Skip to main content
SpringerLink
Log in

Solving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu search

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Manpower scheduling is a complicated problem to solve that strives to satisfy employers’ objectives and employees’ preferences as much as possible by generating fairly desirable schedules. But sometimes, objectives and preferences may not be determined precisely. This problem causes manpower scheduling takes the fuzzy nature. This paper presents a new fuzzy multi-objective mathematical model for a multi-skilled manpower scheduling problem considering imprecise target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented mathematical model and two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Since the complexity of a manpower scheduling problem is NP-hard, the single-objective models are solved by two meta-heuristics, namely particle swarm optimization and elite tabu search. Eventually, the performance of the proposed algorithms is verified and the results are compared with each other to select the best schedules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Valls V, Perez A, Quintanilla S (2009) Skilled workforce scheduling in service centers. Eur J Oper Res 193:791–804

    Article  MATH  Google Scholar 

  2. Costa MC, Jarray F, Picouleau C (2006) An acyclic days-off scheduling problem. 4OR. A Quarterly Journal of Operations Research 4:73–85

    MathSciNet  MATH  Google Scholar 

  3. Mohan S (2008) Scheduling part-time personnel with availability restrictions and preferences to maximize employee satisfaction. Mathematical and Computer Modeling 48:1806–1813

    Article  MATH  Google Scholar 

  4. Cai X, Li KN (2000) A genetic algorithm for scheduling staff of mixed skills under multi-criteria. Eur J Oper Res 125:359–369

    Article  MATH  Google Scholar 

  5. Eitzen G, Panton D, Mills G (2004) Multi-skilled workforce optimization. Ann Oper Res 127:359–372

    Article  MATH  Google Scholar 

  6. Techawiboonwong A, yenradee P, Das SK (2006) A master scheduling model with skilled and unskilled temporary workers. Int J Prod Econ 103:798–809

    Article  Google Scholar 

  7. Tien J, Kamiyama A (1982) On manpower scheduling algorithms. SIAM Rev 24(3):275–287

    Article  MathSciNet  MATH  Google Scholar 

  8. Baker KR (1976) Workforce allocation in cyclical scheduling problems: a survey. Operational Research Quarterly 27(1ii):155–167

    Article  Google Scholar 

  9. Ernst AT, Jiang H, Krishnamoorthy M, Sier D (2004) Staff scheduling and rostering: a review of applications, methods and models. Eur J Oper Res 153:3–27

    Article  MathSciNet  MATH  Google Scholar 

  10. Edie L (1954) Traffic delays at toll booths. J Oper Res Soc Am 2(2):107–138

    Article  Google Scholar 

  11. Topaloglu S (2009) A shift scheduling model for employees with different seniority levels and an application in healthcare. Eur J Oper Res 198:943–957

    Article  MATH  Google Scholar 

  12. Puente J, Gomez A, Fernandez I, Priore P (2009) Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms. Comput Ind Eng 56(4):1232–1242

    Article  Google Scholar 

  13. Burke EK, Curtois T, Qu R, Vanden Berghe G (2010) A scatter search methodology for the nurse rostering problem. J Oper Res Soc 61:1667–1679

    Article  Google Scholar 

  14. Artigues C, Gendreau M, Rousseau LM, Vergnaud A (2009) Solving an integrated employee timetabling and job-shop scheduling problem via hybrid branch-and-bound. Comput Oper Res 36:2330–2340

    Article  MATH  Google Scholar 

  15. De-Matta R, Peters E (2009) Developing work schedules for an inter-city transit system with multiple driver types and fleet types. Eur J Oper Res 192:852–865

    Article  MathSciNet  MATH  Google Scholar 

  16. Weide O, Ryan D, Ehrgott M (2010) An iterative approach to robust and integrated aircraft routing and crew scheduling. Comput Oper Res 37:833–844

    Article  MATH  Google Scholar 

  17. Avramidis AN, Chan W, Gendreau M, L'Ecuyer P, Pisacane O (2010) Optimizing daily agent scheduling in a multiskill call center. Eur J Oper Res 200:822–832

    Article  MATH  Google Scholar 

  18. Ingolfsson A, Campello F, Wu X, Cabral E (2010) Combining integer programming and the randomization method to schedule employees. Eur J Oper Res 202:153–163

    Article  MATH  Google Scholar 

  19. Charnes A, Cooper WW (1961) Management models and industrial applications of linear programming. John Wiley and Sons, New York

    MATH  Google Scholar 

  20. Narasimhan R (1980) Goal programming in a fuzzy environment. Decis Sci 11:325–336

    Article  MathSciNet  Google Scholar 

  21. Hannan EL (1981) Linear programming with multiple fuzzy goals. Fuzzy Set Syst 6(3):235–248

    Article  MathSciNet  MATH  Google Scholar 

  22. Hop NV (2007) Fuzzy stochastic goal programming problems. Eur J Oper Res 176:77–86

    Article  MATH  Google Scholar 

  23. Aköz O, Petrovic D (2007) A fuzzy goal programming method with imprecise goal hierarchy. Eur J Oper Res 181:1427–1433

    Article  MATH  Google Scholar 

  24. Surapati P, Kumar RT (2008) Multiobjective transportation model with fuzzy parameters: Priority based fuzzy goal programming approach. Journal of Transportation Systems Engineering and Information Technology 8(3):40–48

    Article  Google Scholar 

  25. Liang TF (2009) Fuzzy multi-objective project management decisions using two-phase fuzzy goal programming approach. Comput Ind Eng 57:1407–1416

    Article  Google Scholar 

  26. Baky IA (2009) Fuzzy goal programming algorithm for solving decentralized bi-level multi-objective programming problems. Fuzzy Set Syst 160:2701–2713

    Article  MathSciNet  MATH  Google Scholar 

  27. Ozcan U, Toklu B (2009) Multiple-criteria decision-making in two-sided assembly line balancing: a goal programming and a fuzzy goal programming models. Comput Oper Res 36:1955–1965

    Article  Google Scholar 

  28. Arora SR, Gupta R (2009) Interactive fuzzy goal programming approach for bilevel programming problem. Eur J Oper Res 194:368–376

    Article  MathSciNet  MATH  Google Scholar 

  29. Gharehgozli AH, Tavakkoli-Moghaddam R, Zaerpour N (2009) A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates. Robotics and Computer-Integrated Manufacturing 25:853–859

    Article  Google Scholar 

  30. Kara Y, Paksoy T, Chang CT (2009) Binary fuzzy goal programming approach to single model straight and U-shaped assembly line balancing. Eur J Oper Res 195:335–347

    Article  MathSciNet  MATH  Google Scholar 

  31. Huang DK, Chiu HN, Yeh RH, Chang JH (2009) A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem. Comput Ind Eng 56:1–10

    Article  Google Scholar 

  32. Liang TF (2010) Applying fuzzy goal programming to project management decisions with multiple goals in uncertain environments. Expert Syst Appl 37(12):8499–8507

    Article  Google Scholar 

  33. Baky IA (2010) Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach. Appl Math Model 34:2377–2387

    Article  MathSciNet  MATH  Google Scholar 

  34. Lau HC (1996) On the complexity of manpower shift scheduling. Comput Oper Res 23:93–102

    Article  MATH  Google Scholar 

  35. Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceeding of IEEE International Conference on Neural Networks IV, 1942–1948. Perth, Australia: Piscataway, NJ IEEE service center

  36. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In Proceeding of the Sixth International Symposium on Micro Machine and Human Science, 39–43. Nagoya, Japan: Piscataway, NJ IEEE service center

  37. Akjiratikarl C, Yenradee P, Drake PR (2007) PSO-based algorithm for home care worker scheduling in the UK. Comput Ind Eng 53:559–583

    Article  Google Scholar 

  38. Liu B, Wang L, Jin YH (2008) An effective hybrid PSO-based algorithm for flowshop scheduling with limited buffers. Comput Oper Res 35:2791–2806

    Article  MATH  Google Scholar 

  39. Chen RM, Wu CL, Wang CM, Lo ST (2010) Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Syst Appl 37:1899–1910

    Article  Google Scholar 

  40. Lin SY, Horng SJ, Kao TW, Huang DK, Fahn CS, Lai JL, Chen RJ, Kuo IH (2010) An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model. Expert Systems and Applications 37:7825–7830

    Article  Google Scholar 

  41. Moslehi G, Mahnam M (2011) A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int J Prod Econ 129(1):14–22

    Article  Google Scholar 

  42. Shiau DF (2011) A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst Appl 38(1):235–248

    Article  MathSciNet  Google Scholar 

  43. Tavakkoli-Moghaddam R, Azarkish M, Sadeghnejad A (2011) Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS. Int J Adv Manuf Technol 53:733–750

    Article  Google Scholar 

  44. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549

    Article  MathSciNet  MATH  Google Scholar 

  45. Glover F (1989) Tabu search-part I. ORSA J Comput 1:190–206

    Article  MathSciNet  MATH  Google Scholar 

  46. Xu K, Feng Z, Jun K (2010) A tabu-search algorithm for scheduling jobs with controllable processing times on a single machine to meet due-dates. Comput Oper Res 37(11):1924–1938

    Article  MathSciNet  MATH  Google Scholar 

  47. Cesaret B, Oguz C, Salman FS (2012) A tabu search algorithm for order acceptance and scheduling. Comput Oper Res 39(6):1197–1205

    Article  Google Scholar 

  48. Zimmermann HJ (1993) Fuzzy set theory and its applications. 2nd revised edition. Kluwer Academic Publishers, Boston

    Google Scholar 

  49. Bellman RE, Zadeh LA (1970) Decision making in a fuzzy environment. Manag Sci 17:141–164

    Article  MathSciNet  Google Scholar 

  50. Li RJ (1990) Multiple objective decision making in a fuzzy environment. Ph.D. Thesis, Department of Industrial Engineering, Kansas state University, Manhattan, US

  51. Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Set Syst 1:45–55

    Article  MATH  Google Scholar 

  52. Niu Q, Jiao B, Gu X (2008) Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time. Appl Math Comput 205:148–158

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Parisa Shahnazari-Shahrezaei

  2. Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Reza Tavakkoli-Moghaddam

  3. Department of Industrial Engineering, Parand Branch, Islamic Azad University, Parand, Iran

    Hamed Kazemipoor

Authors
  1. Parisa Shahnazari-Shahrezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Tavakkoli-Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamed Kazemipoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parisa Shahnazari-Shahrezaei.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shahnazari-Shahrezaei, P., Tavakkoli-Moghaddam, R. & Kazemipoor, H. Solving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu search. Int J Adv Manuf Technol 64, 1517–1540 (2013). https://doi.org/10.1007/s00170-012-4119-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-012-4119-y

Keywords

Search

Navigation