Abstract
This work deals with the classical Job Shop Scheduling Problem (JSSP) of minimizing the makespan. Metaheuristics are often used on the JSSP solution, but a performance comparable to the state-of-the-art depends on an efficient exploration of the solutions space characteristics. Thus, it is proposed an intensification approach based on the concepts of attraction basins and big valley. Suboptimal solutions obtained by the metaheuristic genetic algorithm are selected and subjected to intensification, in which a binary Bidimensional Genetic Algorithm (BGA) is utilized to enlarge the search neighborhood from a current solution, to escape of attraction basins. Then, the best solution found in this neighborhood is used as the final point of the path relinking strategy derived from the initial suboptimal solution, for exploring possible big valleys. Finally, the best solution in the path is inserted into the population. Trials with usual instances of the literature show that the proposed approach yields greater results with regards to local search, based on permutation of operations on critical blocks, either on the makespan reduction or on the number of generations, and competitive results regarding the contemporary literature.
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References
Adams J, Balas E, Zawack D (1988) The shifting bottleneck procedure for job shop scheduling. Manage Sci 34(3):391–401. https://doi.org/10.1287/mnsc.34.3.391
Aiex R, Binato S, Resende MGC (2003) Parallel GRASP with path-relinking for job shop scheduling. Parallel Comput 29(4):393–430. https://doi.org/10.1016/S0167-8191(03)00014-0
Akkan C, Karabati S (2004) The two-machine flowshop total completion time problem: improved lower bounds and a branch-and-bound algorithm. Eur J Oper Res 159(2):420–429. https://doi.org/10.1016/S0377-2217(03)00415-6
Akram K, Kamal K, Zeb A (2016) Fast simulated annealing hybridized with quenching for solving job shop scheduling problem. Appl Soft Comput J 49:510–52. https://doi.org/10.1016/j.asoc.2016.08.037
Asadzadeh L (2015) A local search genetic algorithm for the job shop scheduling problem with intelligent agents. Comput Ind Eng 85:376–383. https://doi.org/10.1016/j.cie.2015.04.006
Asadzadeh L (2016) A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy. Comput Ind Eng 102:359–367. https://doi.org/10.1016/j.cie.2016.06.025
Asadzadeh L, Zamanifar K (2010) An agent-based parallel approach for the job shop scheduling problem with genetic algorithms. Math Comput Modell 52(11):1957–1965. https://doi.org/10.1016/j.mcm.2010.04.019
Basseur M, Goëffon A (2015) Climbing combinatorial fitness landscapes. Appl Soft Comput 30:688–70. https://doi.org/10.1016/j.asoc.2015.01.047
Beasley J (1990) OR-library: distributing test problems by electronic mail. J Oper Res Soc 41(1):1069–107. https://doi.org/10.1057/jors.1990.166
Bierwirth C, Kuhpfahl J (2017) Extended GRASP for the job shop scheduling problem with total weighted tardiness objective. Eur J Oper Res 261(3):835–84. https://doi.org/10.1016/j.ejor.2017.03.030
Binato S, Hery WJ, Loewenstern DM et al (2002) A grasp for job shop scheduling. Springer, Boston, MA, pp 59–79. https://doi.org/10.1007/978-1-4615-1507-4_3
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):189–213. https://doi.org/10.1007/s10479-005-3971-7
Bożejko W, Smutnicki C, Uchroński M, et al (2017) Big valley in scheduling problems landscape - Metaheuristics with reduced searching area. In: 2017 22nd International conference on methods and models in automation and robotics, MMAR 2017 pp 458–46. https://doi.org/10.1109/MMAR.2017.8046871
Bożejko W, Gnatowski A, Smutnicki C et al (2018) Local search metaheuristics with reduced searching diameter. In: Pichler F, Quesada-Arencibia A (eds) Computer aided systems theory—EUROCAST 2017, vol 1. Springer, Berlin, pp 447–454. https://doi.org/10.1007/978-3-319-74718-7-54
Fisher H, Thompson GL (1963) Probabilistic learning combinations of local job-shop scheduling rules. In: Muth J, Thompson G (eds) Industrial scheduling. Prentice Hall, Hoboken, pp 225–251
Gao D, Wang GG, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using de algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–327. https://doi.org/10.1109/TFUZZ.2020.3003506
Gao L, Zhang G, Zhang L et al (2011) An efficient memetic algorithm for solving the job shop scheduling problem. Comput Ind Eng 60(4):699–705. https://doi.org/10.1016/j.cie.2011.01.003
Gao L, Li X, Wen X et al (2015) A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem. Comput Ind Eng 88:417–429. https://doi.org/10.1016/j.cie.2015.08.002
Glover F (1997) Tabu search and adaptive memory programming–advances, applications and challenges. In: Barr RS, Helgason RV, Kennington JL (eds) Interfaces in computer science and operations research: advances in metaheuristics, optimization, and stochastic modeling technologies. Springer, Boston, MA, pp 1–7. https://doi.org/10.1007/978-1-4615-4102-8-1
Glover F, Laguna M (1997) Tabu search. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6089-0
Glover F, Samorani M (2019) Intensification, diversification and learning in metaheuristic optimization. J Heurist 25(4):517–520. https://doi.org/10.1007/s10732-019-09409-w
Gonçalves JF, Mendes JJdM, Resende MGC (2005) A hybrid genetic algorithm for the job shop scheduling problem. Eur J Oper Res 167(1):77–9. https://doi.org/10.1016/j.ejor.2004.03.012
Grabowski J, Wodecki M (2005) A very fast tabu search algorithm for job shop problem, vol 30. Springer, Berlin, pp 117–144. https://doi.org/10.1145/2486001.2486037
Grassi F, Schimit PHT, Pereira FH (2016) Dynamic seed genetic algorithm to solve job shop scheduling problems. In: Nääs I, Vendrametto O, Mendes Reis J et al (eds) Advances in production management systems. Initiatives for a sustainable world. Springer, Cham, pp 170–177. https://doi.org/10.1007/978-3-319-51133-7_21
Hasan SM, Sarker R, Essam D et al (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1(1):69–83. https://doi.org/10.1007/s12293-008-0004-5
He L, Chiong R, Li W et al (2022) Multiobjective optimization of energy-efficient job-shop scheduling with dynamic reference point-based fuzzy relative entropy. IEEE Trans Industr Inf 18(1):600–610. https://doi.org/10.1109/TII.2021.3056425
Qing-dao-er ji R, Wang Y (2012) A new hybrid genetic algorithm for job shop scheduling problem. Comput Op Res 39(10):2291–2299. https://doi.org/10.1016/j.cor.2011.12.005
Kuhpfahl J, Bierwirth C (2016) A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective. Comput Op Res 66:44–5. https://doi.org/10.1016/j.cor.2015.07.011
Kurdi M (2016) An effective new island model genetic algorithm for job shop scheduling problem. Comput Op Res 67:132–142. https://doi.org/10.1016/j.cor.2015.10.005
Lawrence S (1984) Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). PhD thesis, Graduate School of Industrial Administration, Carnegie-Mellon University
Lei D (2011) Simplified multi-objective genetic algorithms for stochastic job shop scheduling. Appl Soft Comput J 11(8):4991–4996. https://doi.org/10.1016/j.asoc.2011.06.001
Li M, Wang GG (2022) A review of green shop scheduling problem. Inf Sci 589:478–49. https://doi.org/10.1016/j.ins.2021.12.122
Lozano M, García-Martínez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37(3):481–497. https://doi.org/10.1016/j.cor.2009.02.010
Malan KM, Engelbrecht AP (2014) Fitness landscape analysis for metaheuristic performance prediction. In: Recent advances in the theory and application of fitness landscapes. Emergence, complexity and computation, vol 6. Springer, Berlin, Heidelberg, pp 103–113. https://doi.org/10.1007/978-3-642-41888-4-4
Mattfeld DC, Bierwirth C, Kopfer H (1999) A search space analysis of the job shop scheduling problem. Ann Oper Res 86:441–453. https://doi.org/10.1023/A:1018979424002
Morita M, Ochiai H, Tamura K, et al (2016) Multi-point search combinatorial optimization method based on neighborhood search using evaluation of big valley structure. In: Proceedings—2015 IEEE international conference on systems, man, and cybernetics, SMC 2015, pp 2835–2840,https://doi.org/10.1109/SMC.2015.494
Moser I, Gheorghita M, Aleti A (2017) Identifying features of fitness landscapes and relating them to problem difficulty. Evol Comput 25(3):407–43. https://doi.org/10.1162/evco-a-00177
Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2:1–14. https://doi.org/10.1016/j.swevo.2011.11.003
Nowicki E, Smutnicki C (1996) A fast taboo search algorithm for the job shop problem. Manage Sci 42(6):797–81. https://doi.org/10.2307/2634595
Nowicki E, Smutnicki C (2005) An advanced tabu search algorithm for the job shop problem. J Sched 8(2):145–159. https://doi.org/10.1007/s10951-005-6364-5
Pardalos PM, Shylo OV, Vazacopoulos A (2010) Solving job shop scheduling problems utilizing the properties of backbone and "big valley“. Comput Optim Appl 47(1):61–76. https://doi.org/10.1007/s10589-008-9206-5
Peng B, Lü Z, Cheng T (2015) A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput Op. Res. 53:154–164. https://doi.org/10.1016/j.cor.2014.08.006
Pérez E, Herrera F, Hernández C (2003) Finding multiple solutions in job shop scheduling by niching genetic algorithms. J Intell Manuf 14(3–4):323–33. https://doi.org/10.1023/A:1024649709582
Pérez E, Posada M, Herrera F (2012) Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling. J Intell Manuf 23(3):341–356. https://doi.org/10.1007/s10845-010-0385-4
Pinedo ML (2012) Scheduling: theory, algorithms, and systems, 4th edn. Springer, Boston, MA
Resende MGC, Ribeiro CC (2016) Local search, vol 4. Springer, New York, Chap, pp 63–69. https://doi.org/10.1007/978-1-4939-6530-4-4
Ribeiro CC, Resende MGC (2012) Path-relinking intensification methods for stochastic local search algorithms. J Heurist 18(2):193–214. https://doi.org/10.1007/s10732-011-9167-1
Shao W, Xiao T, Su Z et al (2023) A hybridization of granular adaptive tabu search with path relinking for the multi-depot open vehicle routing problem. Egypt Inform J 24(4):100420. https://doi.org/10.1016/j.eij.2023.100420
Smutnicki C, Bożejko W (2018) Tabu search and solution space analyses. The job shop case. In: Moreno-Díaz R, Pichler F, Quesada-Arencibia A (eds) Computer aided systems theory—EUROCAST 2017. Springer, Cham, pp 383–391
Streeter MJ, Smith SF (2006) How the landscape of random job shop scheduling instances depends on the ratio of jobs to machines. J Art Intell Resarch 26:247–287. https://doi.org/10.1613/jair.2013
Wang GG, Gao D, Pedrycz W (2022) Solving multiobjective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm. IEEE Trans Industr Inf 18(12):8519–8528. https://doi.org/10.1109/TII.2022.3165636
Wang X, Duan H (2014) A hybrid biogeography-based optimization algorithm for job shop scheduling problem. Comput Ind Eng 73:96–114. https://doi.org/10.1016/j.cie.2014.04.006
Watson JP (2010) An introduction to fitness landscape analysis and cost models for local search. In: Handbook of metaheuristics. pp 599–623. https://doi.org/10.1007/978-1-4419-1665-5-20
Wong LP, Puan CY, Low MYH, et al (2008) Bee colony optimization algorithm with big valley landscape exploitation for job shop scheduling problems. In: 2008 winter simulation conference, pp 2050–205. https://doi.org/10.1109/WSC.2008.4736301
Wong LP, Puan CY, Low MYH et al (2010) Bee colony optimisation algorithm with big valley landscape exploitation for job shop scheduling problems. Int J Bio Inspir Comput 2(2):85–99. https://doi.org/10.1504/IJBIC.2010.032125
Zhang R, Chiong 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. J Clean Prod 112:3361–3375. https://doi.org/10.1016/j.jclepro.2015.09.097
Funding
This work was partially supported by Grant #2018/08326-6, São Paulo Research Foundation (FAPESP). Additionally, the authors would like to thank Universidade Nove de Julho for the support and the scholarship granted to the first of them.
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All authors contributed to the study conception and design. Material preparation, data collection, analysis and the first draft of the manuscript were performed by AFCR. FHP commented on previous versions of the manuscript, read and approved the final manuscript.
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Castello Rosa, A.d.F., Pereira, F.H. An intensification approach based on fitness landscape characteristics for job shop scheduling problem. J Comb Optim 47, 77 (2024). https://doi.org/10.1007/s10878-024-01176-0
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DOI: https://doi.org/10.1007/s10878-024-01176-0