Advertisement

Extended Genetic Algorithm for solving open-shop scheduling problem

  • Ali Asghar Rahmani Hosseinabadi
  • Javad Vahidi
  • Behzad Saemi
  • Arun Kumar Sangaiah
  • Mohamed Elhoseny
Methodologies and Application

Abstract

Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.

Keywords

Extended Genetic Algorithm Makespan Crossover Mutation Open-shop scheduling 

Notes

Acknowledgements

The authors are very thankful for the suggested comments of the Editor in Chief and reviewers to improve our paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ahmdizar F, Hosseinabadi M (2012) A novel hybrid genetic algorithm for the open-shop scheduling problem. Int J Adv Manuf Technol 62:775–787CrossRefGoogle Scholar
  2. Alinaghian M, Amanipour H, Tirkolaee EB (2014) Enhancement of inventory management approaches in vehicle routing-cross docking problems. J Supply Chain Manag Syst 3(3):27–34Google Scholar
  3. Azadeh A, Goldansaz SM, Zahedi-Anaraki A (2016) Solving and optimizing a bi-objective open-shop scheduling problem by a modified genetic algorithm. Int J Adv Manuf Technol 85:1603–1613CrossRefGoogle Scholar
  4. Babaee Tirkolaee E, Alinaghian M, Bakhshi Sasi M, Seyyed Esfahani MM (2016) Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: a waste collection application. J Ind Eng Manag Stud 3(1):61–76Google Scholar
  5. Bai D, Tang L (2013) Open shop scheduling problem to minimize makespan with release dates. Appl Math Model 37:2008–2015MathSciNetCrossRefzbMATHGoogle Scholar
  6. Baia D, Zhang Z, Zhang Q (2016) Flexible open-shop scheduling problem to minimize makespan. Comput Oper Res 67:207–215MathSciNetCrossRefzbMATHGoogle Scholar
  7. Chen Y, Zhang A, Chen G, Dong J (2013) Approximation algorithms for parallel open-shop scheduling. Inf Process Lett 113:220–224MathSciNetCrossRefzbMATHGoogle Scholar
  8. Ciro GC, Dugardin F, Yalaoui F, Kelly R (2015) A fuzzy ant colony optimization to solve an open-shop scheduling problem with multi-skills resource constraints. In: 8th IFAC conference on manufacturing modelling, management and control MIM 2015, vol 48, pp 715–720Google Scholar
  9. Ciro GC, Dugardin F, Yalaoui F, Kelly R (2016) A NSGA-II and NSGA-III comparison for solving an open-shop scheduling problem with resource constraints. In: 8th IFAC conference on manufacturing modelling, management and control MIM 2016, vol 49, pp 1272–1277Google Scholar
  10. Colak S, Agarwal A (2005) Non-greedy heuristics and augmented neural networks for the open-shop scheduling problem. Naval Res Logist 52:631–644MathSciNetCrossRefzbMATHGoogle Scholar
  11. Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach. IEEE Trans Evol Comput 18:602–622CrossRefGoogle Scholar
  12. Fang H-L, Ross P, Corne D (1994) A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: Proceedings of the 11th European conference on artificial intelligence, pp 590–594Google Scholar
  13. Farahabadi AB, Hosseinabadi AR (2013) Present a new hybrid algorithm scheduling flexible manufacturing system consideration cost maintenance. Int J Sci Eng Res 4(9):1870–1875Google Scholar
  14. Goldansaz SM, Jolai F, ZahediAnaraki AH (2013) A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open-shop. Appl Math Model 37:9603–9616MathSciNetCrossRefGoogle Scholar
  15. Gonzalez T, Sahni S (1976) Open-shop scheduling to minimize finish time. J Assoc Comput Mach 23:665–679MathSciNetCrossRefzbMATHGoogle Scholar
  16. Graham RL, Lawler EL, Lenstra JK, Kan AHG (1979) Optimization and approximation in deterministic machine scheduling: a survey. Ann Discrete Math 5:287–326MathSciNetCrossRefzbMATHGoogle Scholar
  17. Harmanani HM, Ghosn SB (2016) An efficient method for the open-shop scheduling problem using simulated annealing. Inf Technol New Gener Adv Intell Syst Comput 448:1183–1193CrossRefGoogle Scholar
  18. Hosseinabadi AR, Yazdanpanah M, Rostami AS (2012) A new search algorithm for solving symmetric traveling salesman problem based on gravity. World Appl Sci J 16(10):1387–1392Google Scholar
  19. Hosseinabadi AR, Farahabadi AB, Rostami MS, Lateran AF (2013) Presentation of a new and beneficial method through problem solving timing of open-shop by random algorithm gravitational emulation local search. Int J Comput Sci Issues 10(1, No 2):745–752Google Scholar
  20. Hosseinabadi AR, Kardgar M, Shojafar M, Shamshirband S, Abraham A (2014) GELS-GA: hybrid metaheuristic algorithm for solving multiple travelling salesman problem. In: 14th IEEE ISDA, pp 76–81Google Scholar
  21. Hosseinabadi AR, Siar H, Shamshirband S, Shojafar M, Nizam MH, Nasir M (2015) Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises. Ann Oper Res 229(1):451–474MathSciNetCrossRefzbMATHGoogle Scholar
  22. Hosseinabadi AR, Vahidi J, Balas VE, Mirkamali SS (2016a) OVRP_GELS: solving open vehicle routing problem using the gravitational emulation local search algorithm. Neural Comput Appl 29(10):955–968CrossRefGoogle Scholar
  23. Hosseinabadi AR, Kardgar M, Shojafar M, Shamshirband Sh, Abraham A (2016b) Gravitational search algorithm to solve open vehicle routing problem. In: 6th international conference on innovations in bio-inspired computing and applications (IBICA 2015). Chapter advances in intelligent systems and computing, Kochi, India. Springer, pp 93–103Google Scholar
  24. Hosseinabadi AR, Alavipour F, Shamshirbnd Sh, Balas VE (2017a) A novel meta-heuristic combinatory method for solving capacitated vehicle location-routing problem with hard time windows. Springer International Publishing Switzerland (2017) Transportation systems, advances in intelligent systems and computing, vol 454. Springer, China, pp 707–728Google Scholar
  25. Hosseinabadi AR, Rostami NSH, Kardgar M, Mirkamali SS, Abraham A (2017b) A new efficient approach for solving the capacitated vehicle routing problem using the gravitational emulation local search algorithm. Appl Math Model 49:663–679MathSciNetCrossRefGoogle Scholar
  26. Karagöz S, Yıldız AR (2017) A comparison of recent metaheuristic algorithms for crashworthiness optimisation of vehicle thin-walled tubes considering sheet metal forming effects. Int J Veh Des 73:179–188CrossRefGoogle Scholar
  27. Khuri S, Miryala SR (1999) Genetic algorithms for solving open-shop scheduling problems. In: Proceedings of the 9th Portuguese conference on artificial intelligence. Lecture Notes in Computer Science, pp 357–368Google Scholar
  28. Koulamas C, Kyparisis GJ (2015) The three-machine proportionate Open-shop and mixed shop minimum makespan problems. Eur J Oper Res 243:70–74MathSciNetCrossRefzbMATHGoogle Scholar
  29. Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys DB (1993) Sequencing and scheduling: algorithms and complexity. In: Graves SC, Rinnooy Kan AHG, Zipkin PH (eds) Handbook in operations research and management science, logistics of production and inventory, 4th edn. North-Holland, Amsterdam, pp 445–522CrossRefGoogle Scholar
  30. Low C, Yeh Y (2009) Genetic algorithm-based heuristics for an open-shop scheduling problem with setup, processing, and removal times separated. Robot Comput Integr Manuf 25:314–322CrossRefGoogle Scholar
  31. Mirmohammadi SH, Babaee Tirkolaee E, Goli A, Dehnavi-Arani S (2017) The periodic green vehicle routing problem with considering of time-dependent urban traffic and time windows. Iran Univ Sci Technol 7(1):143–156Google Scholar
  32. Noori-Darvish S, Tavakkoli-Moghaddam R (2011) Solving a bi-objective open-shop scheduling problem with fuzzy parameters. J Appl Oper Res 3:59–74Google Scholar
  33. Pinedo M (1995) Scheduling: theory algorithms and systems. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  34. Prins C (2000) Competitive genetic algorithms for the open-shop scheduling problem. Math Methods Oper Res 52:389–411MathSciNetCrossRefzbMATHGoogle Scholar
  35. Rostami AS, Mohanna F, Keshavarz H, Hosseinabadi AR (2015) Solving multiple traveling salesman problem using the gravitational emulation local search algorithm. Appl Math Inf Sci 9(2):699–709MathSciNetGoogle Scholar
  36. Shamshirband S, Shojafar M, Hosseinabadi AR, Kardgar M, Nizam MH, Nasir M, Ahmad R (2015a) OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises. Ann Oper Res 229(1):743–758MathSciNetCrossRefzbMATHGoogle Scholar
  37. Shamshirband S, Shojafar M, Hosseinabadi AR, Abraham A (2015b) OVRP_ICA: an imperialist-based optimization algorithm for the open vehicle routing problem. In: International conference on hybrid artificial intelligence systems (HAIS), Chapter Springer LNCS, vol 9121, pp 221–233Google Scholar
  38. Shojafar M, Kardgar M, Hosseinabadi AR, Shamshirband Sh, Abraham A (2016) TETS: a genetic-based scheduler in cloud computing to decrease energy and makespan. In: The 15th international conference on hybrid intelligent systems (HIS 2015), Chapter advances in intelligent systems and computing, vol 420. Seoul, South Korea. Springer, pp 103–115Google Scholar
  39. Taillard E (1993) Benchmarks for basic scheduling problems. Eur J Oper Res 64:278–285CrossRefzbMATHGoogle Scholar
  40. Talbi, El-Ghazali (2009) Metaheuristics: from design to impelementation. Wiley, Hoboken. ISBN: 978-0-470-27858-1, 1–624Google Scholar
  41. Tavakkolai H, Hosseinabadi AR, Yadollahi M, Mohammadpour T (2015) Using gravitational search algorithm for in advance reservation of resources in solving the scheduling problem of works in workflow workshop environment. Indian J Sci Technol 8(11):1–16CrossRefGoogle Scholar
  42. Tellache NEH, Boudhar M (2017) Open shop scheduling problems with conflict graphs. Discrete Appl Math 227:103–120MathSciNetCrossRefzbMATHGoogle Scholar
  43. Tirkolaee EB, Goli A, Bakhshi M, Mahdavi I (2017) Robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numer Algebra Control Optim 7(4):417–433MathSciNetCrossRefzbMATHGoogle Scholar
  44. Tirkolaee EB, Alinaghian A, Hosseinabadi AAR, Sasi MB, Sangaiah AK (2018) An improved ant colony optimization for the multi-trip Capacitated Arc Routing Problem. Comput Electr Eng. ISSN 0045-7906.  https://doi.org/10.1016/j.compeleceng.2018.01.040
  45. Vasant P (2014) Handbook of research on artificial intelligence techniques and algorithms. IGI Global 1–796(ISBN13):9781466672581Google Scholar
  46. Vasant P, Wilhelm Weber G, Dieu VN (2016) Handbook of research modern optimization algorithms and applications in engineering and economics. IGI Global 1–960(ISBN13):9781466696440zbMATHGoogle Scholar
  47. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88CrossRefGoogle Scholar
  48. Yıldız AR, Ozturk N, Kaya N, Ozturk F (2007) Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm. Struct Multidiscip Optim 34:317–332CrossRefGoogle Scholar
  49. Zhang ZH, Bai D (2014) An extended study on an open-shop scheduling problem using the minimisation of the sum of quadratic completion times. Appl Math Comput 230:238–247MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Ayatollah Amoli BranchIslamic Azad UniversityAmolIran
  2. 2.Iran University of Science and TechnologyTehranIran
  3. 3.Computer DepartmentKavosh Institute of Higher EducationMahmood AbadIran
  4. 4.School of Computing Science and EngineeringVellore Institute of Technology (VIT)VelloreIndia
  5. 5.Faculty of Computers and InformationMansoura UniversityMansouraEgypt

Personalised recommendations