SciELO - Scientific Electronic Library Online

 
vol.21 issue1Project activity analysis without the network modelA model for quality management in a supply chain with a retailer and a manufacturer author indexsubject indexarticles search
Home Pagealphabetic serial listing  

South African Journal of Industrial Engineering

On-line version ISSN 2224-7890
Print version ISSN 1012-277X

S. Afr. J. Ind. Eng. vol.21 n.1 Pretoria  2010

 

GENERAL ARTICLES

 

The discrete time, cost and quality trade-off problem in project scheduling: An efficient solution method based on CellDE algorithm

 

 

Gh. AssadipourI; H. IranmaneshII

IDepartment of Industrial Engineering, University of Tehran, Iran, hiranmanesh@ut.ac.ir
IIDepartment of Industrial Engineering, University of Tehran, Iran, ghazal.assadipour@gmail.com

 

 


ABSTRACT

The trade-off between time, cost, and quality is one of the important problems of project management. This problem assumes that all project activities can be executed in different modes of cost, time, and quality. Thus a manager should select each activity's mode such that the project can meet the deadline with the minimum possible cost and the maximum achievable quality. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimisation method. The proposed algorithm provides project managers with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Three metrics are employed for evaluating the performance of the algorithm, appraising the diversity and convergence of the achieved Pareto fronts. Finally a comparison is made between CellDE and another meta-heuristic available in the literature. The results show the superiority of CellDE.


OPSOMMING

'n Balans tussen tyd, koste en gehalte is een van die belangrike probleme van projekbestuur. Die vraagstuk maak gewoonlik die aanname dat alle projekaktiwiteite uitgevoer kan word op uiteenlopende wyses wat verband hou met koste, tyd en gehalte. 'n Projekbestuurder selekteer gewoonlik die uitvoeringsmetodes sodanig per aktiwiteit dat gehoor gegegee word aan minimum koste en maksimum gehalte teen die voorwaarde van voltooiingsdatum wat bereik moet word.
Aangesien die beskrewe problem NP-hard is, word dit behandel ten opsigte van konflikterende doelwitte met 'n multidoelwit metaheuristiese metode (CellDE). Die metode is 'n hibride-sellulêre genetiese algoritme. Die algoritme lewer aan die besluitvormer 'n versameling van ongedomineerde of Pareto-optimale oplossings vir voorkeurgedrewe besluitvorming. Uiteenlopende probleme word opgelos deur die algoritme. Drie verskillende waardebepalings word toegepas op die gedrag van die algoritme. Die resultate bevestig die voortreflikheid van CellDE.


 

 

“Full text available only in PDF format”

 

 

REFERENCES

[1] De, P., Dunne, E.J., Ghosh, J.B.& Wells, C.E. 1995. The discrete time-cost tradeoff problem revisited, European Journal of Operational Research, 81, pp 225-238.         [ Links ]

[2] Rahimi, M. & Iranmanesh, H. 2008. Multi objective particle swarm optimization for a discrete time, cost and quality trade-off problem, World Applied Sciences Journal, 4(2), pp 270-276.         [ Links ]

[3] Kelly, J.E. & Walker, M.R. 1959. Critical path planning and scheduling: An introduction, Mauchly Associates, Ambler, PA.         [ Links ]

[4] Siemens, N. 1971. A simple CPM time/cost trade-off algorithm, Management Science, 17, pp B-354-B-363.         [ Links ]

[5] Goyal, S.K. 1975. A note on the paper: A simple CPM time/cost trade-off algorithm, Management Science, 21, pp 718-722.         [ Links ]

[6] De, P., Dunne, E.J., Ghosh, J.B. & Wells, C. 1997. Complexity of the discrete time-cost tradeoff problem for project networks, Operations Research, 45, pp 302-306.         [ Links ]

[7] Babu, A.J.G. & Suresh, N. 1996. Project management with time, cost and quality considerations, European Journal of Operational, 88, pp 320-327.         [ Links ]

[8] Khang, D.B. & Myint, Y.M. 1999. Time, cost and quality trade-off in project management: A case study, International Journal of Project Management, 17(4), pp 249-256.         [ Links ]

[9] Tareghian, H.R. & Taheri, H. 2006. On the discrete time, cost and quality trade-off problem, Applied Mathematics and Computation, 181, pp 1305-1312.         [ Links ]

[10] Tareghian, H.R. & Taheri, H. 2007. A solution procedure for the discrete time, cost and quality tradeoff problem using electromagnetic scatter, Applied Mathematics and Computation, 190, pp 1136-1145.         [ Links ]

[11] Iranmanesh, H., Skandari, M.R. & Allahverdiloo, M. 2008. Finding Pareto optimal front for the multi-mode time cost and quality trade-off in project scheduling, International Journal of Computer, Information, and Systems Science, and Engineering 2, pp 118-122.         [ Links ]

[12] Eskandari, H. & Geiger, C.D. 2008. A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems, Journal of Heuristics, 14(3), pp 203-241.         [ Links ]

[13] Fonseca, C.M. & Fleming, P.J. 1995. An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation, 1(1), pp 1-16.         [ Links ]

[14] Durillo, J.J., Nebro, A.J., Luna, F. & Alba, E. 2008. Solving three-objective optimization problems using a new hybrid cellular genetic algorithm, PPSN 2008, pp 661-670.         [ Links ]

[15] Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B. & Alba, E. 2007. Design issues in a multiobjective cellular genetic algorithm, Evolutionary Multi-Criterion Optimization, LNCS 4403, pp 126-140.         [ Links ]

[16] Storn, R. & Price, K. 1995. Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, Berkeley, CA.         [ Links ]

[17] Neri, F. & Tirronen, V. 2008. On memetic differential evolution frameworks: A study of advantages and limitations in hybridization, Proceedings of the IEEE World Congress on Computational Intelligence, pp 2135-2142.         [ Links ]

[18] Zielinski, K., Weitkemper, P., Laur, R. & Kammeyer, K.D. 2006. Parameter study for differential evolution using a power allocation problem including interference cancellation, Proceedings of the IEEE Congress on Evolutionary Computation, pp 1857-1864.         [ Links ]

[19] Zitzler, E. & Thiele, L. 1999. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, 3(4), pp 257-271.         [ Links ]

[20] Deb, K., Pratap, A., Agrawal, S. & Meyarivan, T. 2002. A fast and elitist multi-objective genetic algorithm: NSGA II, IEEE Transactions on Evolutionary Computation, 6(2), pp 182-197.         [ Links ]

 

 

* Corresponding author

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License