.1016/j.eswa.2017.01.006
Ribas, I., Companys, R., & Tort-Martorell, X. (2019). An iterated greedy algorithm for solving the total tardiness parallel blocking flow shop scheduling problem. Expert Systems with Applications, 121, 347–361. https://doi.org/10.1016/j.eswa.2018.12.039
Ronconi, D P, & Armentano, V. A. (2001). Lower bounding schemes for flowshops with blocking in-process. Journal of the Operational Research Society, 52(11), 1289–1297. https://doi.org/10.1057/palgrave.jors.2601220
Ronconi, Débora P. (2004). A note on constructive heuristics for the flowshop problem with blocking. International Journal of Production Economics, 87(1), 39–48. https://doi.org/10.1016/S0925-5273(03)00065-3
Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033–2049. https://doi.org/https://doi.org/10.1016/j.ejor.2005.12.009
Shao, Z., Pi, D., & Shao, W. (2018a). Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem. Engineering Optimization, 50(5), 894–916. https://doi.org/10.1080/0305215X.2017.1353090
Shao, Z., Pi, D., & Shao, W. (2018b). A novel discrete water wave optimization algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 40, 53–75. https://doi.org/10.1016/j.swevo.2017.12.005
Shao, Z., Pi, D., & Shao, W. (2020). Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Expert Systems with Applications, 145, 113147. https://doi.org/https://doi.org/10.1016/j.eswa.2019.113147
Shao, Z., Pi, D., Shao, W., & Yuan, P. (2019). An efficient discrete invasive weed optimization for blocking flow-shop scheduling problem. Engineering Applications of Artificial Intelligence, 78, 124–141. https://doi.org/10.1016/j.engappai.2018.11.005
Shao, Z., Shao, W., & Pi, D. (2020). Effective heuristics and metaheuristics for the distributed fuzzy blocking flow-shop scheduling problem. Swarm and Evolutionary Computation, 59, 100747. https://doi.org/10.1016/j.swevo.2020.100747
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Birgin, E. G., Ferreira, J. E., & Ronconi, D. P. (2020). A filtered beam search method for the m-machine permutation flowshop scheduling problem minimizing the earliness and tardiness penalties and the waiting time of the jobs. Computers and Operations Research, 114, 104824. https://doi.org/10.1016/j.cor.2019.104824
Blazewicz, J., H. Ecker, K., Pesch, E., Schmidt, G., & Wȩglarz, J. (2007). Handbook on scheduling. From theory to applications. International Handbook on Information Systems. https://doi.org/10.1007/978-3-540-32220-7
Caraffa, V., Ianes, S., P. Bagchi, T., & Sriskandarajah, C. (2001). Minimizing makespan in a blocking flowshop using genetic algorithms. International Journal of Production Economics, 70(2), 101–115. https://doi.org/10.1016/S0925-5273(99)00104-8
Carlier, J., Haouari, M., Kharbeche, M., & Moukrim, A. (2010). An optimization-based heuristic for the robotic cell problem. European Journal of Operational Research, 202(3), 636–645. https://doi.org/10.1016/j.ejor.2009.06.035
Chen, H., Zhou, S., Li, X., & Xu, R. (2014). A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines with arbitrary release times and blocking. International Journal of Production Research, 52(19), 5714–5734. https://doi.org/10.1080/00207543.2014.910625
Cheng, C.-Y., Ying, K.-C., Chen, H.-H., & Lu, H.-S. (2019). Minimising makespan in distributed mixed no-idle flowshops. International Journal of Production Research, 57(1), 48–60. https://doi.org/10.1080/00207543.2018.1457812
Dubois-Lacoste, J., Pagnozzi, F., & Stützle, T. (2017). An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers & Operations Research, 81, 160–166. https://doi.org/10.1016/J.COR.2016.12.021
Elmi, A., & Topaloglu, S. (2013). A scheduling problem in blocking hybrid flow shop robotic cells with multiple robots. Computers and Operations Research, 40(10), 2543–2555. https://doi.org/10.1016/j.cor.2013.01.024
Fernandez-Viagas, V., Ruiz, R., & Framinan, J. M. (2017). A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. European Journal of Operational Research, 257(3), 707–721. https://doi.org/10.1016/J.EJOR.2016.09.055
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The Complexity of Flowshop and Jobshop Scheduling. Math. Oper. Res., 1(2), 117–129. https://doi.org/10.1287/moor.1.2.117
Gilmore, P. C., Lawler, E. L., & Shmoys, D. B. (1984). Well-solved Special Cases of the Traveling Salesman Problem. University of California at Berkeley.
Glover, F. (1990). Tabu search—part II. ORSA Journal on Computing, 2, 4–32. https://doi.org/10.1287/ijoc.2.1.4
Gong, D., Han, Y., & Sun, J. (2018). A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems. Knowledge-Based Systems, 148, 115–130. https://doi.org/10.1016/j.knosys.2018.02.029
Gong, H., Tang, L., & Duin, C. W. (2010). A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times. Computers and Operations Research, 37(5), 960–969. https://doi.org/10.1016/j.cor.2009.08.001
Hall, N. G., & Sriskandarajah, C. (1996). A Survey of Machine Scheduling Problems with Blocking and No-Wait in Process. Oper. Res., 44(3), 510–525. https://doi.org/10.1287/opre.44.3.510
Han, Y.-Y., Gong, D., & Sun, X. (2015). A discrete artificial bee colony algorithm incorporating differential evolution for the flow-shop scheduling problem with blocking. Engineering Optimization, 47(7), 927–946. https://doi.org/10.1080/0305215X.2014.928817
Han, Y.-Y., Liang, J. J., Pan, Q.-K., Li, J.-Q., Sang, H.-Y., & Cao, N. N. (2013). Effective hybrid discrete artificial bee colony algorithms for the total flowtime minimization in the blocking flowshop problem. The International Journal of Advanced Manufacturing Technology, 67(1), 397–414. https://doi.org/10.1007/s00170-012-4493-5
Han, Y.-Y., Pan, Q.-K., Li, J.-Q., & Sang, H. (2012). An improved artificial bee colony algorithm for the blocking flowshop scheduling problem. The International Journal of Advanced Manufacturing Technology, 60(9), 1149–1159. https://doi.org/10.1007/s00170-011-3680-0
Han, Y., Gong, D., Li, J., & Zhang, Y. (2016). Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm. International Journal of Production Research, 54(22), 6782–6797. https://doi.org/10.1080/00207543.2016.1177671
Han, Y., Li, J., Sang, H., Liu, Y., Gao, K., & Pan, Q. (2020). Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time. Applied Soft Computing Journal, 93, 106343. https://doi.org/10.1016/j.asoc.2020.106343
Johnson, S. M. (1954). Optimal Two and Three Stage Production Schedules With Set-Up Time Included. Naval Research Logistics Quarterly, 1, 61–68. https://doi.org/10.1002/nav.3800010110
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. J. Artif. Int. Res., 4(1), 237–285.
Karimi-Mamaghan, M., Mohammadi, M., Pasdeloup, B., & Meyer, P. (2022). Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.03.054
Kizilay, D., Tasgetiren, M. F., Pan, Q. K., & Gao, L. (2019). A variable block insertion heuristic for solving permutation flow shop scheduling problem with makespan criterion. Algorithms, 12(5). https://doi.org/10.3390/a12050100
Mccormick, S., Pinedo, M., J. Shenker, S., & Wolf, B. (1989). Sequencing in an Assembly Line With Blocking to Minimize Cycle Time. Operations Research, 37, 925–935. https://doi.org/10.1287/opre.37.6.925
Merchan, A. F., & Maravelias, C. T. (2016). Preprocessing and tightening methods for time-indexed MIP chemical production scheduling models. Computers and Chemical Engineering, 84, 516–535. https://doi.org/10.1016/j.compchemeng.2015.10.003
Miyata, H. H., & Nagano, M. S. (2019). The blocking flow shop scheduling problem: A comprehensive and conceptual review. In Expert Systems with Applications (Vol. 137, pp. 130–156). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2019.06.069
Naderi, B., & Ruiz, R. (2010). The distributed permutation flowshop scheduling problem. Computers & Operations Research, 37(4), 754–768. https://doi.org/10.1016/J.COR.2009.06.019
Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91–95. https://doi.org/10.1016/0305-0483(83)90088-9
Newton, M. A. H., Riahi, V., Su, K., & Sattar, A. (2019). Scheduling blocking flowshops with setup times via constraint guided and accelerated local search. Computers & Operations Research, 109, 64–76. https://doi.org/10.1016/J.COR.2019.04.024
Osman, I., & N. Potts, C. (1989). Simulated Annealing for Permutation Flow-Shop Scheduling. Omega, 17, 551–557. https://doi.org/10.1016/0305-0483(89)90059-5
Öztop, H, Tasgetiren, M. F., Kandiller, L., & Pan, Q.-K. (2020). A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185556
Öztop, Hande, Tasgetiren, M. F., Kandiller, L., & Pan, Q.-K. (2022). Metaheuristics with restart and learning mechanisms for the no-idle flowshop scheduling problem with makespan criterion. Computers & Operations Research, 138, 105616. https://doi.org/https://doi.org/10.1016/j.cor.2021.105616
Pan, Q.-K., & Ruiz, R. (2012). An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega, 40(2), 166–180. https://doi.org/10.1016/J.OMEGA.2011.05.002
Ramezanian, R., Vali-Siar, M. M., & Jalalian, M. (2019). Green permutation flowshop scheduling problem with sequence-dependent setup times: a case study. International Journal of Production Research, 57(10), 3311–3333. https://doi.org/10.1080/00207543.2019.1581955
Riahi, V., Newton, M. A. H., Su, K., & Sattar, A. (2019). Constraint guided accelerated search for mixed blocking permutation flowshop scheduling. Computers & Operations Research, 102, 102–120. https://doi.org/10.1016/J.COR.2018.10.003
Ribas, I., & Companys, R. (2015). Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization. Computers & Industrial Engineering, 87, 30–39. https://doi.org/https://doi.org/10.1016/j.cie.2015.04.013
Ribas, I., Companys, R., & Tort-Martorell, X. (2015). An efficient Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with total flowtime minimization. Expert Systems with Applications, 42(15), 6155–6167. https://doi.org/https://doi.org/10.1016/j.eswa.2015.03.026
Ribas, I., Companys, R., & Tort-Martorell, X. (2017). Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Systems with Applications, 74, 41–54. https://doi.org/10.1016/j.eswa.2017.01.006
Ribas, I., Companys, R., & Tort-Martorell, X. (2019). An iterated greedy algorithm for solving the total tardiness parallel blocking flow shop scheduling problem. Expert Systems with Applications, 121, 347–361. https://doi.org/10.1016/j.eswa.2018.12.039
Ronconi, D P, & Armentano, V. A. (2001). Lower bounding schemes for flowshops with blocking in-process. Journal of the Operational Research Society, 52(11), 1289–1297. https://doi.org/10.1057/palgrave.jors.2601220
Ronconi, Débora P. (2004). A note on constructive heuristics for the flowshop problem with blocking. International Journal of Production Economics, 87(1), 39–48. https://doi.org/10.1016/S0925-5273(03)00065-3
Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033–2049. https://doi.org/https://doi.org/10.1016/j.ejor.2005.12.009
Shao, Z., Pi, D., & Shao, W. (2018a). Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem. Engineering Optimization, 50(5), 894–916. https://doi.org/10.1080/0305215X.2017.1353090
Shao, Z., Pi, D., & Shao, W. (2018b). A novel discrete water wave optimization algorithm for blocking flow-shop scheduling problem with sequence-dependent setup times. Swarm and Evolutionary Computation, 40, 53–75. https://doi.org/10.1016/j.swevo.2017.12.005
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