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Soft Computing

, Volume 20, Issue 4, pp 1565–1580 | Cite as

A model for resource-constrained project scheduling using adaptive PSO

  • Neetesh Kumar
  • Deo Prakash Vidyarthi
Methodologies and Application

Abstract

Resource-constrained project scheduling problem (RCPSP) is an important, but computationally hard problem. Particle swarm optimization (PSO) is a well-known and highly used meta-heuristics to solve such problems. In this work, a simple, effective and improved version of PSO i.e. adaptive-PSO (A-PSO) is proposed to solve the RCPSP. Conventional canonical PSO is improved at two points; during the particle’s position and velocity updation, due to dependent activities in RCPSP, a high possibility arises for the particle to become invalid. To overcome this, an important operator named valid particle generator (VPG) is proposed and embedded into the PSO which converts an invalid particle into a valid particle effectively with the knowledge of the in-degree and out-degree of the activities depicted by the directed acyclic graph. Second, inertia weight \((\omega )\) that plays a significant role in the quick convergence of the PSO is adaptively tuned by considering the effects of fitness value, previous value of \(\omega \) and iteration counter. Performance of the model is evaluated on the standard benchmark data of the RCPSP problem. Results show the effectiveness of the proposed model in comparison to other existing state of the art model that uses heuristics/meta-heuristics. The proposed model has the potential to be applied to other similar problems.

Keywords

Particle swarm optimization NP-hard Heuristics  Resource-constrained project scheduling problem Makespan 

References

  1. Akbari R, Zeighami V, Ziarati K (2011) Artificial bee colony for resource constrained project scheduling problem. Int J Ind Eng Comput 2:45–60Google Scholar
  2. Al Badawi A, Shatnawi A (2013) Static scheduling of directed acyclic data flow graphs onto multicores using particle swarm optimization. Comput Oper Res 40:2322–2328Google Scholar
  3. Alba E, Chicano JF (2007) Software project management with GAs. Inf Sci 177:2380–2401CrossRefGoogle Scholar
  4. Alcaraz J, Maroto C, Ruiz R (2003) Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. J Oper Res Soc 54(6):614–626CrossRefzbMATHGoogle Scholar
  5. Artigues C, Michelon P, Reusser S (2003) Insertion techniques for static and dynamic resource-constrained project scheduling. Eur J Oper Res 149:249–267MathSciNetCrossRefzbMATHGoogle Scholar
  6. Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3:180–184Google Scholar
  7. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA J Comput (Springer) 6(2):154–180CrossRefzbMATHGoogle Scholar
  8. Birattari M, Stützle T, Paquete L (2010) Varrentrapp K A racing algorithm for configuring metaheuristics. In: Langdon WB et al. (eds) GECCO 2002: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann, San Francisco, vol 2, pp 11–18Google Scholar
  9. Blazewicz J, Lenstra JK (1983) Scheduling subject to resource constraints: classification and complexity. Discret Appl Math 5:11–24MathSciNetCrossRefzbMATHGoogle Scholar
  10. Bouleimen K, Lecocq H (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149(2):268–281MathSciNetCrossRefzbMATHGoogle Scholar
  11. Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In: IEEE swarm intelligence symposium, SIS, vol 2007, pp 120–127Google Scholar
  12. Brucker P, Knust S, Schoo A (1998) A branch and bound algorithm for the resource- on strained project scheduling problem1. Eur J Oper Res 107(2):272–288CrossRefzbMATHGoogle Scholar
  13. Chen C-T, Huang S-F (2007) Applying fuzzy method for measuring criticality in project network. Inf Sci 177:2448–2458CrossRefzbMATHGoogle Scholar
  14. 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(3):1899–1910CrossRefGoogle Scholar
  15. Chen W, Shi Y, Tenga H, Lan X (2010) An efficient hybrid algorithm for resource-constrained project scheduling. Inf Sci 180:1031–1039CrossRefGoogle Scholar
  16. Christofides N, Alvarez-Valdes R, Tamarit JM (1987) Project scheduling with resource constraints: a branch and bound approach. Eur J Oper Res 29:262–273MathSciNetCrossRefzbMATHGoogle Scholar
  17. de Oca MAM, Aydın D, Stü tzle T (2011) An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms. Soft Computing 15:2233–2255CrossRefGoogle Scholar
  18. Demeulemeester E, Herroelen W (1995) New benchmark results for the resource-constrained project scheduling problem. In: Proceedings of the INFORMS Singapore international meeting, SingaporeGoogle Scholar
  19. Dorndorf U, Pesch E, Huy TP (2000) A time-oriented branch-and-bound algorithm for resource constrained project scheduling with generalized precedence constraints. Manag Sci 46:1365–1384CrossRefzbMATHGoogle Scholar
  20. Hartmann S (1998) A competitive genetic algorithm for resource-constrained project scheduling. Navig Res Logist 45(7):733–750MathSciNetCrossRefzbMATHGoogle Scholar
  21. Hartmann S (2002) A self-adapting genetic algorithm for project scheduling under resource constraints. Navig Res Logist 49:433–448MathSciNetCrossRefzbMATHGoogle Scholar
  22. Heilmann RA (2003) Branch-and-bound procedure for the multimode resource-constrained project scheduling problem with minimum and maximum time lags. Eur J Oper Res 144(2):348–365MathSciNetCrossRefzbMATHGoogle Scholar
  23. Herbots J, Herroelen W, Leus R (2004) Experimental investigation of the applic- ability of ant colony optimization algorithms for project scheduling. Research Report, KU, LeuvenGoogle Scholar
  24. Jarboui B, Damak N, Siarry P, Rebai A (2008) A combinatorial particle swarm optimization for solving multi-mode resource constrained project scheduling. Appl Math Comput 105(1):299–308MathSciNetCrossRefzbMATHGoogle Scholar
  25. Jia Qiong, Seo Yoonho (2013) Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutation-based ABC computation. Comput Oper Res 40:2037–2050MathSciNetCrossRefGoogle Scholar
  26. Jia Q, Seo Y (2013) An improved particle swarm optimization for the resource-constrained project scheduling problem. Int J Adv Manuf Technol 67:2627–2638CrossRefGoogle Scholar
  27. Jones KO (2005) Comparison of genetic algorithm and particle swarm optimization. In: Proceedings in International Conference on Computer Systems and Technologies, pp 1–6Google Scholar
  28. Kaplan L (1996) Resource-constrained project scheduling with preemption of jobs. Dissertation, University of MichiganGoogle Scholar
  29. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence, 1st edn. Morgan Kaufmann, San FranciscoGoogle Scholar
  30. Klein R (2000) Scheduling of resource-constrained projects. Kluwer, BostonCrossRefzbMATHGoogle Scholar
  31. Kolisch R, Sprecher A (1997) PSPLIB—a project scheduling problem library: OR Software—ORSEP Operations Research Software Exchange Program. Eur J Oper Res 96:205–216CrossRefzbMATHGoogle Scholar
  32. Kolisch R, Hartmann S (2006) Experimental investigation of heuristics for resource-constrained project scheduling: an update. Eur J Oper Res 174(1):23–37CrossRefzbMATHGoogle Scholar
  33. Kolisch R, Hartmann S (1999) Heuristic Algorithms for the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis-Project Scheduling International Series in Operations Research & Management Science, vol 14, pp 147–178Google Scholar
  34. Kone O, Artigues C, Lopez P, Mongeau M (2011) Event-based MILP models for resource-constrained project scheduling problems. Comput Oper Res 38(1):3–13MathSciNetCrossRefzbMATHGoogle Scholar
  35. Koulinas G, Kotsikas L, Anagnostopoulos K (2014) A Particle Swarm Optimization based Hyper-heuristic Algorithm for the Classic Resource Constrained Project Scheduling Problem, Information Sciences. doi: 10.1016/j.ins.2014.02.155
  36. Lu M, Lam HC, Dai F (2008) Resource-constrained critical path analysis based on discrete event simulation and particle swarm optimization. Autom Constr 17(6):670–681CrossRefGoogle Scholar
  37. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8:204–210Google Scholar
  38. Merkle D, Middendorf M, Schmeck H (2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evolut Comput 6(4):333–346CrossRefzbMATHGoogle Scholar
  39. Mingozzi A, Maniezzo V, Ricciardelli S, Bianco L (1998) an exact algorithm for the resource-constrained project scheduling problem based on a new mathematical formulation. Manag Sci 44(5):714–729CrossRefzbMATHGoogle Scholar
  40. Nonobe K, Ibaraki T (2002) Formulation and tabu search algorithm for the resource constrained project scheduling problem. In: Ribeiro CC, Hansen P (eds) Essays and surveys in meta-heuristics. Kluwer Academic Publishers, Dordrecht, pp 557–588CrossRefGoogle Scholar
  41. Palpant M, Artigues C, Michelon P (2004) LSSPER: solving the resource-constrained project scheduling problem with large neighborhood search. Ann Oper Res 31:237–257MathSciNetCrossRefzbMATHGoogle Scholar
  42. Pritsker AAB, Watters LJ (1969) Wolfe PM Multi-project scheduling with limited resources: a zero-one programming approach. Manag Sci 16(1):93–108CrossRefGoogle Scholar
  43. Schirmer A (2000) Case-based reasoning and improved adaptive search for project scheduling. Navig Res Logist 47:201–222MathSciNetCrossRefzbMATHGoogle Scholar
  44. Sevkli Z, Sevilgen FE (2004) Keleş, Particle swarm optimization for the orienteering problem. Algorithmica 35(1):30–39Google Scholar
  45. Shi YJ, Qu FZ, Chen W, Li B (2010) An artificial bee colony with random key for resource-constrained project scheduling, In: International conference on life system modeling and simulation and international conference on intelligent computing for sustainable energy and environment (LSMS/ICSEE), WuXi, China, pp 148–157Google Scholar
  46. Singh Navjot, Arya Rinki (2014) A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recognit 47:1731–1739CrossRefGoogle Scholar
  47. Tasgetiren MF, Sevkli M, Liang YC (2004) Gencyilmaz G. Particle swarm optimization algorithm for single machine total weighted tardiness problem. In: IEEE Congress on Evolutionary Computation vol 2, pp 1412–1419Google Scholar
  48. Tseng L-Y, Chen S-C (2006) A hybrid metaheuristic for the resource-constrained project scheduling problem. Eur J Oper Res 175:707–721Google Scholar
  49. Valls V, Quintanilla S, Ballestín F (2003) Resource-constrained project scheduling: a critical activity reordering heuristic. Eur J Oper Res 149:282–301MathSciNetCrossRefzbMATHGoogle Scholar
  50. Valls V, Ballestín F, Quintanilla S (2004) A population-based approach to the resource-constrained project scheduling problem. Ann Oper Res 131:305–324MathSciNetCrossRefzbMATHGoogle Scholar
  51. Valls V, Ballestin F, Quintanilla MS (2005) Justification and RCPSP: a technique that pays. Eur J Oper Res 165(2):375–386MathSciNetCrossRefzbMATHGoogle Scholar
  52. Zhang H, Li XD, Li H, Huang FL (2005) Particle swarm optimization-based schemes for resource-constrained project scheduling. Autom Constr 14(3):393–404CrossRefGoogle Scholar
  53. Zhang H, Li H, Tam CM (2006) Particle swarm optimization for resource-constrained project scheduling. Int J Proj Manag 24:83–92Google Scholar
  54. Ziaratia K, Akbaria R, Zeighami V (2011) On the performance of bee algorithms for resource-constrained project scheduling problem. Appl Soft Comput 11(4):3720–3733CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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