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Discrete hybrid cuckoo search and simulated annealing algorithm for solving the job shop scheduling problem

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Abstract

The job shop scheduling problem (JSSP) is a popular NP-hard scheduling problem that simulates the scheduling problems of different real-world applications in fields such as manufacturing, engineering, and planning. In general, many optimization-based scheduling algorithms are guaranteed to provide an optimal solution for small instances of the JSSP, but do not perform well with large instances of the JSSP. The hybrid cuckoo search and simulated annealing (CSA) algorithm is a new hybrid optimization algorithm for solving continuous optimization problems. It incorporates the optimization operators of the simulated annealing algorithm into the cuckoo search algorithm. In this paper, we present discrete CSA (DCSA) which is a discrete variation of CSA for solving the JSSP. DCSA incorporates four modifications into CSA. First, it uses the opposition-based learning method in the initialization step to produce diverse candidate solutions. Second, it uses a combination of the variable neighborhood search (VNS) and Lévy-flight methods for better exploration of the search space. Third, it uses the elite opposition-based learning method before the abandon step of CSA to jump out of local optima. Finally, it uses the smallest position value with the JSSP to convert the continuous candidate solutions generated by CSA into discrete ones. DCSA was examined and compared to six popular optimization-based scheduling algorithms (DABC (Yin et al. in Sci Res Essays 6:2578–2596, 2011), PSO-VNS (Tasgetiren et al. in Int J Oper Res 3:120–135, 2006), DCS (Quaarab et al., in: Yang (ed) Recent advances in swarm intelligence and evolutionary computation, Springer, New York, 2015), GWO (Jiang and Zhang in IEEE Access 6:26231–26240, 2018), DWPA (Wang et al. in 2019 5th International Conference on Control, Automation and Robotics ICCAR 2019 581–585, 2019) and DGA (Kalshetty et al. in J Sci Res 64:310–321, 2020) using 34 JSSP instances from the OR-Library: Fisher and Thompson (3 instances), and Lawrence (31 instances). The experimental and statistical results indicate that DCSA converges faster to the Best-Known Solution for 29 instances out of the 34 tested JSSP instances and requires less computational time for the 34 tested instances than the computational times of the other algorithms.

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References

  1. Yin M, Li X, Zhou J (2011) An efficient job shop scheduling algorithm based on artificial bee colony. Sci Res Essays 6:2578–2596

    Google Scholar 

  2. Tasgetiren MF, Sevkli M, Liang Y-C, Yenisey MM (2006) A particle swarm optimization and differential evolution algorithms for job shop scheduling problem. Int J Oper Res 3:120–135

    MathSciNet  MATH  Google Scholar 

  3. Ouaarab A, Ahiod B, Yang X-S (2015) Discrete cuckoo search applied to job shop scheduling problem. In: Yang X-S (ed) Recent advances in swarm intelligence and evolutionary computation. Springer, Cham, pp 121–137. https://doi.org/10.1007/978-3-319-13826-8_7

    Chapter  Google Scholar 

  4. Jiang T, Zhang C (2018) Application of grey wolf optimization for solving combinatorial problems: job shop and flexible job shop scheduling cases. IEEE Access 6:26231–26240. https://doi.org/10.1109/ACCESS.2018.2833552

    Article  Google Scholar 

  5. Wang F, Tian Y, Wang X (2019) A discrete wolf pack algorithm for job shop scheduling problem. In: 2019 5th International Conference on Control, Automation and Robotics ICCAR 2019. pp 581–585. https://doi.org/10.1109/ICCAR.2019.8813444

  6. Kalshetty YR, Adamuthe AC, Kumar SP (2020) Genetic algorithms with feasible operators for solving job shop scheduling problem. J Sci Res 64:310–321. https://doi.org/10.37398/jsr.2020.640157

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. Chakraborty S, Bhowmik S (2013) Job shop scheduling using simulated annealing. In: First International Conference on Computation and Communication Advancement. pp 69–73

  9. Chaouch I, Driss OB, Ghedira K (2017) A modified ant colony optimization algorithm for the distributed job shop scheduling problem. Procedia Comput Sci 112:296–305. https://doi.org/10.1016/j.procs.2017.08.267

    Article  Google Scholar 

  10. Zaher H, El-sherbieny M (2017) Bat algorithm for job shop scheduling problem. J Multidiscip Eng Sci Technol 4:6758–6763

    Google Scholar 

  11. Banharnsakun A, Sirinaovakul B, Achalakul T (2012) Job shop scheduling with the best-so-far ABC. Eng Appl Artif Intell 25:583–593

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. Khadwilard A, Pongcharoen P (2013) Job shop scheduling optimisation using harmony search algorithm. Naresuan Univ J Sci Technol pp 8–14

  14. Rameshkumar K, Rajendran C (2018) A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899X/310/1/012143

    Article  Google Scholar 

  15. Udaiyakumar KC, Chandrasekaran M (2014) Application of firefly algorithm in job shop scheduling problem for minimization of makespan. Procedia Eng 97:1798–1807. https://doi.org/10.1016/j.proeng.2014.12.333

    Article  Google Scholar 

  16. Wang H, Wang W, Sun H et al (2017) A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Comput 21:4297–4307. https://doi.org/10.1007/s00500-016-2062-9

    Article  Google Scholar 

  17. Alawad NA, Abed-alguni BH (2021) Discrete island-based cuckoo search with highly disruptive polynomial mutation and opposition-based learning strategy for scheduling of workflow applications in cloud environments. Arab J Sci Eng 46:3213–3233. https://doi.org/10.1007/s13369-020-05141-x

    Article  Google Scholar 

  18. Alkhateeb F, Abed-Alguni BH (2017) A hybrid cuckoo search and simulated annealing algorithm. Intell Syst 28(4):683–698

    Article  Google Scholar 

  19. Mousavirad SJ, Rahnamayan S (2020) One-array differential evolution algorithm with a novel replacement strategy for numerical optimization. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 2514–2519

  20. Mousavirad SJ, Rahnamayan S (2019) Differential evolution algorithm based on a competition scheme. In: 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, pp 929–934

  21. Mousavirad SJ, Rahnamayan S (2020) A novel center-based differential evolution algorithm. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1–8

  22. Abed-Alguni BH, Klaib AF (2020) Hybrid whale optimisation and β-hill climbing algorithm for continuous optimisation problems. Int J Comput Sci Math. https://doi.org/10.1504/IJCSM.2020.112650

    Article  MathSciNet  Google Scholar 

  23. Zhu J, Shao ZH, Chen C (2019) An improved whale optimization algorithm for job-shop scheduling based on quantum computing. Int J Simul Model 18:521–530. https://doi.org/10.2507/ijsimm18(3)co13

    Article  Google Scholar 

  24. Liu M, Yao X, Li Y (2020) Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Appl Soft Comput J 87:105954. https://doi.org/10.1016/j.asoc.2019.105954

    Article  Google Scholar 

  25. Abed-Alguni BH, Klaib AF, Nahar KMO (2019) Island-based whale optimisation algorithm for continuous optimisation problems. Int J Reason Intell Syst 11:319–329

    Google Scholar 

  26. Alawad NA, Abed-alguni BH (2021) Discrete Jaya with refraction learning and three mutation methods for the permutation flow shop scheduling problem. J Supercomput pp 1–22

  27. Alkhateeb F, Abed-Alguni BH (2019) A hybrid cuckoo search and simulated annealing algorithm. J Intell Syst 28:683–698

    Article  Google Scholar 

  28. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing optimization by simulated annealing. Science 80(220):671–680

    Article  Google Scholar 

  29. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009—Proceedings. pp 210–214

  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. IEEE, pp 1942–1948

  31. Yang X, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29:464–483

    Article  Google Scholar 

  32. Abed-alguni BH, Alkhateeb F (2017) Novel selection schemes for cuckoo search. Arab J Sci Eng. https://doi.org/10.1007/s13369-017-2663-3

    Article  Google Scholar 

  33. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24:1097–1100

    Article  MathSciNet  Google Scholar 

  34. Huang KW, Girsang AS, Wu ZX, Chuang YW (2019) A hybrid crow search algorithm for solving permutation flow shop scheduling problems. Appl Sci. https://doi.org/10.3390/app9071353

    Article  Google Scholar 

  35. Fisher H, Thompson GL (1963) Probabilistic learning combinations of local job-shop scheduling rules. Ind Sched pp 225–251

  36. Lawrence S (1984) Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Grad Sch Ind Adm Carnegie-Mellon Univ

  37. Nazif H (2015) Solving job shop scheduling problem using an ant colony algorithm. J Asian Sci Res 5:261–268. https://doi.org/10.18488/journal.2/2015.5.5/2.5.261.268

    Article  Google Scholar 

  38. Singh S, Singh KP (2015) Cuckoo search optimization for job shop scheduling problem. Adv Intell Syst Comput 335:99–111. https://doi.org/10.1007/978-81-322-2217-0_9

    Article  Google Scholar 

  39. Zaman HRR, Gharehchopogh FS (2021) An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput pp 1–35

  40. Jiang T (2018) A hybrid grey wolf optimization for job shop scheduling problem. Int J Comput Intell Appl 17:1–12. https://doi.org/10.1142/S1469026818500165

    Article  Google Scholar 

  41. Gao K, Yang F, Zhou M et al (2018) Flexible job-shop rescheduling for new job insertion by using discrete Jaya algorithm. IEEE Trans Cybern 49:1944–1955

    Article  Google Scholar 

  42. Abed-Alguni BH, Paul DJ (2020) Hybridizing the cuckoo search algorithm with different mutation operators for numerical optimization problems. J Intell Syst. https://doi.org/10.1515/jisys-2018-0331

    Article  Google Scholar 

  43. Fernandez-Viagas V, Molina-Pariente JM, Framinan JM (2020) Generalised accelerations for insertion-based heuristics in permutation flowshop scheduling. Eur J Oper Res 282:858–872

    Article  MathSciNet  Google Scholar 

  44. Tasgetiren MF, Sevkli M, Liang YC, Gencyilmaz G (2004) Particle swarm optimization algorithm for permutation flowshop sequencing problem. In: Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 3172 LNCS. pp 382–389. https://doi.org/10.1007/978-3-540-28646-2_38

  45. Gharehchopogh FS, Maleki I, Dizaji ZA (2021) Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evol Intell pp 1–32

  46. Gupta S, Deep K (2019) A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Syst Appl 119:210–230

    Article  Google Scholar 

  47. Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23. https://doi.org/10.1016/j.swevo.2017.09.010

    Article  Google Scholar 

  48. Zhou X, Wu Z, Wang H et al (2013) Elite opposition-based particle swarm optimization. Acta Electron Sin 41:1647–1652

    Google Scholar 

  49. Singh MK (2013) Evaluating levy flight parameters for random searches in a 2D space

  50. Wang K, Ma WQ, Luo H, Qin H (2016) Coordinated scheduling of production and transportation in a two-stage assembly flowshop. Int J Prod Res 54:6891–6911

    Article  Google Scholar 

  51. Beasley JE (1990) OR-Library: distributing test problems by electronic mail. J Oper Res Soc 41:1069–1072

    Article  Google Scholar 

  52. Bouzidi A, Riffi ME (2014) Cat swarm optimization to solve job shop scheduling problem. In: 2014 Third IEEE International Colloquium in Information Science and Technology (CIST). IEEE, pp 202–205

  53. Abed-alguni BH, Barhoush M (2018) Distributed grey wolf optimizer for numerical optimization problems. Jordanian J Comput Inf Technol 4:130–149

    Google Scholar 

  54. Abed-alguni BH, Alawad NA (2021) Distributed grey wolf optimizer for scheduling of workflow applications in cloud environments. Appl Soft Comput 102:107113

    Article  Google Scholar 

  55. Abed-alguni BH (2019) Island-based cuckoo search with highly disruptive polynomial mutation. Int J Artif Intell 17:57–82

    Google Scholar 

  56. Ghafori S, Gharehchopogh FS (2021) Advances in spotted hyena optimizer: a comprehensive survey. Arch Comput Methods Eng pp 1–22

  57. Gharehchopogh FS, Abdollahzadeh B (2021) An efficient harris hawk optimization algorithm for solving the travelling salesman problem. Cluster Comput pp 1–25

  58. Gharehchopogh FS, Farnad B, Alizadeh A (2021) A farmland fertility algorithm for solving constrained engineering problems. Concurr Comput Pract Exp p e6310

  59. Mousavirad SJ, Schaefer G, Esmaeili L, Korovin I (2020) On improvements of the human mental search algorithm for global optimisation. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1–8

  60. Abed-alguni BH (2018) Action-selection method for reinforcement learning based on cuckoo search algorithm. Arab J Sci Eng 43:6771–6785

    Article  Google Scholar 

  61. Abed-alguni BH (2017) Bat Q-learning algorithm. Jordan J Comput Inf Technol 3:56–77

    Google Scholar 

  62. Abed-alguni BH, Ottom MA (2018) Double delayed Q-learning. Int J Artif Intell 16:41–59

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Sciences, Yarmouk University, Irbid, Jordan

    Faisal Alkhateeb, Bilal H. Abed-alguni & Mohammad Hani Al-rousan

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  1. Faisal Alkhateeb
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  2. Bilal H. Abed-alguni
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Correspondence to Bilal H. Abed-alguni.

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Alkhateeb, F., Abed-alguni, B.H. & Al-rousan, M.H. Discrete hybrid cuckoo search and simulated annealing algorithm for solving the job shop scheduling problem. J Supercomput 78, 4799–4826 (2022). https://doi.org/10.1007/s11227-021-04050-6

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