Abstract
The paper considers the problems of scheduling n jobs that are released over time on a machine in order to optimize one or more objectives. The problems are dynamic single-machine scheduling problems (DSMSPs) with job release dates and needed to be solved urgently because they exist widely in practical production environment. Gene expression programming-based scheduling rules constructor (GEPSRC) was proposed to construct effective scheduling rules (SRs) for DSMSPs with job release dates automatically. In GEPSRC, Gene Expression Programming (GEP) worked as a heuristic search to search the space of SRs. Many experiments were conducted, and comparisons were made between GEPSRC and some previous methods. The results showed that GEPSRC achieved significant improvement.
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Jakobovic D, Budin L (2006) Dynamic scheduling with genetic programming. Lect Notes Comput Sci 3905:73–84
Holthaus O, Rajendran C (1997) New dispatching rules for scheduling in a job shop - an experimental study. Int J Adv Manuf Technol 13(2):148–153
Kianfar K, Ghomi SMT, Karimi B (2009) New dispatching rules to minimize rejection and tardiness costs in a dynamic flexible flow shop. Int J Adv Manuf Technol 45(7–8):759–771
Wang JB, Wang LY, Wang D, Huang X, Wang XR (2009) A note on single-machine total completion time problem with general deteriorating function. Int J Adv Manuf Technol 44(11–12):1213–1218
Ham M, Fowler JW (2008) Scheduling of wet etch and furnace operations with next arrival control heuristic. Int J Adv Manuf Technol 38(9–10):1006–1017
Norman B, Bean J (1999) A genetic algorithm methodology for complex scheduling problems. Nav Res Logist 46:199–211
Panneerselvam R (2006) Simple heuristic to minimize total tardiness in a single machine scheduling problem. Int J Adv Manuf Technol 30(7–8):722–726
Haupt R (1989) A survey of priority rule-based scheduling. OR Spektrum 11:3–16
Catoni O (1998) Solving scheduling problems by simulated annealing. SIAM J Control Optim 36(5):1639–1675
Finke DA, Medeiros DJ, Traband MT (2002) Shop scheduling using Tabu search and simulation. In: Yucesan E, Chen CH, Snowdon JL, Charnes JM (eds) Proceedings of the 2002 winter simulation conference, 8–11 December 2002 San Diego. WSC, New York, pp 1013–1017
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison–Wesley, Boston
Aytug H, Lawley MA, Mckay K, Mohan S, Uzsoy R (2005) Executing production schedules in the face of uncertainties: a review and some future directions. Eur J Oper Res 161(1):86–110
Graham RL (1966) Bounds for certain multiprocessor anomalies. Bell Syst Tech J 45:1563–1581
Smith WE (1956) Various optimizers for single-stage production. Nav Res Logist Q 3:59–66
Baker KR (1974) Introduction to sequencing and scheduling. Wiley, New York
Hoogeveen JA, Vestjens APA (1996) Optimal on-line algorithms for single-machine scheduling. Lect Notes Comput Sci 1084:404–414
Phillips C, Stein C, Wein J (1995) Minimizing average completion time in the presence of release dates. Math Program 82:199–223
Kellerer H, Tautenhahn T, Woeginger GJ (1999) Approximability and nonapproximability results for minimizing total flow time on a single machine. SIAM J Comput 28(4):1155–1166
Guo Y, Lim A, Rodriguesc B, Yu S (2004) Minimizing total flow time in single machine environment with release time: an experimental analysis. Comput Ind Eng 47:123–140
Sgall J (1998) On-line scheduling. Lect Notes Comput Sci 442:196–231
Pruhs K, Sgall J, Torng E (2004) Online scheduling. In: Leung JYT (ed) Handbook of scheduling: algorithms, models and performance analysis. Chapman & Hall/CRC, Boca Raton
Blackstone JH, Phillips DT, Hogg GL (1982) A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res 20(1):27–45
Jeong KC, Kim YD (1998) A real-time scheduling mechanism for a flexible manufacturing system using simulation and dispatching rules. Int J Prod Res 36(9):2609–2626
Yin YL, Rau H (2006) Dynamic selection of sequencing rules for a class-based unit-load automated storage and retrieval system. Int J Adv Manuf Technol 29(11–12):1259–1266
Chen CC, Yih Y (1996) Identifying attributes for knowledge-base development in dynamic scheduling environments. Int J Prod Res 34(6):1739–1755
El-Bouri A, Shah P (2006) A neural network for dispatching rule selection in a job shop. Int J Adv Manuf Technol 31(3–4):342–349
Aytug H, Koehler GJ, Snowdon JL (1994) Genetic learning of dynamic scheduling within a simulation environment. Comput Oper Res 21(8):909–925
Trappey AJC, Lin GYP, Ku CC, Ho PS (2007) Design and analysis of a rule-based knowledge system supporting intelligent dispatching and its application in the TFT-LCD industry. Int J Adv Manuf Technol 35(3–4):385–393
Singh A, Mehta NK, Jain PK (2007) Multicriteria dynamic scheduling by swapping of dispatching rules. Int J Adv Manuf Technol 34(9–10):988–1007
Yang HB, Yan HS (2009) An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell. Int J Adv Manuf Technol 42(3–4):312–320
Geiger CD, Uzsoy R, Aytug H (2006) Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J Sched 9:7–34
Koza JR (2007) Introduction to genetic programming. In: Lipson H (ed) Proceedings of GECCO 2007: genetic and evolutionary computation conference, 7-11 July 2007 London. ACM, London, pp 3323–3365
Dimopoulos C, Zalzala AMS (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv Eng Softw 32(6):489–498
Yin WJ, Liu M, Wu C (2003) Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Sarker R et al (eds) Proceeding of CEC2003: congress on evolutionary computation, 9-12 December 2003 Canberra, Australia. IEEE, Piscataway, pp 1050–1055
Atlan L, Bonnet J, Naillon M (1994) Learning distributed reactive strategies by genetic programming for the general job shop problem. In: Dankel D, Stewan J (eds) Proceedings of The 7th annual Florida artificial intelligence research symposium, 5-6 May 1994 Pensacola Beach, Florida, USA. IEEE, Piscataway
Miyashita K (2000) Job-shop scheduling with genetic programming. In: Whitley LD, Goldberg DE et al (eds) Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2000), 8-12 July 2000 Las Vegas, Nevada, USA. Kaufmann, San Francisco, pp 505–512
Ho NB, Tay JC (2003) Evolving dispatching rules for solving the flexible job shop problem. In: Corne D (ed) Proceedings of the 2005 IEEE congress on evolutionary computation, 2–4 September 2005 Edinburgh, UK. IEEE, Piscataway, pp 2848–2855
Tay JC, Ho NB (2007) Designing dispatching rules to minimize total tardiness. Stud Comput Intel 49:101–124
Tay JC, Ho NB (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problem. Comput Ind Eng 54(3):453–473
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129
Hardy Y, Steeb WH (2002) Gene expression programming and one-dimensional chaotic maps. Int J Mod Phys C 13(1):13–24
Jayamohan MS, Rajendran C (2000) New dispatching rules for shop scheduling: a step forward. Int J Prod Res 38:563–586
Montagne ER (1969) Sequencing with time delay costs. Industrial Engineering Research Bulletin, Arizona State University 5
Oliver H, Chandrasekharan R (1997) Efficient dispatching rules for scheduling in a job shop. Int J Prod Econ 48(1):87–105
Kanet JJ, Li XM (2004) A weighted modified due date rule for sequencing to minimize weighted tardiness. J Sched 7(4):261–276
Bhaskaran K, Pinedo M (1992) Dispatching. In: Salvendy G (ed) Handbook of industrial engineering. Wiley, New York, pp 2184–2198
Vepsalainen APJ, Morton TE (1987) Priority rules for job shops with weighted tardiness costs. Manage Sci 33:1035–1047
Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: Grefenstette JJ (ed) Proceedings of The First International Conference on Genetic Algorithms and their Applications, 24-26 July 1985 Pittsburgh, USA. Erlbaum, Hillsdale, pp 183–187
Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT, Cambridge
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Nie, L., Shao, X., Gao, L. et al. Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int J Adv Manuf Technol 50, 729–747 (2010). https://doi.org/10.1007/s00170-010-2518-5
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DOI: https://doi.org/10.1007/s00170-010-2518-5