Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems

Information Sciences

Volumn 298, Issue , 2015, Pages 198-224

  Shen, Xiao Ning ^a   Yao, Xin ^b  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Decision making; Evolutionary computations; Mathematical modeling; Metaheuristics; Scheduling

Indexed keywords

DECISION MAKING; ECONOMIC AND SOCIAL EFFECTS; EVOLUTIONARY ALGORITHMS; HEURISTIC METHODS; MACHINE SHOPS; MANUFACTURE; MATHEMATICAL MODELS; OPTIMIZATION; SCHEDULING;

DECISION MAKER'S PREFERENCES; DYNAMIC DECISION MAKING; FLEXIBLE JOB-SHOP SCHEDULING; FLEXIBLE JOB-SHOP SCHEDULING PROBLEM; META HEURISTICS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; NEW MATHEMATICAL MODEL; OPERATION SEQUENCING;

JOB SHOP SCHEDULING;

EID: 84922454987     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.11.036     Document Type: Article

References (66)

1. R.J. Abumaizar, and J.A. Svestka Rescheduling job shops under random disruptions Int. J. Prod. Res. 35 7 1997 2065 2082
2. M.A. Adibi, M. Zandieh, and M. Amiri Multi-objective scheduling of dynamic job shop using variable neighborhood search Expert Syst. Appl. 37 1 2010 282 287
3. N. Al-Hinai, and T.Y. ElMekkawy Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm Int. J. Prod. Econ. 132 2 2011 279 291
4. S. Balin A robust scheduling method based on a multi-objective immune algorithm Inf. Sci. 181 2011 3551 3569
5. G. Chryssolouris, and E. Subramanian Dynamic scheduling of manufacturing job shops using genetic algorithm J. Intell. Manuf. 12 2001 281 293
6. L.K. Church, and R. Uzsoy Analysis of periodic and event-driven rescheduling policies in dynamic shops Int. J. Comput. Integr. Manuf. 5 3 1992 153 163
7. J. Cohen Statistical Power Analysis for the Behavioral Sciences second ed. 1988 Lawrence Erlbaum Hillsdale, NJ
8. K. Deb, S. Chaudhuri, K. Meitinnen, Towards estimating nadir objective vector using evolutionary approaches, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2006, pp. 643-650.
9. K. Deb, M. Mohan, and S. Mishra Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal Evolution. Comput. 13 4 2005 501 525
10. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans. Evol. Comput. 6 2 2002 182 197
11. K. Deb, N. Udaya Bhaskara Rao, S. Karthik, Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, in: Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, vol. 4403, 2007, pp. 803-817.
12. Y. Demir, and S. Kürşat Işleyen Evaluation of mathematical models for flexible job-shop scheduling problems Appl. Math. Model. 37 2013 977 988
13. I. Durgun, and A.R. Yildiz Structural design optimization of vehicle components using Cuckoo search algorithm Mater. Test. 54 2012 185 188
14. P. Fattahi, and A. Fallahi Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability CIRP J. Manuf. Sci. Technol. 2 2 2010 114 123
15. P. Fattahi, M. Saidi Mehrabad, and F. Jolai Mathematical modelling and heuristic approaches to flexible job shop scheduling problems J. Intell. Manuf. 18 2007 331 342
16. J. Fülöp, Introduction to Decision Making Methods, Working Paper 05-6, Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, 2007 < http://academic.evergreen.edu/projects/bdei/documents/decisionmakingmethods.pdf >.
17. M.R. Garey, D.S. Johnson, and R. Sethi The complexity of flow shop and job shop scheduling Math. Operat. Res. 1 1976 117 129
18. M. Gen, Y. Tsujimura, E. Kubota, Solving job shop scheduling problem by genetic algorithms, in: Proceedings of IEEE International Conference on Systems, Man, and Cybernetic, 1994, pp. 1577-1582.
19. T. Hildebrandt, J. Heger, B. Scholz-Reiter, Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach, in: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, 2010, pp. 257-264.
20. M.T. Jensen Generating robust and flexible job shop schedules using genetic algorithms IEEE Trans. Evol. Comput. 7 3 2003 275 288
21. L. Jourdan, E. Talbi, Combinatorial optimization of stochastic multi-objective problems: an application to the flow-shop scheduling problem, in: Proceedings of Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, vol. 4403, 2007, pp. 457-471.
22. I. Kacem, S. Hammadi, and P. Borne Approach by localization and multi-objective evolutionary optimization for flexible job shop scheduling problems IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32 1 2002 1 13
23. D. Lei Simplified multi-objective genetic algorithms for stochastic job shop scheduling Appl. Soft Comput. 11 8 2011 4991 4996
24. L. Liu, H. Gu, and Y. Xi Robust and stable scheduling of a single machine with random machine breakdowns Int. J. Adv. Manuf. Technol. 31 2007 645 654
25. C. Mészáros, and T. Rapcsák On sensitivity analysis for a class of decision systems Dec. Support Syst. 16 1996 231 240
26. S. Nguyen, M. Zhang, M. Johnston, and K. Tan Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming IEEE Trans. Evol. Comput. 18 2 2013 193 208
27. L. Nie, L. Gao, P. Li, and X. Li A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates J. Intell. Manuf. 24 4 2013 763 774
28. L. Nie, L. Gao, P. Li, and X. Shao Reactive scheduling in a job shop where jobs arrive over time Comput. Ind. Eng. 66 2 2013 389 405
29. D. Ouelhadj, and S. Petrovic A survey of dynamic scheduling in manufacturing systems J. Sched. 12 4 2009 417 431
30. C. Ozguven, L. Ozbakir, and Y. Yavuz Mathematical models for job-shop scheduling problems with routing and process plan flexibility Appl. Math. Model. 34 2010 1539 1548
31. S.S. Panwalkar, and W. Iskander A survey of scheduling rules Operat. Res. 25 1977 45 61
32. X. Qiu, and H.Y.K. Lau An AIS-based hybrid algorithm with PDRs for multi-objective dynamic online job shop scheduling problem Appl. Soft Comput. 13 3 2013 1340 1351
33. R. Rangsaritratsamee, W.G. Frell, and M.B. Kurz Dynamic rescheduling that simultaneously considers efficiency and stability Comput. Ind. Eng. 46 2004 1 15
34. C. Raquel, and X. Yao Dynamic multi-objective optimization: a survey of the state-of-the-art Evolutionary Computation for Dynamic Optimisation Problems 2013 Springer-Verlag Berlin 58 106
35. T.L. Saaty The Analytic Hierarchy Process 1980 McGraw Hill New York
36. T.L. Saaty, and L.G. Vargas Comparison of eigenvalue, logarithmic least squares and least squares methods in estimating ratios Math. Model. 5 1984 309 324
37. J.R. Schott, Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization, Master's Thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1995.
38. A.I. Sivakumar, and A.K. Gupta Online multiobjective Pareto optimal dynamic scheduling of semiconductor back-end using conjunctive simulated scheduling IEEE Trans. Electron. Packag. Manuf. 29 2 2006 99 109
39. X.N. Shen, Y. Guo, Q.W. Chen, and W.L. Hu A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic Comput. Optimiz. Appl. 46 2010 159 188
40. L. Song, L.L. Minku, X. Yao, The impact of parameter tuning on software effort estimation using learning machines, in: Proceedings of the 9th International Conference on Predictive Models in Software Engineering, 2013, p. 10.
41. D.A. Van Veldhuizen, G. B Lamont, Multiobjective evolutionary algorithm test suites, in: Proceedings of the 1999 ACM Symposium on Applied Computing, 1999, pp. 351-357.
42. D.A. Van Veldhuizen, G. B. Lamont, Multiobjective Evolutionary Algorithm Research: A History and Analysis, Technical Report, Department of Electrical and Computer Engineering, Air Force Institute of Technology, OH, 1998.
43. Y. Wang, and K. Chin A linear programming approximation to the eigenvector method in the analytic hierarchy process Inf. Sci. 181 2011 5240 5248
44. S.D. Wu, R.H. Storer, and P.C. Chang One-machine rescheduling heuristics with efficiency and stability as criteria Comput. Oper. Res. 20 1 1993 1 14
45. S. Yao, Z. Jiang, N. Li, H. Zhang, and N. Geng A multi-objective dynamic scheduling approach using multiple attribute decision making in semiconductor manufacturing Int. J. Prod. Econ. 130 1 2011 125 133
46. W.C. Yeh, P.J. Lai, W.C. Lee, and M.C. Chuang Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects Inf. Sci. 269 2014 142 158
47. A.R. Yildiz A comparative study of population-based optimization algorithms for turning operations Inf. Sci. 210 2012 81 88
48. A.R. Yildiz Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach Inf. Sci. 220 2013 399 407
49. A.R. Yildiz Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations Appl. Soft Comput. 13 2013 1433 1439
50. A.R. Yildiz A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations Appl. Soft Comput. 13 2013 1561 1566
51. A.R. Yildiz Cuckoo search algorithm for the selection of optimal machining parameters in milling operations Int. J. Adv. Manuf. Technol. 64 2013 55 61
52. A.R. Yildiz A new design optimization framework based on immune algorithm and Taguchi method Comput. Ind. 60 2009 613 620
53. A.R. Yildiz Comparison of evolutionary based optimization algorithms for structural design optimization Eng. Appl. Artif. Intell. 26 2013 327 333
54. A.R. Yildiz Hybrid Taguchi-harmony search algorithm for solving engineering optimization problems Int. J. Ind. Eng. Theory Appl. Pract. 15 2008 286 293
55. A.R. Yildiz A novel particle swarm optimization approach for product design and manufacturing Int. J. Adv. Manuf. Technol. 40 2009 617 628
56. A.R. Yildiz A new hybrid particle swarm optimization approach for structural design optimization in automotive industry J. Autom. Eng. 226 2012 1340 1351
57. A.R. Yildiz, and K.N. Solanki Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach Int. J. Adv. Manuf. Technol. 59 2012 367 376
58. A.R. Yildiz, and K. Saitou Topology synthesis of multicomponent structural assemblies in continuum domains ASME J. Mech. Des. 133 2011 1 9
59. A.R. Yildiz A novel hybrid immune algorithm for global optimization in design and manufacturing Robot. Comp.-Integ. Manuf. 25 2009 261 270
60. A.R. Yildiz An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry J. Mater. Process. Technol. 50 2009 224 228
61. A.R. Yildiz Hybrid immune-simulated annealing algorithm for optimal design and manufacturing Int. J. Mater. Prod. Technol. 34 2009 217 226
62. L. Zhang, L. Gao, and X. Li A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem Int. J. Product. Res. 2013 1 16 (ahead-of-print)
63. F. Zhao, J.Z. Wang, J.B. Wang, and J. Jonrinaldi A dynamic rescheduling model with multi-agent system and its solution method J. Mech. Eng. 58 2 2012 81 92
64. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization, in: Proceedings of the EUROGEN2001 Conference, 2001, pp. 95-100.
65. E. Zitzler, and L. Thiele Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach IEEE Trans. Evol. Comput. 3 4 1999 257 271
66. X.Q. Zuo, H.W. Mo, and J.P. Wu A robust scheduling method based on a multi-objective immune algorithm Inf. Sci. 179 2009 3359 3369