article

Review of job shop scheduling research and its new perspectives under Industry 4.0

Authors:

Jianlin FuAuthors Info & Claims

Journal of Intelligent Manufacturing, Volume 30, Issue 4

Pages 1809 - 1830

https://doi.org/10.1007/s10845-017-1350-2

Published: 01 April 2019 Publication History

Abstract

Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

References

[1]

Aarts, E. H. L., & Lenstra, J. K. (Eds.). (1997). Local search in combinatorial optimization. London: Wiley.

Digital Library

[2]

Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science, 34(3), 391-401.

[3]

Adibi, M. A., Zandieh, M., & Amiri, M. (2010). Multi-objective scheduling of dynamic job shop using variable neighborhood search. Expert Systems with Applications, 37(1), 282-287.

Digital Library

[4]

Akyol, D. E., & Bayhan, G. M. (2007). A review on evolution of production scheduling with neural networks. Computers & Industrial Engineering, 53(1), 95-122.

Digital Library

[5]

Apt, K. (2003). Principles of constraint programming. Cambridge: Cambridge University Press.

Digital Library

[6]

Balas, E., & Vazacopoulos, A. (1998). Guided local search with shifting bottleneck for job shop scheduling. Management Science, 44(2), 262-275.

Digital Library

[7]

Barker, J. R., & McMahon, G. B. (1985). Scheduling the general jobshop. Management Science, 31(5), 594-598.

Digital Library

[8]

Barták, R., Salido, M. A., & Rossi, F. (2010). Constraint satisfaction techniques in planning and scheduling. Journal of Intelligent Manufacturing, 21(1), 5-15.

[9]

Baykasoðlu, A., Hamzadayi, A., & Köse, S. Y. (2014). Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases. Information Sciences, 276, 204-218.

Digital Library

[10]

Blum, C. (2005). Beam-ACO--Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6), 1565-1591.

Digital Library

[11]

Blum, C., & Sampels, M. (2004). An ant colony optimization algorithm for shop scheduling problems. Journal of Mathematical Modelling and Algorithms, 3(3), 285-308.

[12]

Brooks, G. H., & White, C. R. (1965). An algorithm for finding optimal or near optimal solutions to production scheduling problem. Journal of Industrial Engineering, 16(1), 34-40. https://www.researchgate.net/publication/284046228.

[13]

Bullnheimer, B., Hartl, R. F., & Strauss, C. (1999). An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research, 89, 319-328.

[14]

Çaliþ, B., & Bulkan, S. (2015). A research survey: Review of AI solution strategies of job shop scheduling problem. Journal of Intelligent Manufacturing, 26(5), 961-973.

Digital Library

[15]

Canbolat, Y. B., & Gundogar, E. (2004). Fuzzy priority rule for job shop scheduling. Journal of IntelligentManufacturing, 15(4), 527-533.

[16]

Chen, Y.Y., Fu, L. C., & Chen, Y. C. (1998). Multi-agent based dynamic scheduling for a flexible assembly system. In Proceedings, 1998 IEEE International Conference on Robotics and Automation (Vol. 3, pp. 2122-2127). IEEE.

[17]

Chen, J. C., Wu, C. C., Chen, C. W., et al. (2012). Flexible job shop scheduling with parallel machines using genetic algorithm and grouping genetic algorithm. Expert Systems with Applications, 39(11), 10016-10021.

Digital Library

[18]

Cheng, R., Gen, M., & Tsujimura, Y. (1999). A tutorial survey of jobshop scheduling problems using genetic algorithms. Part II: Hybrid genetic search strategies. Computers & Industrial Engineering, 36(2), 343-364.

Digital Library

[19]

Chong, C. S., Sivakumar, A. I., Low, M. Y. H., et al. (2006). A bee colony optimization algorithm to job shop scheduling. In Proceedings of the 38th conference on winter simulation. Winter simulation conference (pp. 1954-1961). http://dl.acm.org/citation.cfm?id=1218469.

Digital Library

[20]

Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (Vol. 142, pp. 134-142). https://www.researchgate.net/publication/216300484.

[21]

Dauzere-Peres, S., & Lasserre, J. B. (1993). A modified shifting bottleneck procedure for job-shop scheduling. The International Journal of Production Research, 31(4), 923-932.

[22]

Davis, L. (1985). Job shop scheduling with genetic algorithms. In Proceedings of an international conference on genetic algorithms and their applications (p. 140). Carnegie-Mellon University, Pittsburgh, PA.

Digital Library

[23]

Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy.

[24]

Falkenauer, E., & Bouffouixm, S. (1991). A genetic algorithm for job shop. In IEEE international conference on robotics and automation, Proceedings, 1991 (pp. 824-829). IEEE.

[25]

Fausett, L. (1994). Fundamentals of neural networks: Architectures, algorithms, and applications. Englewood Cliffs: Prentice-Hall.

Digital Library

[26]

Floudas, C. A., & Lin, X. (2004). Continuous-time versus discretetime approaches for scheduling of chemical processes: A review. Computers & Chemical Engineering, 28(11), 2109-2129.

[27]

Floudas, C. A., & Lin, X. (2005). Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Annals of Operations Research, 139(1), 131-162.

[28]

Fnaiech, N., Hammami, H., Yahyaoui, A., et al. (2012). New Hopfield neural network for joint job shop scheduling of production and maintenance. In IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society (pp. 5535-5541). IEEE.

[29]

Fonseca, D. J., & Navaresse, D. (2002). Artificial neural networks for job shop simulation. Advanced Engineering Informatics, 16(4), 241-246.

[30]

French, S. (1982). Sequencing and scheduling: An introduction to the mathematics of the job-shop. New York: Ellis Horwood.

[31]

Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and job shops cheduling. Mathematics of Operations Research, 1(2), 117-129.

Digital Library

[32]

Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849-866.

Digital Library

[33]

Geyik, F., & Cedimoglu, I. H. (2004). The strategies and parameters of tabu search for job-shop scheduling. Journal of Intelligent Manufacturing, 15(4), 439-448.

[34]

Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5), 533-549.

Digital Library

[35]

Glover, F., & Laguna, M. (2013). Tabu search. New York: Springer. Graves, S. C. (1981). A review of production scheduling. Operations Research, 29(4), 646-675.

Digital Library

[36]

Güçdemir, H., & Selim, H. (2017). Customer centric production planning and control in job shops:Asimulation optimization approach. Journal of Manufacturing Systems, 43, 100-116.

[37]

Harmanani, H. M., & Ghosn, S. B. (2016). An efficient method for the open-shop scheduling problem using simulated annealing (chapter). In Information technology: Newgenerations (pp. 1183-1193). Berlin: Springer.

[38]

Harrison, R. (2016) Engineering the smart factory. Chinese Journal of Mechanical Engineering, 1. http://www.cjmenet.com/CN/article/downloadArticleFile.do?attachType=PDF&id=2905.

[39]

Hefetz, N., & Adiri, I. (1982). An efficient optimal algorithm for the two-machines unit-time jobshop schedule-length problem. Mathematics of Operations Research, 7(3), 354-360.

Digital Library

[40]

Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for Industrie 4.0 scenarios. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 3928-3937). IEEE.

Digital Library

[41]

Hino, R., & Moriwaki, T. (2002). Decentralized job shop scheduling by recursive propagation method. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 45(2), 551-557.

[42]

Huang, S., Tian, N., & Ji, Z. (2016). Particle swarm optimization with variable neighborhood search for multi objective flexible job shop scheduling problem. International Journal of Modeling, Simulation, and Scientific Computing..

[43]

Huang, K. L., & Liao, C. J. (2008). Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research, 35(4), 1030-1046.

Digital Library

[44]

Huang, R. H., Yang, C. L., & Cheng, W. C. (2013). Flexible job shop scheduling with due window--A two-pheromone ant colony approach. International Journal of Production Economics, 141(2), 685-697.

[45]

Huang, R. H., & Yu, T. H. (2017). An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting. Applied Soft Computing, 57, 642-656.

Digital Library

[46]

Ingimundardottir, H., & Runarsson, T. P. (2011). Supervised learning linear priority dispatch rules for job-shop scheduling. In International conference on learning and intelligent optimization (pp. 263-277). Berlin: Springer. http://link.springer.com/chapter/10.1007%2F978-3-642-25566-3_20.

Digital Library

[47]

Iwamura, K., & Sugimura, N. A. (2010). Study on real-time scheduling for autonomous distributed manufacturing systems. In 2010 IEEE international conference on systems man and cybernetics (SMC) (pp. 1352-1357). IEEE.

[48]

Jain, A. S., & Meeran, S. (1998). A state-of-the-art review of job-shop scheduling techniques. http://ftp.bstu.by/ai/To-dom/My_research/Paper-0-again/For-courses/Job-SSP/jain.pdf. Technical report, Department of Applied Physics, Electronic and Mechanical Engineering, University of Dundee, Dundee, Scotland.

[49]

Jalilvand-Nejad, A., & Fattahi, P. (2015). A mathematical model and genetic algorithm to cyclic flexible job shop scheduling problem. Journal of Intelligent Manufacturing, 26(6), 1085-1098.

Digital Library

[50]

Johnson, S. M. (1954). Optimal two and three stage production schedules with setup times included. Naval Research Logistics Quarterly, 1(1), 61-68.

[51]

Jones, D. F., Mirrazavi, S. K., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European Journal of Operational Research, 137(1), 1-9.

[52]

Ju, Q. Y. (2007). Planning and scheduling optimization of job-shop in intelligent manufacturing system (pp. 1-97). Doctoral dissertation of Nanjing University of Aeronautics and Astronautics, Nanjing, China.

[53]

Kagermann, H. (2015). Change through digitization--Value creation in the age of Industry 4.0. Management of permanent change (pp. 23- 45). Springer Fachmedien Wiesbaden, Wiesbaden.

[54]

Kartam, N., & Tongthong, T. (1998). An artificial neural network for resource leveling problems. Ai Edam, 12(3), 273-287.

Digital Library

[55]

Karthikeyan, S., Asokan, P., Nickolas, S., et al. (2015). A hybrid discrete firefly algorithm for solvingmulti-objective flexible job shop scheduling problems. International Journal of Bio-Inspired Computation, 7(6), 386-401.

Digital Library

[56]

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of IEEE international conference on neural network (pp. 1942-1948), Perth.

[57]

Khadwilard, A., Chansombat, S., Thepphakorn, T., Chainate, W., & Pongcharoen, P. (2012). Application of firefly algorithm and its parameter setting for job shop scheduling. The Journal of Industrial Technology, 8(1), 49-58. https://www.researchgate.net/publication/225274007.

[58]

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. http://www.jstor.org/stable/1690046.

[59]

Kuczapski, A. M., Micea, M. V., Maniu, L. A., et al. (2015). Efficient generation of near optimal initial populations to enhance genetic algorithms for job-shop scheduling. Information Technology and Control, 39(1). http://kalbos.ktu.lt/index.php/ITC/article/view/12091/6739.

[60]

Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93-110.

[61]

Lian, L., & Mesghouni, K. (2014). Comparative study of heuristics algorithms in solving flexible job shop scheduling problem with condition based maintenance. Journal of Industrial Engineering and Management, 7(2), 518-531.

[62]

Lin, L., Hao, X. C., Gen, M., et al. (2012). Network modeling and evolutionary optimization for scheduling in manufacturing. Journal of Intelligent Manufacturing, 23(6), 2237-2253.

Digital Library

[63]

Liu, S. Q., & Kozan, E. (2012). A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem. Journal of the Operational Research Society, 63(2), 168-182.

[64]

Lomnicki, Z. A. (1965). A "branch-and-bound" algorithm for the exact solution of the three-machine scheduling problem. Journal of the Operational Research Society, 16(1), 89-100.

[65]

?ukasik, S., & ?ak, S. (2009). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Berlin: Springer.

Digital Library

[66]

Manne, A. S. (1960). On the job-shop scheduling problem. Operations Research, 8(2), 219-223.

Digital Library

[67]

Marichelvam, M. K., & Geetha, M. (2016). A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time. International Journal of Bio-Inspired Computation, 8(5), 318-325.

Digital Library

[68]

Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Transactions on Evolutionary Computation, 18(2), 301-305.

Digital Library

[69]

McMahon, G., & Florian, M. (1975). On scheduling with ready times and due dates to minimize maximum lateness. Operations Research, 23(3), 475-482.

Digital Library

[70]

Meeran, S., & Morshed, M. S. (2012). A hybrid genetic tabu search algorithm for solving job shop scheduling problems: A case study. Journal of Intelligent Manufacturing, 23(4), 1063-1078.

Digital Library

[71]

Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333-346.

Digital Library

[72]

Min, H. S., & Yih, Y. (2003). Selection of dispatching rules on multiple dispatching decision points in real-time scheduling of a semiconductor wafer fabrication system. International Journal of Production Research, 41(16), 3921-3941.

[73]

Morton, T., & Pentico, D. W. (1993). Heuristic scheduling systems: With applications to production systems and project management. London: Wiley.

[74]

Muthiah, A., Rajkumar, A., & Rajkumar, R. (2016). Hybridization of artificial bee colony algorithm with particle swarm optimization algorithm for flexible job shop scheduling. In 2016 international conference on energy efficient technologies for sustainability (ICEETS) (pp. 896-903). IEEE.

[75]

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.

[76]

Neto, R. F. T., & GodinhoFilho, M. (2013). Literature review regarding ant colony optimization applied to scheduling problems: Guidelines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26(1), 150- 161.

Digital Library

[77]

Nouiri, M., Bekrar, A., Jemai, A., et al. (2015). An effective and distributed particle swarm optimization algorithm for flexible jobshop scheduling problem. Journal of Intelligent Manufacturing.

Digital Library

[78]

Nouiri, M., Bekrar, A., Jemai, A., et al. (2017). Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns. Computers & Industrial Engineering.

Digital Library

[79]

Paul, M., Sridharan, R., & Ramanan, T. R. (2016). A multi-objective decision-making framework using preference selection index for assembly job shop scheduling problem. International Journal of Management Concepts and Philosophy, 9(4), 362-387.

[80]

Peng, B., Lü, Z., & Cheng, T. C. E. (2015). A tabu search/path relinking algorithm to solve the job shop scheduling problem. Computers & Operations Research, 53, 154-164.

Digital Library

[81]

Pesch, E., & Tetzlaff, U. A. W. (1996). Constraint propagation based scheduling of job shops. INFORMS Journal on Computing, 8(2), 144-157.

Digital Library

[82]

Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical note, Manufacturing Engineering Center, Cardiff University.

[83]

Ponsich, A., & Coello, C.A.C. (2013). A hybrid differential evolution-- Tabu search algorithm for the solution of job-shop scheduling problems. Applied Soft Computing, 13(1), 462-474.

Digital Library

[84]

Ponsich, A., Tapia, M.G. C., & Coello, C.A. C. (2009). Solving permutation problems with differential evolution: An application to the jobshop scheduling problem. In ISDA (pp. 25-30).

Digital Library

[85]

Potts, C. N., & Van Wassenhove, L. N. (1985). A branch and bound algorithm for the total weighted tardiness problem. Operations Research, 33(2), 363-377.

Digital Library

[86]

Reeves, C. R. (1993). Modern heuristic techniques for combinatorial problems. London: Wiley.

Digital Library

[87]

Rosenkrantz, D. J., Stearns, R. E., & Lewis, P. M, I. I. (1977). An analysis of several heuristics for the traveling salesman problem. SIAM Journal on Computing, 6(3), 563-581.

[88]

Rossi, F., VanBeek, P., & Walsh, T. (2006). Handbook of constraint programming. Amsterdam: Elsevier. http://www.informatik.uni-ulm.de/pm/fileadmin/pm/home/fruehwirth/Papers/FAI.pdf.

Digital Library

[89]

Sadeh, N., & Fox, M. S. (1996). Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem. Artificial Intelligence, 86(1), 1-41.

Digital Library

[90]

Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., et al. (2015). An ant colony algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 86, 2-13.

Digital Library

[91]

Sakawa, M., & Kubota, R. (2000). Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. European Journal of Operational Research, 120(2), 393-407.

[92]

Sarin, S. C., Ahn, S., & Bishop, A. B. (1988). An improved branching scheme for the branch and bound procedure of scheduling n jobs on m parallel machines to minimize total weighted flowtime. The International Journal of Production Research, 26(7), 1183-1191.

[93]

Sayadi, M., Ramezanian, R., & Ghaffari-Nasab, N. (2010). A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations, 1(1), 1-10.

[94]

Shivasankaran, N., Kumar, P. S., & Raja, K. V. (2015). Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. International Journal of Computational Intelligence Systems, 8(3), 455-466.

[95]

Simon, F. Y. P., & Takefuji, Y. (1988). Stochastic neural networks for solving job-shop scheduling. I. Problem representation. In IEEE international conference on neural networks (pp. 275-282). IEEE.

[96]

Singh, M. R., & Mahapatra, S. S. (2016). A quantum behaved particle swarm optimization for flexible job shop scheduling. Computers & Industrial Engineering, 93, 36-44.

Digital Library

[97]

Sotskov, Y. N., Tautenhahn, T., & Werner, F. (1999). On the application of insertion techniques for job shop problems with setup times. RAIRO-Operations Research, 33(2), 209-245.

[98]

Storn, R., & Price, K. (1995). Differential evolution--A simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley: ICSI.

[99]

Teekeng, W., Thammano, A., Unkaw, P., et al. (2016). A new algorithm for flexible job-shop scheduling problem based on particle swarm optimization. Artificial Life and Robotics, 21(1), 18-23.

Digital Library

[100]

Van Dyke Parunak, H., Irish, B.W., Kindrick, J., & Lozo, P.W. (1985). Fractal actors for distributed manufacturing control. In Second conference on artificial intelligence applications: The engineering of knowledge-based systems (pp. 653-660). Miami Beach, FL, USA.

[101]

Van Eck, M. (2003). Advanced planning and scheduling--Is logistics everything? Amsterdam: University of Amsterdam. http://www.docin.com/p-1372360378.html.

[102]

Wagner, H. M. (1959). An integer linearprogramming model for machine scheduling. Naval Research Logistics Quarterly, 6(2), 131-140.

[103]

Wang, L., Cai, J., Li, M., et al. (2017). Flexible job shop scheduling problem using an improved ant colony optimization. Scientific Programming.

Digital Library

[104]

Wang, C., & Jiang, P. (2016). Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. Journal of Intelligent Manufacturing.

Digital Library

[105]

Wenqi, H., & Aihua, Y. (2004). An improved shifting bottleneck procedure for the job shop scheduling problem. Computers & Operations Research, 31(12), 2093-2110.

[106]

Werner, F., & Winkler, A. (1995). Insertion techniques for the heuristic solution of the job shop problem. Discrete Applied Mathematics, 58(2), 191-211.

Digital Library

[107]

Xanthopoulos, A. S., & Koulouriotis, D. E. (2015). Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing. Journal of Intelligent Manufacturing.

Digital Library

[108]

Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409-425.

[109]

Xing, L. N., Chen, Y. W., Wang, P., et al. (2010). A knowledge-based ant colony optimization for flexible job shop scheduling problems. Applied Soft Computing, 10(3), 888-896.

Digital Library

[110]

Xue, H., Zhang, P., & Wei, S. (2015). Applying a hybrid algorithm of immunity and ant colony in job-shop scheduling. In Industrial engineering and manufacturing technology: Proceedings of the 2014 international conference on industrial engineering and manufacturing technology (ICIEMT 2014) (Vol. 4, p. 91), Shanghai, China. CRC Press, 10-11 July 2014.

[111]

Yang, X. S. (2008). Nature-inspired metaheuristic algorithms. Bristol: Luniver Press.

[112]

Yin, L., Yang, L., & Hu, M. (2015). Job shop scheduling based on improved discrete particle swarm optimization. In Proceedings of the 21st international conference on industrial engineering and engineeringmanagement 2014 (pp. 99-101). Amsterdam: Atlantis Press.

[113]

Zahmani, M. H., Atmani, B., Bekrar, A., et al. (2015). Multiple priority dispatching rules for the job shop scheduling problem. In 2015 3rd international conference on control, engineering and information technology (CEIT) (pp. 1-6). IEEE.

[114]

Zandieh, M., Khatami, A. R., & Rahmati, S. H. A. (2017). Flexible job shop scheduling under condition-based maintenance: Improved version of imperialist competitive algorithm. Applied Soft Computing, 58, 449-464.

[115]

Zhang, R., & Chong, R. (2016). 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. Journal of Cleaner Production, 112, 3361-3375.

[116]

Zhang, W., Wen, J. B., Zhu, Y. C., et al. (2017). Multi-objective scheduling simulation of flexible job-shop based on multipopulation genetic algorithm. International Journal of Simulation Modelling (IJSIMM).

[117]

Zhang, H., Yan, Q., Zhang, G., et al. (2016). A chaotic differential evolution algorithm for flexible job shop scheduling. In Asian simulation conference (pp. 79-88). Singapore: Springer.

[118]

Zhao, B., Gao, J., Chen, K., et al. (2015). Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines. Journal of Intelligent Manufacturing.

Digital Library

[119]

Zhao, F., Shao, Z., Wang, J., et al. (2016). A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems. International Journal of Production Research, 54(4), 1039-1060.

[120]

Zorin, D. A., & Kostenko, V. A. (2014). Simulated annealing algorithm for job shop scheduling on reliable real-time systems. In International conference on operations research and enterprise systems (pp. 31-46). Berlin: Springer.

Cited By

Zhang FMei YNguyen SZhang M(2024)Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325524628:1(147-167)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3255246
Mo FChaplin JSanderson DMartínez-Arellano GRatchev S(2024)Semantic models and knowledge graphs as manufacturing system reconfiguration enablersRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2023.10262586:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.rcim.2023.102625
Herrmann JMordaschew VTackenberg S(2024)A multi-skill RCPSP variant for persons with disabilities in sheltered workshopsProcedia Computer Science10.1016/j.procs.2024.01.131232:C(1329-1338)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.01.131
Show More Cited By

Review of job shop scheduling research and its new perspectives under Industry 4.0

Recommendations

Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion

Flexible job shop scheduling problem.Ensembles of heuristics.Re-scheduling for new job insertion.Multiple objectives scheduling problem. This study investigates the flexible job shop scheduling problem (FJSP) with new job insertion. FJSP with new job ...
A Broader View of the Job-Shop Scheduling Problem

We define a job-shop scheduling problem with three dynamic decisions: assigning due-dates to exogenously arriving jobs, releasing jobs from a backlog to the shop floor, and sequencing jobs at each of two workstations in the shop. The job-shop is modeled ...
A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates

Flexible job shop scheduling problem (FJSSP) is generalization of job shop scheduling problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Intelligent Manufacturing

Journal of Intelligent Manufacturing Volume 30, Issue 4

April 2019

508 pages

ISSN:0956-5515

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

58
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang FMei YNguyen SZhang M(2024)Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325524628:1(147-167)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TEVC.2023.3255246
Mo FChaplin JSanderson DMartínez-Arellano GRatchev S(2024)Semantic models and knowledge graphs as manufacturing system reconfiguration enablersRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2023.10262586:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.rcim.2023.102625
Herrmann JMordaschew VTackenberg S(2024)A multi-skill RCPSP variant for persons with disabilities in sheltered workshopsProcedia Computer Science10.1016/j.procs.2024.01.131232:C(1329-1338)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.01.131
NOHAIR LADRAOUI ANAMIR A(2024)An Improved Hybrid Metaheuristic for Active Job-Shop Scheduling ProblemsProcedia Computer Science10.1016/j.procs.2023.12.164231:C(56-62)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.12.164
Khurshid BMaqsood S(2024)A hybrid evolution strategies-simulated annealing algorithm for job shop scheduling problemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108016133:PAOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108016
Tejer MSzczepanski RTarczewski T(2024)Robust and efficient task scheduling for robotics applications with reinforcement learningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107300127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107300
Ji BZhang SYu SXiao XChen CZheng G(2024)Novel model and solution method for flexible job shop scheduling problem with batch processing machinesComputers and Operations Research10.1016/j.cor.2023.106442161:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.cor.2023.106442
Jin XMi NSong WLi Q(2024)Scheduling of twin automated stacking cranes based on Deep Reinforcement LearningComputers and Industrial Engineering10.1016/j.cie.2024.110104191:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110104
Wei GYe CXu J(2024)Shared manufacturing-based distributed flexible job shop scheduling with supply-demand matchingComputers and Industrial Engineering10.1016/j.cie.2024.109950189:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.109950
Liu MLv JDu SDeng YShen XZhou Y(2024)Multi-resource constrained flexible job shop scheduling problem with fixture-pallet combinatorial optimisationComputers and Industrial Engineering10.1016/j.cie.2024.109903188:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.109903
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents