Solve the issue of project scheduling in a steady state with resource constraints and timing of delivery intervals
Subject Areas :Meysam Jafari Eskandari 1 , rozbeh azizmohammadi 2
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Keywords: Resource constrained project scheduling problems, Robustness, activity-splitting, Earliness–tardiness, Interval deadline, Genetic algorithm, Cuckoo algorithm,
Abstract :
Due to considering the real conditions of project and solving manager’s problems, the primary methods development of scheduling for projects has recently drawn researcher’s attentions so that these methods are looking for finding optimal sequence to realize project goals and to provide its constraints such as dependence, resource constraint (renewable and non-renewable). The importance of these issues has practically and theoretically led researchers to do much efforts on different conditions of issues for project schedule, various methods to solve and or to develop each of them. In this research based on selection of some executive methods for any activity with renewable and non-renewable resource and considering prerequisite relation from kind of start to end and having delivery time for any activity in two time periods that has been provided with regard to delivery time of penalty cost with delay or without delay. The presented model has been solved in small scale by gams software and in small, medium and large scale using meta-heuristic Methods of NSGA ll and cuckoo after coding in software of matlab 2013. The comparison of the answer obtained from the above algorithm indicates the better performance of genetic algorithm in most indexes and cuckoo algorithm has superiority on time index of problem solving.
1. Malcolm, DG, RoseboomJH, ClarkCE, FazarW, (1959), Application of a technique for research and development program evalution, oper Res 1959; 7:646–69.
2. Kelley, J. E. J. the Critical Path Method: Resource Planning and Scheduling. New Jersey : Prentice Hall, 1963.
3. Johnson, T. J. R. An algorithm for the resource constrained project scheduling Problem. Massachusett : Ph.D. Dissertation, Massachusetts Institute of Technology, 1967.
4. Davis, E. W., (1966), Resource allocation in project network models - A Survey, The Journal of Industrial Engineering 17, 177-188..
5. Petrović, R., (1968), Optimization of resource allocation in project planning, Operations Research, 16, 559-568.
6. Balas, E. Project scheduling with resource constraints, In Anonymous Applications of Mathematical Programming (E.M.L. Beale Ed.). s.l. : The English Universities Press London., 1971.
7. Herroelen, W., De Reyck, B, and Demeulemeester, E., (1998), Resource-constrained Project scheduling: a survey of recent developments, Computers & Operations Research, 25, 279-302.
8. Cheng, J, Fowler, J, Kempf, K, Scott. (2015), Multi-mode resource-constrained project scheduling Problems with non-preemptive activity splitting, Computers & Operations Research, http://dx.doi.org/10.1016/j.cor.2014.04.018.
9. Demeulemeester,E,l & Herroelen,W.S, (2002), Project scheduling, KLUWER ACADEMIC PUBLISHERS, NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW,0-306-48142-1.
10. Shadrokh,S, Kianfar,F,(2007), A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty.European Journal of Operational Research 181.
11. Khoshjahan, Y, Najafi, AA, Afshar-Nadjafi,B,(2013),Resource constrained project scheduling problem with discounted earliness–tardiness penalties: Mathematical modeling and solving procedure , Computers & Industrial Engineering, Volume 66, 293-300.
12.bashiri.m(2014),Genetic algorithm in single and multi-objective ,shahed university publishers.
13.Tavakkoli moqaddam r,(2013),meta-heuristic algorithms theory and implementation in matlab,islamic azad university-south tehran branch.
14. Rajabion R,(2011), Cuckoo optimization Algorithm,Applied Soft computing,5508-5518.
15. Afshar-Najafi, B., Shadrokh, Sh., (2010), An Algorithm for the weighted Earliness-Tardiness unconstrained project scheduling program, Journal of Applied Sciences, 1651-1659.
16. Ballestin F, Valls V, Quintanilla S., (2009), scheduling projects with limited number of Preemptions, Computers & Operations Research 36, 2913-2925.
17. Cheng MY,Tran D, (2014), Using a fuzzy clustering chaotic-based differential with serial method to solve resource-constrained project scheduling problems, automation in construction ,volume 37 page 88-97.
18. Ghoddousi, P, Eshtehardian, E, Jooybanpour, S, Javanmardi, A., (2013), Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm, Automation in Construction 30 216–227.
20. Koulinas, G, Kotsikas, L, Anagnostopoulos, K.,(2014), A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem, Department of Production and Management Engineering, Democritus University of Thrace, 12 Vas. Sofias st, 671 00 Xanthi, Greece
21. Moukrim, A,Quilliotb, A,Toussaint, H., (2015), An effective branch-and-price algorithm for the preemptive resource constrained project scheduling problem based on minimal interval order enumeration, European journal of operational research 244,360-368
22. Ranjbar, M, Hosseinabadi, S, Abasian, F., (2013), Minimizing total weighted late work in the resource-constrained project scheduling problem, Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, P.O. Box, 91775-1111, Iran.
23. Ranjbar,M, Khalilzadeh, M, Kianfar, F, Etminani, K ,(2012), An optimal procedure for minimizing total weighted resource tardiness penalty costs in the resource-constrained project scheduling problem , Computers & Industrial Engineering, Volume 62, Issue 1, February ,Pages 264-270
24. Shahsavar M, Niaki, Najafi A.A,(2010), An efficient genetic algorithm to maximize net present value of project payments under inflation and bonus-penalty policy in resource investment problem. Advances in Engineering Software, 41, 1023-1030.
25. Tavana, M, Abtahi, A R, Khalili-Damghani, K., (2014) , A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems, Expert Systems with Applications 41, 1830–1846.
