An Applicable Heuristic for Scheduling Multi-mode Resource Constraint Projects Using PERT Technique in the Presence of Uncertain Duration of Activities

Delgoshaei, Aidin; Ali, Ahad; Parvin, Masih; Ghoreishi, Maryam

doi:10.22034/2018.4.4

An Applicable Heuristic for Scheduling Multi-mode Resource Constraint Projects Using PERT Technique in the Presence of Uncertain Duration of Activities

Document Type : Research Paper

Authors

¹ Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran and Faculty of Engineering, university of Putra Malaysia, Serdang, Kuala Lumpur, Malaysia

² Department of Mechanical Engineering, Lawrence Technological University, Southfield, Michigan, USA

³ Faculty of Engineering, university of Putra Malaysia, Serdang, Kuala Lumpur, Malaysia

⁴ Department of Economics and Business, BSS, Aarhus University, Aarhus, Denmark

10.22034/2018.4.4

Abstract

In project planning process, resource over-allocation is a major shortcoming. The resource over-allocation causes the schedules not to be applicable in practice. Besides, in real projects, it is hard to predict the duration of activities since they may be changed due to lack of resources, delays in delivering resources, improper workers etc. that cause activities not to complete as predicted. Hence, it is important to develop a method that can schedule activities by considering different execution conditions. In this research, we focused on another aspect of solving resource over-allocation problem by considering uncertain activity duration. For this purpose, a mixed integer programming model is developed where the objective function is maximizing net present value of the project while duration of activities are not deterministic. Then a number of examples are solved using a heuristic algorithm. The results showed that the proposed algorithm can effectively solve the case studies with no over-allocated resources. Afterward, the algorithm is solved using the data of constructing a hospital. The results showed that the algorithm can successfully use for real projects.

Keywords

Main Subjects

project management

References

Abbasi, B.; Shadrokh, S.; Arkat, J. (2006), Bi-objective resource-constrained project scheduling with robustness and makespan criteria, Applied Mathematics and Computation. Vol. 180(1), pp. 146-152.

Ballestín, F.; Valls, V.; Quintanilla, S. (2008), Pre-emption in resource-constrained project scheduling, European Journal of Operational Research. Vol. 189(3), pp. 1136-1152.

Buddhakulsomsiri, J.; Kim, D. S. (2006), Properties of multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting, European Journal of Operational Research. Vol. 175(1), pp. 279-295.

Castejón-Limas, M.; Ordieres-Meré, J.; González-Marcos, A.; González-Castro, V. (2011), Effort estimates through project complexity, Annals of Operations research. Vol. 186(1), pp. 395-406.

Chen, J.; Askin, R. G. (2009), Project selection, scheduling and resource allocation with time dependent returns, European Journal of Operational Research. Vol. 193(1), pp. 23-34.

Chtourou, H.; Haouari, M. (2008), A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling, Computers & Industrial Engineering. Vol. 55(1), pp. 183-194.

Damay, J.; Quilliot, A.; Sanlaville, E. (2007), Linear programming based algorithms for preemptive and non-preemptive RCPSP, European Journal of Operational Research. Vol. 182(3), pp. 1012-1022.

Delgoshaei, A.; Ali, A. (2019), Review evolution of cellular manufacturing system’s approaches: Human resource planning method, Journal of Project Management. Vol. 4(1), pp. 31-42.

Delgoshaei, A.; Ali, A.; Ariffin, M. K. A.; Gomes, C. (2016a), A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty, Computers & Industrial Engineering. Vol. 100, pp. 110-132.

Delgoshaei, A.; Ariffin, M.; Baharudin, B.; Leman, Z. (2015), Minimizing makespan of a resource-constrained scheduling problem: A hybrid greedy and genetic algorithms, International Journal of Industrial Engineering Computations. Vol. 6(4), pp. 503-520.

Delgoshaei, A.; Ariffin, M.; Baharudin, B.; Leman, Z. (2016b), A new method for decreasing cell-load variation in dynamic cellular manufacturing systems, International Journal of Industrial Engineering Computations. Vol. 7(1), pp. 83-110.

Delgoshaei, A.; Ariffin, M. K.; Baharudin, B. H. T. B.; Leman, Z. (2014), A Backward Approach for Maximizing Net Present Value of Multi-mode Pre-emptive Resource-Constrained Project Scheduling Problem with Discounted Cash Flows Using Simulated Annealing Algorithm, International Journal of Industrial Engineering and Management. Vol. 5(3), pp. 151-158.

Delgoshaei, A.; Ariffin, M. K. A.; Ali, A. (2017), A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS, International Journal of Production Research. Vol. 55(4), pp. 997-1039.

Delgoshaei, A.; Ariffin, M. K. M.; Baharudin, B. H. T. (2016c), Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach, Journal of Industrial Engineering and Management. Vol. 9(3), pp. 732-785.

Delgoshaei, A.; Gomes, C. (2016), A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost, Applied Soft Computing. Vol. 49, pp. 27-55.

Delgoshaei, A.; Rabczuk, T.; Ali, A.; Ariffin, M. K. A. (2016d), An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources, Annals of Operations Research. Vol.259(1-2), pp. 1-33.

Hartmann, S.; Briskorn, D. (2010), A survey of variants and extensions of the resource-constrained project scheduling problem, European Journal of Operational Research. Vol. 207(1), pp. 1-14.

Hendricks, M. H.; Voeten, B.; Kroep, L. H. (2002), Human resource allocation in a multiproject research and development environment, Managing Multiple Projects. Planning, Scheduling, and Allocating Resources for Competitive Advantage. Vol. 17(3), pp.181-188.

Jarboui, B.; Damak, N.; Siarry, P.; Rebai, A. (2008), A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems, Applied Mathematics and Computation. Vol. 195(1), pp. 299-308.

Ke, H.; Liu, B. (2010), Fuzzy project scheduling problem and its hybrid intelligent algorithm, Applied Mathematical Modelling. Vol. 34(2), pp. 301-308.

Kim, K.; Yun, Y.; Yoon, J.; Gen, M.; Yamazaki, G. (2005), Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling, Computers in industry. Vol. 56(2), pp. 143-160.

Kolisch, R.; Drexl, A. (1997), Local search for nonpreemptive multi-mode resource-constrained project scheduling, IIE transactions. Vol. 29(11), pp. 987-999.

Kreter, S.; Rieck, J.; Zimmermann, J. (2016), Models and solution procedures for the resource-constrained project scheduling problem with general temporal constraints and calendars, European Journal of Operational Research. Vol. 251(2), pp. 387-403.

Laslo, Z. (2010), Project portfolio management: An integrated method for resource planning and scheduling to minimize planning/scheduling-dependent expenses, International Journal of Project Management. Vol. 28(6), pp. 609-618.

Lin, C.-M.; Gen, M. (2008), Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm, Expert Systems with Applications. Vol. 34(4), pp. 2480-2490.

Mika, M.; Waligóra, G.; Węglarz, J. (2005), Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models, European Journal of Operational Research. Vol. 164(3), pp. 639-668.

Naber, A.; Kolisch, R. (2014), MIP models for resource-constrained project scheduling with flexible resource profiles, European Journal of Operational Research. Vol. 239(2), pp. 335-348.

Papke-Shields, K. E.; Boyer-Wright, K. M. (2017), Strategic planning characteristics applied to project management, International Journal of Project Management. Vol. 35(2), pp. 169-179.

Pérez, E.; Posada, M.; Lorenzana, A. (2016), Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms, Soft Computing. Vol. 20(5), pp. 1879-1896.

Rabbani, M.; Ravanbakhsh, M.; Farrokhi-Asl, H.; Taheri, M. (2017), Using metaheuristic algorithms for solving a hub location problem: application in passive optical network planning, International Journal of Supply and Operations Management. Vol. 4(1), pp. 15-32.

Seifi, M.; Tavakkoli-Moghaddam, R. (2008), A new bi-objective model for a multi-mode resource-constrained project scheduling problem with discounted cash flows and four payment models, Int. J. of Engineering, Transaction A: Basic. Vol. 21(4), pp. 347-360.

Sharon, A.; Dori, D. (2015), A Project–product model–based approach to planning work breakdown structures of complex system projects, IEEE Systems Journal. Vol. 9(2), pp. 366-376.

Vaez, P. (2017), A New Mathematical Model for Simultaneous Lot-sizing and Production Scheduling Problems Considering Earliness/Tardiness Penalties and Setup Costs, International Journal of Supply and Operations Management. Vol. 4(2), pp. 167-179.

Van de Vonder, S.; Demeulemeester, E.; Herroelen, W.; Leus, R. (2005), The use of buffers in project management: The trade-off between stability and makespan, International Journal of Production Economics. Vol. 97(2), pp. 227-240.

Van de Vonder, S.; Demeulemeester, E.; Herroelen, W.; Leus, R. (2006), The trade-off between stability and makespan in resource-constrained project scheduling, International Journal of Production Research. Vol. 44(2), pp. 215-236.

Van Peteghem, V.; Vanhoucke, M. (2010), A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem, European Journal of Operational Research. Vol. 201(2), pp. 409-418.

Vanhoucke, M.; Debels, D. (2008), The impact of various activity assumptions on the lead time and resource utilization of resource-constrained projects, Computers & Industrial Engineering. Vol. 54(1), pp. 140-154.

Ward, S.; Chapman, C. (2003), Transforming project risk management into project uncertainty management, International Journal of Project Management. Vol. 21(2), pp. 97-105.

Węglarz, J.; Józefowska, J.; Mika, M.; Waligóra, G. (2011), Project scheduling with finite or infinite number of activity processing modes–A survey, European Journal of Operational Research. Vol. 208(3), pp. 177-205.

Yan, L.; Jinsong, B.; Xiaofeng, H.; Ye, J. (2009), A heuristic project scheduling approach for quick response to maritime disaster rescue, International Journal of Project Management. Vol. 27(6), pp. 620-628.

Yu; Wang, S.; Wen, F.; Lai, K. K. (2012), Genetic algorithm-based multi-criteria project portfolio selection, Annals of Operations Research. Vol. 197(1), pp. 71-86.

Zhou, M.; Askin, R. G. (1998), Formation of general GT cells: an operation-based approach, Computers & industrial engineering. Vol. 34(1), pp. 147-157.

ZIAEE, M. (2017), Modeling and solving the distributed and flexible job shop scheduling problem with WIPs supply planning and bounded processing times. Vol.4(1) , pp. 78-89.