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Scheduling in Job Shops

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Handbook on Scheduling

Part of the book series: International Handbook on Information Systems ((INFOSYS))

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Abstract

In this chapter we continue scheduUng of tasks on dedicated processors or machines. We assume that tasks belong to a set of jobs, each of which is characterized by its own machine sequence. We will assume that any two consecutive tasks of the same job are to be processed on different machines. The type of factory layout is the job shop. It provides the most flexible form of manufacturing, however, frequently accepting unsatisfactory machine utilization and a large amount of work-in-process. Hence, makespan minimization is one of the objectives in order to schedule job shops effectively, see e.g. [Pin95].

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References

  1. A. Aggoun, N. Beldiceanu, Extending CHIP in order to solve complex scheduling and placement problems, Math. Comput. Model. 17, 1993, 57–73.

    Article  Google Scholar 

  2. J. Adams, E. Balas, D. Zawack, The shifting bottleneck procedure for job shop scheduling, Management Sci. 34, 1988, 391–401.

    Google Scholar 

  3. D. Applegate, W. Cook, A computational study of the job-shop scheduling problem, ORSA J. Comput. 3, 1991, 149–156.

    Google Scholar 

  4. E. J. Anderson, C. A. Glass, C. N. Potts, Local search in combinatorial optimization: applications in machine scheduling, in: E. Aarts, J. K. Lenstra (eds.), Local Search in Combinatorial Optimization, Wiley, New York, 1997.

    Google Scholar 

  5. S. Ashour, S. R. Hiremath, A branch-and-bound approach to the job-shop scheduling problem, Internat. J. Prod. Res. 11, 1973, 47–58.

    Article  Google Scholar 

  6. S. B. Akers, A graphical approach to production scheduling problems, Oper. Res. 4, 1956, 244–245.

    Google Scholar 

  7. E. H. L. Aarts, P. J. P. van Laarhoven, J. K. Lenstra, N. L. J. Ulder, A computational study of local search shop scheduling, ORSA J. Comput. 6, 1994, 118–125.

    Google Scholar 

  8. E. Balas, Machine sequencing via disjunctive graphs: An implicit enumeration algorithm, Oper. Res. 17, 1969, 941–957.

    Google Scholar 

  9. E. Balas, On the facial structure of scheduling polyhedra, Math. Programming Study 24, 1985, 179–218.

    Google Scholar 

  10. W. Brinkköter, P. Brucker, Solving open benchmark problems for the job shop problem, Journal of Scheduling 4, 2001, 53–64.

    Article  Google Scholar 

  11. J. W. Barnes, J. B. Chambers, Solving the job shop scheduling problem using tabu search, HE Transactions 27, 1995, 257–263.

    Google Scholar 

  12. J. Błażewicz, W. Domschke, E. Pesch, The job shop scheduling problem: Conventional and new solution techniques, European J. Oper. Res. 93, 1996, 1–33.

    Article  Google Scholar 

  13. J. Błażewicz, M. Dror, J. Weglarz, Mathematical programming formulations for machine scheduling: A survey. European J. Oper. Res. 51, 1991, 283–300.

    Article  Google Scholar 

  14. S. Brah, J. Hunsucker, J. Shah, Mathematical modeling of scheduling problems, J. Inform. Opt. Sci. 12, 1991, 113–137.

    Google Scholar 

  15. P. Brucker, J. Hurink, F. Werner, Improving local search heuristics for some scheduling problems, Discrete Appl. Math. 65, 1996, 97–122.

    Article  Google Scholar 

  16. P. Brucker, J. Hurink, F. Werner, Improving local search heuristics for some scheduling problems: Part II, Discrete Appl. Math 72, 1997, 47–69.

    Article  Google Scholar 

  17. C. Bierwirth, A generalized permutation approach to job shop scheduling with genetic algorithms, OR Spektrum 17, 1995, 87–92.

    Article  Google Scholar 

  18. P. Brucker, B. Jurisch, A new lower bound for the job-shop scheduling problem, European J. Oper. Res. 64, 1993, 156–167.

    Article  Google Scholar 

  19. P. Brucker, B. Jurisch, A. Krämer, The job-shop problem and immediate selection, Annals of OR 50, 1994, 73–114.

    Article  Google Scholar 

  20. P. Brucker, B. Jurisch, B. Sievers, Job-shop (C codes), European J. Oper. Res. 57, 1992, 132–133.

    Article  Google Scholar 

  21. P. Brucker, B. Jurisch, B. Sievers, A branch and bound algorithm for the jobshop scheduling problem, Discrete Appl. Math. 49, 1994, 107–127.

    Article  Google Scholar 

  22. P. Brucker, S. Knust, Complex Scheduling, Springer, Berlin 2006.

    Google Scholar 

  23. K. R. Baker, E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, Preemptive scheduling of a single machine to minimize maximum cost subject to release dates and precedence constraints, Oper. Res. 31, 1983, 381–386.

    Google Scholar 

  24. E. Balas, J. K. Lenstra, A. Vazacopoulos, One machine scheduling with delayed precedence constraints, Management Sci. 41, 1995, 94–109.

    Google Scholar 

  25. J. R. Barker, G. B. McMahon, Scheduling the general job-shop, Management Sci. 31, 1985,594–598.

    Article  Google Scholar 

  26. E. H. Bowman, The scheduling sequencing problem, Oper. Res. 7, 1959, 621–624.

    Google Scholar 

  27. P. Baptiste, C. Le Pape, A theoretical and experimental comparison of constraint propagation techniques for disjunctive scheduling, Proc. of the 14th Internal Joint Conf. on Artificial Intelligence (IJCAI), Montreal, 1995.

    Google Scholar 

  28. J. H. Blackstone, D. T Phillips, G. L. Hogg, A state of the art survey of dispatching rules for manufacturing job shop tasks, Internat. J. Prod. Res. 20, 1982, 27–45.

    Article  Google Scholar 

  29. P. Baptiste, C. Le Pape, W. Nuijten, Constraint-based optimization and approximation for job-shop scheduling, Proc. of the AAAI-SIGMAN Workshop on Intelligent Manufacturing Systems, IJCAI, Montreal, 1995.

    Google Scholar 

  30. P. Baptiste, C. Le Pape, W. Nuijten, Incorporating efficient operations research algorithms in constraint-based scheduling, Proc. of the 1st. Joint Workshop on Artificial Intelligence and Operations Research, Timberline Lodge, Oregon, 1995.

    Google Scholar 

  31. J. Błażewicz, E. Pesch, M. Sterna, A branch and bound algorithm for the job shop scheduling problem, in: A. Drexl, A. Kimms (eds.) Beyond Manufacturing Resource Planning (MRPII), Springer, 1998, 219–254.

    Google Scholar 

  32. J. Błażewicz, E. Pesch, M. Sterna, A note on disjunctive graph representation, Bulletin of the Polish Academy of Sciences 47, 1999, 103–114.

    Google Scholar 

  33. J. Błażewicz, E. Pesch, M. Sterna, The disjunctive graph machine representation of the job shop problem, European J. Oper. Res. 127, 2000, 317–331.

    Article  Google Scholar 

  34. P. Bertier, B. Roy, Trois examples numeriques d’application de la procedure SEP, Note de travail No. 32 de la Direction Scientifique de la SEMA, 1965.

    Google Scholar 

  35. P. Brucker, An efficient algorithm for the job-shop problem with two jobs, Computing 40, 1988, 353–359.

    Article  Google Scholar 

  36. P. Brucker, A polynomial algorithm for the two machine job-shop scheduling problem with a fixed number of jobs, OR Spektrum 16, 1994, 5–7.

    Article  Google Scholar 

  37. P. Brucker, Scheduling Algorithms, Springer, 4. edition, Berlin 2004.

    Google Scholar 

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

    Article  Google Scholar 

  39. E. Balas, A. Vazacopoulos, Guided local search with shifting bottleneck for job shop scheduling, Management Sci. 44, 1998, 262–275.

    Google Scholar 

  40. G. H. Brooks, C. R. White, An algorithm for finding optimal or near-optimal solutions to the production scheduling problem, J. Industrial Eng. 16, 1965, 34–40.

    Google Scholar 

  41. M. Chandrasekaran, P. Asokan, S. Kumanan, T. Balamurugan, S. Nickolas, Solving job shop scheduling problems using artificial immune system, International Journal of Advanced Manufacturing Technology 31, 2006, 580–593.

    Article  Google Scholar 

  42. J. Carlier, The one machine sequencing problem, European J. Oper. Res. 11, 1982, 42–47.

    Article  Google Scholar 

  43. J. Carlier, Scheduling jobs with release dates and tails on identical machines to minimize the makespan, European J. Oper. Res. 29, 1987, 298–306.

    Article  Google Scholar 

  44. J. M. Charlton, C. C. Death, A generalized machine scheduling algorithm, Oper. Res. Quart. 21, 1970, 127–134.

    Google Scholar 

  45. W. B. Crowston, F. Glover, G. L. Thompson, J. D. Trawick, Probabilistic and parametric learning combinations of local job shop scheduling rules, ONR Research Memorandum No. 117, GSIA, Carnegie-Mellon University, Pittsburg, 1963.

    Google Scholar 

  46. Y. Caseau, F. Laburthe, Disjunctive scheduling with task intervals, Working paper, Ecole Normale Supérieure, Paris, 1995.

    Google Scholar 

  47. W. Clark, The Gantt Chart: A Working Tool of Management, The Ronald Press (3rd ed.), Pittman, New York, 1922.

    Google Scholar 

  48. J. Carlier, E. Pinson, An algorithm for solving the job-shop problem, Management Sci. 35, 1989, 164–176.

    Google Scholar 

  49. J. Carlier, E. Pinson, A practical use of Jackson’s preemptive schedule for solving the job shop problem, Ann. Oper. Res. 26, 1990, 269–287.

    Google Scholar 

  50. J. Carlier, E. Pinson, Adjustments of heads and tails for the job-shop problem, European J. Oper. Res. 78, 1994, 146–161.

    Article  Google Scholar 

  51. Y. Caseau, C. Le Pape, W. P. M. Nuijten, private communication, 1996.

    Google Scholar 

  52. C. Chu, M. C. Portmann, J. M. Proth, A splitting-up approach to simplify job-shop scheduling problems, Internal. J. Prod. Res. 30, 1992, 859–870.

    Google Scholar 

  53. J. E. Day, P. M. Hottenstein, Review of sequencing research, Naval Res. Logistics Quart. 17, 1970, 11–39.

    Article  Google Scholar 

  54. S. Dauzere-Peres, J.-B. Lasserre, A modified shifting bottleneck procedure for job-shop scheduling, Internat. J. Prod. Res. 31, 1993, 923–932.

    Article  Google Scholar 

  55. E. Demirkol, S. Mehta, R. Uzsloy, A computational study of the shifting bottleneck procedure for job shop scheduling problems, Journal of Heuristics 3, 1997, 111–137.

    Article  Google Scholar 

  56. R. Dechter, J. Pearl, Network-based heuristics for constraint satisfaction problems, Artificial Intelligence 34, 1988, 1–38.

    Article  Google Scholar 

  57. U. Dorndorf, E. Pesch, Combining genetic and local search for solving the job shop scheduling problem, Proc. Symposium on Appl. Mathematical Programming and Modeling-APMOD93, Budapest, 1993, 142–149.

    Google Scholar 

  58. U. Dorndorf, E. Pesch, Variable depth search and embedded schedule neighborhoods for job shop scheduling, Proc. 4th Internat. Workshop on Project Management and Scheduling, 1994, 232–235.

    Google Scholar 

  59. U. Dorndorf, E. Pesch, Evolution based learning in a job shop scheduling environment, Comput. Oper. Res. 22, 1995, 25–40.

    Article  Google Scholar 

  60. U. Dorndorf, T. Phan Huy, E. Pesch, A survey of interval capacity consistency tests for time-and resource-constrained scheduling, in: J. Weglarz (ed.) Project Scheduling-Recent Models, Algorithms and Applications, Kluwer Academic Publ., 1999, 213–238.

    Google Scholar 

  61. U. Dorndorf, E. Pesch, T. Phan Huy, Constraint propagation techniques for disjunctive scheduling problems, Artificial Intelligence 122, 2000, 189–240.

    Article  Google Scholar 

  62. U. Dorndorf, E. Pesch, T. Phan-Huy, Constraint propagation and problem decomposition: A preprocessing procedure for the job shop problem, Ann. Oper. Res. 115, 2002, 125–145.

    Article  Google Scholar 

  63. M. Dell’Amico, M. Trubian, Applying tabu-search to the job shop scheduling problem, Ann. Oper. Res. 41, 1993, 231–252.

    Article  Google Scholar 

  64. F. Della Croce, R. Tadei, G. Volta, A genetic algorithm for the job shop problem. Comput. Oper. Res. 22, 1995, 15–24.

    Article  Google Scholar 

  65. A. El-Bouri, N. Azizi, S. Zolfaghri, A comparative study of a new heuristic based on adaptive memory programming and simulated annealing: The case of job shop scheduling, European J. Oper. Res. 177, 2007, 1894–1910.

    Article  Google Scholar 

  66. M. L. Fisher, Optimal solution of scheduling problems using Lagrange multipliers: Part I, Oper. Res. 21, 1973, 1114–1127.

    Google Scholar 

  67. M. L. Fisher, B. J. Lageweg, J. K. Lenstra, A. H. G. Rinnooy Kan, Surrogate duality relaxation for job shop scheduling, Discrete Appl. Math. 5, 1983, 65–75.

    Article  Google Scholar 

  68. M. S. Fox, Constraint-Directed Search: A Case Study of Job Shop Scheduling, Pitman, London, 1987.

    Google Scholar 

  69. M. S. Fox, S. F. Smith, ISIS-a knowledge based system for factory scheduling, Expert Systems 1, 1984, 25–49.

    Article  Google Scholar 

  70. H. Fisher, G. L. Thompson, Probabilistic learning combinations of local job-shop scheduling rules, in: J. F. Muth, G. L. Thompson (eds.), Industrial Scheduling, Prentice Hall, Englewood Cliffs, N.J., 1963.

    Google Scholar 

  71. M. Florian, P. Trépant, G. McMahon, An implicit enumeration algorithm for the machine sequencing problem, Management Sci. 17, 1971, B782–B792.

    Google Scholar 

  72. H. L. Gantt, Efficiency and democracy, Trans. Amer. Soc. Mech. Engin. 40, 1919, 799–808.

    Google Scholar 

  73. W. S. Gere, Heuristics in job-shop scheduling, Management Sci. 13, 1966, 167–190.

    Google Scholar 

  74. P. J. O. Grady, C. Harrison, A general search sequencing rule for job shop sequencing, Internat. J. Prod. Res. 23, 1985, 951–973.

    Google Scholar 

  75. J. Grabowski, E. Nowicki, S. S. Zdrzalka, A block approach for single machine scheduling with release dates and due dates, European J. Oper. Res. 26, 1985, 278–285.

    Article  Google Scholar 

  76. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, Mass., 1989.

    Google Scholar 

  77. C. A. Glass, C. N. Potts, P. Shade, Genetic algorithms and neighborhood-neighborhood search for scheduling unrelated parallel machines, Working paper No. OR47, University of Southampton.

    Google Scholar 

  78. H. H. Greenberg, A branch and bound solution to the general scheduling problem, Oper. Res. 16, 1968, 353–361.

    Google Scholar 

  79. T. Gonzalez, S. Sahni, Flowshop and jobshop schedules: Complexity and approximation, Oper. Res. 20, 1978, 36–52.

    Google Scholar 

  80. B. Giffler, G. L. Thompson, Algorithms for solving production scheduling problems, Oper. Res. 8, 1960, 487–503.

    Google Scholar 

  81. N. Hefetz, I. Adiri, An efficient optimal algorithm for the two-machines unit-time job-shop schedule length problem, Math. Oper. Res. 7, 1982, 354–360.

    Google Scholar 

  82. R. Haupt, A survey of priority-rule based scheduling, OR Spektrum 11, 1989, 3–16.

    Article  Google Scholar 

  83. P. van Hentenryck, Y. Deville, C.-M. Teng, A generic arc-consistency algorithm and its specializations, Artificial Intelligence 57, 1992, 291–321.

    Article  Google Scholar 

  84. C. C. Han, C. H. Lee, Comments on Mohr and Hendersons path consistency algorithm, Artificial Intelligence 36, 1988, 125–130.

    Article  Google Scholar 

  85. K.-L. Huang, C.-J. Liao, Ant colony optimization combined with taboo search for the job shop scheduling problem, Working paper, 2006, National Taiwan University of Science and Technology, Taipei.

    Google Scholar 

  86. J. R. Jackson, An extension of Johnson’s results on job lot scheduling, Naval Res. Logist. Quart. 3, 1956, 201–203.

    Article  Google Scholar 

  87. A.S. Jain, S. Meeran, Deterministic job shop scheduling: past, present and future, European J. Oper. Res. 113, 1999, 390–434.

    Article  Google Scholar 

  88. S. M. Johnson, Optimal two-and three-stage production schedules with setup times included, Naval Res. Logist. Quart. 1, 1954, 61–68.

    Article  Google Scholar 

  89. A.S. Jain, B. Rangaswamy, S. Meeran, New and “stronger” job-shop neighbourhoods: A focus on the method of Nowicki and Smtnicki (1996), Journal of Heuristics 6, 2000, 457–480.

    Article  Google Scholar 

  90. A. Kolen, E. Pesch, Genetic local search in combinatorial optimization, Discrete Appl. Math. 48, 1994, 273–284.

    Article  Google Scholar 

  91. M. Kolonko, Some new results on simulated annealing applied to the job shop scheduling problem, European J. Oper. Res. 113, 1999, 123–136.

    Article  Google Scholar 

  92. W. Kubiak, S. Sethi, C. Srishkandarajah, An efficient algorithm for a job shop problem, Math. Industrial Syst. 1, 1995, 203–216.

    Google Scholar 

  93. P. J. M. van Laarhoven, E. H. L. Aarts, J. K. Lenstra, Job shop scheduling by simulated annealing, Oper. Res. 40, 1992, 113–125.

    Google Scholar 

  94. B. Lageweg, J. K. Lenstra, A. H. G. Rinnooy Kan, Job-shop scheduling by implicit enumeration, Management Sci. 24, 1977, 441–450.

    Google Scholar 

  95. E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan, D. B. Shmoys, Sequencing and scheduling: algorithms and complexity, in: S. C. Graves, A. H. G. Rinnooy Kan, P. H. Zipkin (eds.), Handbooks in Oper. Res. and Management Sci., Vol. 4: Logistics of Production and Inventory, Elsevier, Amsterdam, 1993.

    Google Scholar 

  96. H. R. Lourenço, A computational study of the job-shop and flow shop scheduling problems, Ph.D. thesis, Cornell University, 1993.

    Google Scholar 

  97. H. R. Lourenço, Job-shop scheduling: Computational study of local search and large-step optimization methods, European J. Oper. Res. 83, 1995, 347–364.

    Article  Google Scholar 

  98. J. K. Lenstra, A. H. G. Rinnooy Kan, Computational complexity of discrete optimization problems, Ann. Discrete Math. 4, 1979, 121–140.

    Google Scholar 

  99. J. K. Lenstra, R. H. G. Rinnooy Kan, P. Brucker, Complexity of machine scheduling problems, Ann. Discrete Math. 4, 1977, 121–140.

    Google Scholar 

  100. A. K. Mackworth, Consistency in networks of relations, Artificial Intelligence 8, 1977, 99–118.

    Article  Google Scholar 

  101. A. S. Manne, On the job shop scheduling problem, Oper. Res. 8, 1960, 219–223.

    Google Scholar 

  102. D. C. Mattfeld, Evolutionary Search and the Job Shop, Physica, Heidelberg, 1996.

    Google Scholar 

  103. P. Meseguer, Constraint satisfaction problems: An overview, AICOM 2, 1989, 3–17.

    Google Scholar 

  104. G. B. McMahon, M. Florian, On scheduling with ready times and due dates to minimize maximum lateness, Oper. Res. 23, 1975, 475–482.

    Google Scholar 

  105. J. Montgomery, C. Fayad, S. Petrovic, Solution representation for job shop scheduling problems in ant colony optimization, Lecture Notes in Computer Science 4150, 2006, 484–491.

    Google Scholar 

  106. R. Mohr, T. C. Henderson, Arc and path consistency revisited, Artificial Intelligence 28, 1986, 225–233.

    Article  Google Scholar 

  107. S. Minton, M. D. Johnston, A. B. Philips, P. Laird, Minimizing conflicts: A heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence 58, 1992, 161–205.

    Article  Google Scholar 

  108. U. Montanari, Networks of constraints: fundamental properties and applications to picture processing, Inform. Sci. 7, 1974, 95–132.

    Article  Google Scholar 

  109. P. Martin, D. Shmoys, A new approach to computing optimal schedules for the job shop scheduling problem, Proceedings of the 5 th International IPCO Conference, 1996.

    Google Scholar 

  110. H. Matsuo, C. J. Suh, R. S. Sullivan, A controlled search simulated annealing method for the general job shop scheduling problem, working paper 03-04-88, University of Texas Austin, 1988.

    Google Scholar 

  111. J. F. Muth, G. L. Thompson (eds.), Industrial Scheduling, Prentice Hall, Englewood Cliffs, N.J., 1963.

    Google Scholar 

  112. W. P. M. Nuijten, E. H. L. Aarts, A computational study of constraint satisfaction for multiple capacitated job shop scheduling, European J. Oper. Res. 90, 1996, 269–284.

    Article  Google Scholar 

  113. M. Nawaz, E. E. Enscore, I. Ham, A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem, Omega 11, 1983, 91–95.

    Article  Google Scholar 

  114. E. Nowicki, C. Smutnicki, A fast taboo search algorithm for the job shop problem, Management Sci. 42, 1996, 797–813.

    Article  Google Scholar 

  115. E. Nowicki, C. Smutnicki, An advanced tabu search algorithm for the job shop problem, Journal of Scheduling 8, 2005, 145–159.

    Article  Google Scholar 

  116. W. P. M. Nuijten, Time and Resource Constrained Scheduling, Ponsen & Looijen, Wageningen, 1994.

    Google Scholar 

  117. R. Nakano, T. Yamada, Conventional genetic algorithm for job shop problems, in: R. K. Belew, L. B. Booker (eds.), Proc. 4th. Internal Conf. on Genetic Algorithms, Morgan Kaufmann, 1991, 474–479.

    Google Scholar 

  118. P. S. Ow, S. F. Smith, Viewing scheduling as an opportunistic problem-solving process, Ann. Oper. Res. 12, 1988, 85–108.

    Article  Google Scholar 

  119. M. Perregaard, J. Clausen, Parallel branch-and-bound methods for the job-shop scheduling problem, Working paper, University of Copenhagen, 1995.

    Google Scholar 

  120. E. Pesch, Learning in Automated Manufacturing, Physica, Heidelberg, 1994.

    Google Scholar 

  121. T. Phan-Huy, Constraint Propagation in Flexible Manufacturing, Springer, Berlin, 2000.

    Google Scholar 

  122. S. S. Panwalkar, W. Iskander, A survey of scheduling rules, Oper. Res. 25, 1977, 45–61.

    Google Scholar 

  123. P. Pinedo, Scheduling Theory, Algorithms and Systems, Prentice Hall, Englewood Cliffs, N.J., 1995.

    Google Scholar 

  124. F. Pezzella, E. Merelli, Tabu search method guided by shifting bottleneck for the job shop scheduling problem, European J. Oper. Res. 120, 2000, 297–310.

    Article  Google Scholar 

  125. D. B. Porter, The Gantt chart as applied to production scheduling and control, Naval Res. Logist. Quart. 15, 1968, 311–317.

    Google Scholar 

  126. C. N. Potts, Analysis of a heuristic for one machine sequencing with release dates and delivery times, Oper. Res. 28, 1980, 1436–1441.

    Article  Google Scholar 

  127. E. Pesch, U. Tetzlaff, Constraint propagation based scheduling of job shops, Journal on Computing 8, 1996, 144–157.

    Google Scholar 

  128. B. Roy, B. Sussmann, Les problémes d’ordonnancement avec contraintes disjonctives, SEMA, Note D. S. No. 9., Paris, 1964.

    Google Scholar 

  129. N. Sadeh, Look-ahead techniques for micro-opportunistic job shop scheduling, Ph.D. thesis, Carnegie Mellon University, Pittsburgh, 1991.

    Google Scholar 

  130. D. Sun, R. Batta, L. Lin, Effective job shop scheduling through active chain manipulation, Comput. Oper. Res. 22, 1995, 159–172.

    Article  Google Scholar 

  131. S. F. Smith, M. S. Fox, P. S. Ow, Constructing and maintaining detailed production plans: investigations into the development of knowledge-based factory scheduling systems, AI Magazine, 1986, 46–61.

    Google Scholar 

  132. Y. N. Sotskov, N. V. Shaklevich, NP-hardness of shop scheduling problems with three jobs, Discrete Appl. Math. 59, 1995, 237–266.

    Article  Google Scholar 

  133. R. H. Storer, S. D. Wu, R. Vaccari, New search spaces for sequencing problems with application to job shop scheduling, Management Sci. 38, 1992, 1495–1509.

    Google Scholar 

  134. E. Taillard, Parallel tabu search technique for the job shop scheduling problem, ORSA J. Comput. 6, 1994, 108–117.

    Google Scholar 

  135. N. L. J. Ulder, E. H. L. Aarts, H.-J. Bandelt, P. J. P. van Laarhoven, E. Pesch, Genetic local search algorithms for the traveling salesman problem, Lecture Notes in Computer Sci. 496, 1991, 109–116.

    Article  Google Scholar 

  136. R. J. P. Vaessens, Generalized job shop scheduling: complexity and local search, Ph.D. thesis, University of Technology Eindhoven, 1995.

    Google Scholar 

  137. R. J. P. Vaessens, E. H. L. Aarts, J. K. Lenstra, Job shop scheduling by local search, Journal on Computing 8, 1996, 302–317.

    Google Scholar 

  138. S. van de Velde, Machine scheduling and lagrangian relaxation, Ph.D. thesis, CWI Amsterdam, 1991.

    Google Scholar 

  139. H. P. Wagner, An integer linear programming model for machine scheduling, Naval Res. Logist. Quart. 6, 1959, 131–140.

    Article  Google Scholar 

  140. K. P. White, R. V. Rogers, Job-shop scheduling: Limits of the binary disjunctive formulation, Internat. J. Prod. Res. 28, 1990, 2187–2200.

    Article  Google Scholar 

  141. F. Werner, A. Winkler: Insertion techniques for the heuristic solution of the job shop problem, Discrete Appl. Math. 50, 1995, 191–211.

    Article  Google Scholar 

  142. T. Yamada, R. Nakano, A genetic algorithm applicable to large-scale job-shop problems, in: R. Manner, B. Manderick (eds.), Parallel Problem Solving from Nature 2, Elsevier, 1992, 281–290.

    Google Scholar 

  143. C.Y. Zhang, P.G. Li, Y.Q. Rao, Z.L. Guan, A very fast TS/SA algorithm for the job shop scheduling problem, Working paper, 2006, Huazhong University, Wuhan.

    Google Scholar 

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(2007). Scheduling in Job Shops. In: Handbook on Scheduling. International Handbook on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32220-7_10

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