Abstract
In this paper, the job shop scheduling problem is studied with the objectives of minimizing the makespan and the mean flow time of jobs. The simultaneous consideration of these objectives is the multi-objective optimization problem under study. A metaheuristic procedure based on the simulated annealing algorithm called Pareto archived simulated annealing (PASA) is proposed to discover non-dominated solution sets for the job shop scheduling problems. The seed solution is generated randomly. A new perturbation mechanism called segment-random insertion (SRI) scheme is used to generate a set of neighbourhood solutions to the current solution. The PASA searches for the non-dominated set of solutions based on the Pareto dominance or through the implementation of a simple probability function. The performance of the proposed algorithm is evaluated by solving benchmark job shop scheduling problem instances provided by the OR-library. The results obtained are evaluated in terms of the number of non-dominated schedules generated by the algorithm and the proximity of the obtained non-dominated front to the Pareto front.
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References
Fisher H, Thompson G (1963) Probabilistic learning combinations of local job shop scheduling rules. In: Muth JF, Thompson GL (eds) Industrial Scheduling, Prentice-Hall, New York, pp 225–251
Convay RW, Maxwell WL, Miller LW (1967) Theory of Scheduling. Addition-Wesley, Boston
Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York
Coffman EG (1976) Computes and job shop scheduling theory. Wiley, New York
Pinedo M (1995) Scheduling-theory, algorithms and systems. Prentice-Hall, Englewood Cliffs, NJ
Pinedo M, Chao X (1999) Operations scheduling; with applications in manufacturing and services Limited-Computer Science Series. McGraw-Hill, Singapore
Sule DR (2000)Industrial scheduling. PWS, Boston
T’kindt V, Billaut J–C (2001) Multicriteria scheduling problems: A survey. RAIRO Oper Res 35:143–163
Mellor P (1966) A review of job shop scheduling. Oper Res Q 17:161–171
French S (1982) Sequencing and acheduling - an introduction to the mathematics of the job shop. Wiley, New York
Jain AS, Meeran S (1998) Job shop scheduling using neural networks. Int J Prod Res 36(5):1249–1272
Nagar A, Haddock J, Heragu SS (1995) Multiple and bi-criteria scheduling, a literature review. Eur J Oper Res 81:88–104
Rajendran C (1995) Heuristics for scheduling in flow shop with multiple objectives. Eur J Oper Res 82:540-555
Sridhar J, Rajendran C (1996) Scheduling in flow shop and cellular manufacturing systems with multiple objectives - A genetic algorithmic approach. Prod Plan Control 7(3):374–382
Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in mementic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(1):204–223
Daniels RL, Chambers RJ (1990) Multi-objective flow shop scheduling. Naval Res Logist Q 37:981-995
Bagchi TP (1999) Multiobjective scheduling by genetic algorithms. Kluwer, Boston, MA
Suresh G, SahuS (1993) Multiobjective facility layout using simulated annealing. Int J Prod Econ 32:239-254
Schaffer JD (1985) Multiobjecive optimization with vector evaluated genetic algorithms. Proc First ICGA, pp 93–100
Fonseca CM, Fleming PJ (1991) Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. Proc Fifth International Conference on Genetic Algorithms, pp 416–423
Srinivas N, Deb K (1995) Multiobjective function optimisation using nondominated sorting genetic algorithms. IEEE J Evol Comput 2(2):221-248
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) Fast and elitist multiobjective genetic algorithm: NSGA II . IEEE J Evol Comput 6(1):182–197
Chang PC, Hsieh JC, Lin SG (2002) The development of gradual priority weighting approach for the multi-objective flow shop scheduling problem. Int J Prod Econ 79:171–183
Ishibuchi H, Murata T (1998) A multi-objective genetic local search algorithm and its application to flow shop scheduling. IEEE Trans Syst, Man Cybernetics-Part C: Appl Rev 28:392–403
Zitzler E, Thiele L (1999) Multi-objective evolutionary algorithm: A comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3:251–257
Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64:278–285
Framinan JM, Leisten R, Ruiz-Usano R (2002) Efficient heuristics for flow shop sequencing with the objectives of makespan and flow time minimization. Eur J Oper Res 14:559-569
Garey MR, Johnson DS (1979) Computers and intractability, a guide to the theory of NP-completeness. Freeman, New York
Garey MR, Johnson DS, Sethi R (1976) The complexity of flow shop and job shop scheduling. Math Oper Res 1:117–129
Lawler EL, Lenstra JK, Rinnooy Kan AHG (1982) Recent developments in deterministic sequencing and scheduling: A survey. Reidel, Dordrecht pp 35–74
Chen P, Bulfin R (1994) Complexity of multiple machine multicriteria scheduling problems. Proc third IERC, Atlanta, GA
Lawrence S (1984) Supplement to, resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques. Technical Report, GSIA, Carnegie Mellon Univ
Adams J, Balas E, Zawack D (1998) The shifting bottleneck procedure for job shop scheduling. Manage Sci 34:391–401
Applegate D, Cook W (1991) A computational study of the job shop scheduling problem. ORSA J Comput 3(1):149–156
Storer RH, Wu SD, Vaccari R (1992) New search spaces for sequencing instances with application to job shop instances. Manage Sci 38:1495–1509
Yamada T, Nakano R (1992) A genetic algorithm applicable to large-scale job-shop instances. In: Manner R, Manderick B (eds) Parallel instance solving from nature 2, North-Holland, Amsterdam pp 281–290
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Aarts EHL, Lenstra JK (eds) (1997) Local search and combinatorial optimization. Wiley, New York
Matsuo H, Suh CJ, Sullivan RS (1988) A controlled search simulated annealing method for the general job shop scheduling problem. Working Paper, Graduate School of Business, The University of Texas at Austin, TX
Van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40(1):113–125
Yamada T, Nakano R (1996) Job shop scheduling by simulated annealing combined with deterministic local search. In: Osman IH, Kelly JP (eds) Proc Metaheuristics International Conference, Hilton Breckenridge, Colorado, pp 344–349
Kolonko M (1999) Some new results on simulated annealing applied to the job shop scheduling problem. Eur J Oper Res 113(1):123–136
Czyzak P, Jaszkiewicz A (1998) Pareto simulated annealing – A metaheuristic technique for multi-objective combinatorial optimizations. J Multicriteria Decis 7(1):34–47
Calabrese J, Henley R, Udayabhanu V (2001) Benchmarking of dispatching rules for the job shop flow time problem. Proc First International Conference on Logistics and Supply Chain Management, PSG College of Technology, India 1:205–209
Henning A (2002) Practical job shop scheduling problems Dissertaition, Friedrich-Schiller-University Jena, Jena, Germany (in German)
Rajendran C, Chaudhuri D (1992) An eficient heuristic approach to the scheduling of jobs in a flow shop. Eur J Oper Res 61:318–325
Knowles JD (2002) Local search and hybrid evolutionary algorithms for Pareto optimization. PhD Thesis, Department of Computer Science, University of Reading, UK
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Suresh, R., Mohanasundaram, K. Pareto archived simulated annealing for job shop scheduling with multiple objectives. Int J Adv Manuf Technol 29, 184–196 (2006). https://doi.org/10.1007/s00170-004-2492-x
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DOI: https://doi.org/10.1007/s00170-004-2492-x