Baker, K.R., A comparative study of flow-shop algorithms. Operations Research 23:1 (1975), 62–73, 10.1287/opre.23.1.62.
Bendjoudi, A., Melab, N., Talbi, E.-G., Hierarchical branch and bound algorithm for computational grids. Future Generation Computer Systems 28:8 (2012), 1168–1176, 10.1016/j.future.2012.03.001.
Brown, A.P.G., Lomnicki, Z.A., Some applications of the “branch-and-bound” algorithm to the machine scheduling problem. Journal of the Operational Research Society 17:2 (1966), 173–186, 10.1057/jors.1966.25.
de Bruin, A., Kindervater, G.A.P., Trienekens, H.W.J.M., Asynchronous parallel branch and bound and anomalies. Ferreira, A., Rolim, J., (eds.) Parallel algorithms for irregularly structured problems, 1995, Springer Berlin Heidelberg, Berlin, Heidelberg, 363–377, 10.1007/3-540-60321-2_29.
Carlier, J., Rebaï, I., Two branch and bound algorithms for the permutation flow shop problem. European Journal of Operational Research 90:2 (1996), 238–251, 10.1016/0377-2217(95)00352-5.
Chakroun, I., Melab, N., Mezmaz, M., Tuyttens, D., Combining multi-core and GPU computing for solving combinatorial optimization problems. Journal of Parallel and Distributed Computing 73:12 (2013), 1563–1577, 10.1016/j.jpdc.2013.07.023.
Cheng, J., Kise, H., Matsumoto, H., A branch-and-bound algorithm with fuzzy inference for a permutation flowshop scheduling problem. European Journal of Operational Research 96:3 (1997), 578–590, 10.1016/S0377-2217(96)00083-5.
Cheng, J., Kise, H., Steiner, G., Stephenson, P., Branch-and-bound algorithms using fuzzy heuristics for solving large-scale flow-shop scheduling problems. Verdegay, J.-L., (eds.), 2003, Springer Berlin Heidelberg, Berlin, Heidelberg, 21–35).
Companys, R., Mateo, M., Different behaviour of a double branch-and-bound algorithm on FM|PRMU|CMAX and FM|block|CMAX problems. Computers and Operations Research 34:4 (2007), 938–953, 10.1016/j.cor.2005.05.018.
Daouri, M., Escobar, F.A., Xin Chang, Valderrama, C., A hardware architecture for the branch and bound flow-shop scheduling algorithm. Proceedings of the 2015 Norchip international symposium on system-on-chip (SOC) nordic circuits and systems conference (NORCAS):, 2015, 1–4, 10.1109/NORCHIP.2015.7364362.
Drozdowski, M., Marciniak, P., Pawlak, G., Plaza, M., Grid branch-and-bound for permutation flowshop. Wyrzykowski, R., Dongarra, J.J., Karczewski, K., Wasniewski, J., (eds.) Proceedings of the 9th international conference parallel processing and applied mathematics, PPAM 2011, Torun, Poland, September 11-14, 2011. revised selected papers, part II Lecture Notes in Computer Science, 7204, 2011, Springer, 21–30, 10.1007/978-3-642-31500-8_3.
Dubois-Lacoste, J., Pagnozzi, F., Stützle, T., An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers and Operations Research 81 (2017), 160–166, 10.1016/j.cor.2016.12.021.
Fernandez-Viagas, V., Ruiz, R., Framinan, J.M., 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 (2017), 707–721, 10.1016/j.ejor.2016.09.055.
Framinan, J.M., Gupta, J.N.D., Leisten, R., A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society 55:12 (2004), 1243–1255, 10.1057/palgrave.jors.2601784.
Garey, M.R., Johnson, D.S., Sethi, R., The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research 1:2 (1976), pp.117–129 www.jstor.org/stable/3689278.
Giles, M., Reguly, I., Trends in high-performance computing for engineering calculations. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372(2022), 2014, 20130319, 10.1098/rsta.2013.0319.
Gmys, J., Leroy, R., Mezmaz, M., Melab, N., Tuyttens, D., Work stealing with private integer vector matrix data structure for multi-core branch-and-bound algorithms. Concurrency and Computation: Practice and Experience 28:18 (2016), 4463–4484, 10.1002/cpe.3771.
Gmys, J., Mezmaz, M., Melab, N., Tuyttens, D., A gpu-based branch-and-bound algorithm using integer-vector-matrix data structure. Parallel Computing 59 (2016), 119–139, 10.1016/j.parco.2016.01.008.
Gmys, J., Mezmaz, M., Melab, N., & Tuyttens, D. (2019). Improved upper bounds for permutation flowshop scheduling benchmarks (Taillard and VRF). https://doi.org/10.5281/zenodo.3550553.
Hejazi, S.R., Saghafian, S., Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research 43:14 (2005), 2895–2929, 10.1080/0020754050056417.
Ignall, E., Schrage, L., Application of the branch and bound technique to some flow-shop scheduling problems. Operations Research 13:3 (1965), 400–412, 10.1287/opre.13.3.400.
Jin, Y., Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation 1:2 (2011), 61–70.
Johnson, S.M., Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly 1:1 (1954), 61–68, 10.1002/nav.3800010110.
Kalczynski, P.J., Kamburowski, J., An empirical analysis of the optimality rate of flow shop heuristics. European Journal of Operational Research 198:1 (2009), 93–101, 10.1016/j.ejor.2008.08.021.
Knuth, D., The art of computer programming, Volume 2. Reading, MA, 1997, 192 ISBN=9780201896848.
Ladhari, T., Haouari, M., A computational study of the permutation flow shop problem based on a tight lower bound. Computers and Operations Research 32:7 (2005), 1831–1847, 10.1016/j.cor.2003.12.001.
Lageweg, B.J., Lenstra, J.K., Kan, A.H.G.R., A general bounding scheme for the permutation flow-shop problem. Operations Research 26:1 (1978), 53–67, 10.1287/opre.26.1.53.
Lemesre, J., Dhaenens, C., Talbi, E., An exact parallel method for a bi-objective permutation flowshop problem. European Journal of Operational Research 177:3 (2007), 1641–1655, 10.1016/j.ejor.2005.10.011.
Li, G., Wah, B.W., Coping with anomalies in parallel branch-and-bound algorithms. IEEE Transactions on Computers C-35:6 (1986), 568–573, 10.1109/TC.1986.5009434.
Liu, W., Jin, Y., Price, M., A new improved NEH heuristic for permutation flowshop scheduling problems. International Journal of Production Economics 193 (2017), 21–30, 10.1016/j.ijpe.2017.06.026.
Lomnicki, Z.A., A “branch-and-bound” algorithm for the exact solution of the three-machine scheduling problem. Journal of the Operational Research Society 16:1 (1965), 89–100, 10.1057/jors.1965.7.
McMahon, G.B., Burton, P.G., Flow-shop scheduling with the branch-and-bound method. Operations Research 15:3 (1967), 473–481 http://www.jstor.org/stable/168456.
Melab, N., Gmys, J., Mezmaz, M., Tuyttens, D., Multi-core versus many-core computing for many-task branch-and-bound applied to big optimization problems. Future Generation Computer Systems 82 (2018), 472–481, 10.1016/j.future.2016.12.039.
Mezmaz, M., Leroy, R., Melab, N., Tuyttens, D., A multi-core parallel branch-and-bound algorithm using factorial number system. Proceedings of the 2014 IEEE 28th international parallel and distributed processing symposium, 2014, 1203–1212, 10.1109/IPDPS.2014.124.
Mezmaz, M., Leroy, R., Melab, N., Tuyttens, D., A multi-core parallel branch-and-bound algorithm using factorial number system. Proceedings of the 2014 IEEE 28th international parallel and distributed processing symposium, 2014, 1203–1212, 10.1109/IPDPS.2014.124.
Mezmaz, M., Melab, N., Talbi, E.G., A grid-enabled branch and bound algorithm for solving challenging combinatorial optimization problems. Proceedings of the 2007 IEEE international parallel and distributed processing symposium, Long Beach, CA, 2007, 1–9.
Nabeshima, I., On bound of makespans and its application in m machine scheduling problem. Journal of the Operations Research Society of Japan 9:3-4 (1967), 98–+.
Nawaz, M., Enscore, E.E., Ham, I., A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11:1 (1983), 91–95, 10.1016/0305-0483(83)90088-9.
Potts, C., An adaptive branching rule for the permutation flow-shop problem. European Journal of Operational Research 5:1 (1980), 19–25, 10.1016/0377-2217(80)90069-7.
Potts, C.N., Strusevich, V.A., Fifty years of scheduling: a survey of milestones. Journal of the Operational Research Society 60:sup1 (2009), S41–S68, 10.1057/jors.2009.2.
Ritt, M., A branch-and-bound algorithm with cyclic best-first search for the permutation flow shop scheduling problem. Proceedings of the IEEE international conference on automation science and engineering, CASE 2016, Fort Worth, TX, USA, August 21–25, 2016, 2016, IEEE, 872–877, 10.1109/COASE.2016.7743493.
Rossi, F.L., Nagano, M.S., Neto, R.F.T., Evaluation of high performance constructive heuristics for the flow shop with makespan minimization. The International Journal of Advanced Manufacturing Technology 87:1 (2016), 125–136, 10.1007/s00170-016-8484-9.
Ruiz, R., Maroto, C., Alcaraz, J., Two new robust genetic algorithms for the flowshop scheduling problem. Omega 34:5 (2006), 461–476, 10.1016/j.omega.2004.12.006.
Ruiz, R., Stützle, T., A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research 177:3 (2007), 2033–2049, 10.1016/j.ejor.2005.12.009.
Sutton, R.S., Barto, A.G., et al. Introduction to reinforcement learning, 135, 1998, MIT Press, Cambridge.
Szwarc, W., Optimal elimination methods in the m × n flow-shop scheduling problem. Operations Research 21:6 (1973), 1250–1259, 10.1287/opre.21.6.1250.
Taillard, E., Benchmarks for basic scheduling problems. European Journal of Operational Research 64:2 (1993), 278–285, 10.1016/0377-2217(93)90182-M.
Taillard, E. (2015). Flow shop sequencing: Summary of best known lower and upper bounds of Taillard's instances. http://mistic.heig-vd.ch/taillard/problemes.dir/ordonnancement.dir/flowshop.dir/best_lb_up.txt.
Vallada, E., Ruiz, R., Framinan, J.M., New hard benchmark for flowshop scheduling problems minimising makespan. European Journal of Operational Research 240:3 (2015), 666–677, 10.1016/j.ejor.2014.07.033.
Vu, T.-T., Derbel, B., Parallel branch-and-bound in multi-core multi-cpu multi-GPU heterogeneous environments. Future Generation Computer Systems 56 (2016), 95–109, 10.1016/j.future.2015.10.009.