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Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms

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Abstract

Repetitive project scheduling is a frequently encountered and challenging task in project planning. Researchers have developed numerous methods for the scheduling and planning of repetitive construction projects. However, almost all current repetitive scheduling methods are based on identical production units or they neglect the priorities of activities. This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). In FABC, the fuzzy c-means clustering technique applies several multi-parent crossover operators to utilize population information efficiently and to improve convergence efficiency. The scheduling subsystem considers the following: (1) the logical relationships among activities throughout the project; (2) the assignment of multiple resources; and (3) the priorities of activities in groups to calculate project duration. Two numerical case studies are analyzed to demonstrate the use of the FABC-NRP model and its ability to optimize the scheduling of non-unit repetitive construction projects. Experimental results indicate that the proposed method yields the shortest project duration on average and deviation of optimal solution among benchmark algorithms considered herein and those considered previously. The outcomes will help project managers to prepare better schedules of repetitive projects.

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Abbreviations

NP :

Population size

D :

Number of decision variables

G max :

Maximum number of generations

LB :

Lower bounds

UB :

Upper bounds

limit :

Predetermined number of trials for scout

S n :

Crew option

P n :

Priority value

US n :

Total shift options for each activity

S1:

Scheduling system

p i :

Probability for solution on onlooker bee phase

FT ij :

Finishing time of activity j in group i

ɸ i,j :

A random number in range [− 1; 1]

fit i :

Fitness value of the ith solution

m :

Clustering period

Mod :

Finds the remainder after division

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Funding

This research is funded by Ho Chi Minh City University of Technology - VNU-HCM under Grant number T-KTXD-2019-15.

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Authors and Affiliations

  1. Department of Construction Engineering and Management, Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh, City (VNU-HCM), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Viet Nam

    Duc-Hoc Tran & Duc-Long Luong

  2. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei, Taiwan

    Jui-Sheng Chou

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  1. Duc-Hoc Tran
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Correspondence to Duc-Hoc Tran.

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Tran, DH., Chou, JS. & Luong, DL. Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms. Oper Res Int J 22, 77–103 (2022). https://doi.org/10.1007/s12351-019-00544-7

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  • DOI: https://doi.org/10.1007/s12351-019-00544-7

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