Skip to main content
SpringerLink
Log in

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

  • Original paper
  • Published:
Operational Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

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

References

  • Aderhold A, Diwold K, Scheidler A, Middendorf M (2010) Artificial bee colony optimization: a new selection scheme and its performance. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 283–294

    Google Scholar 

  • Agrama FA (2014) Multi-objective genetic optimization for scheduling a multi-storey building. Autom Constr 44:119–128

    Google Scholar 

  • Al Sarraj ZM (1990) Formal development of line-of-balance technique. J Constr Eng Manag 116(4):689–704

    Google Scholar 

  • Andrade LACG, Cunha CB (2015) An ABC heuristic for optimizing moveable ambulance station location and vehicle repositioning for the city of São Paulo. Int Trans Oper Res 22(3):473–501

    Google Scholar 

  • Anuar S, Selamat A, Sallehuddin R (2016) A modified scout bee for artificial bee colony algorithm and its performance on optimization problems. J King Saud Univ Comput Inf Sci 28(4):395–406

    Google Scholar 

  • Bakry I, Moselhi O, Zayed T (2014) Optimized acceleration of repetitive construction projects. Autom Constr 39:145–151

    Google Scholar 

  • Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inf Technol 47(2):434–459

    Google Scholar 

  • Cai Z, Gong W, Ling CX, Zhang H (2011) A clustering-based differential evolution for global optimization☆. Appl Soft Comput 11(1):1363–1379

    Google Scholar 

  • Chaurasia SN, Sundar S, Singh A (2017) Hybrid metaheuristic approaches for the single machine total stepwise tardiness problem with release dates. Oper Res Int J 17(1):275–295

    Google Scholar 

  • Cheng M-Y, Tran D-H, Yu-Wei W (2014) Using a fuzzy clustering chaotic-based differential evolution with serial method to solve resource-constrained project scheduling problems. Autom Constr 37:88–97

    Google Scholar 

  • Chrzanowski EN, Johnston DW (1986) Application of linear scheduling. J Constr Eng Manag 112(4):476–491

    Google Scholar 

  • Clerc M (2006) Particle swarm optimization. ISTE Ltd, London

    Google Scholar 

  • Cui L, Li G, Zhu Z, Lin Q, Wen Z, Lu N, Wong K-C, Chen J (2017) A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Inf Sci 414(Supplement C):53–67

    Google Scholar 

  • David A, Albulak MZ (1986) Line-of-balance scheduling in pavement construction. J Constr Eng Manag 112(3):411–424

    Google Scholar 

  • Deb K (2005) A population-based algorithm-generator for real-parameter optimization. Soft Comput 9(4):236–253

    Google Scholar 

  • El-Rayes K, Moselhi O (1998) Resource-driven scheduling of repetitive activities. Constr Manag Econ 16(4):433–446

    Google Scholar 

  • Fan S-L, Tserng HP (2006) Object-oriented scheduling for repetitive projects with soft logics. J Constr Eng Manag 132(1):35–48

    Google Scholar 

  • Fan S-L, Sun K-S, Wang Y-R (2012) GA optimization model for repetitive projects with soft logic. Autom Constr 21(Supplement C):253–261

    Google Scholar 

  • Harris RB, Ioannou PG (1998) Scheduling projects with repeating activities. J Constr Eng Manag 124(4):269–278

    Google Scholar 

  • Haupt RL, Ellen Haupt S (2004) Practical genetic algorithms. Wiley, Hoboken

    Google Scholar 

  • Hsie M, Ching-Jung Chang I, Yang T, Huang C-Y (2009) Resource-constrained scheduling for continuous repetitive projects with time-based production units. Autom Constr 18(7):942–949

    Google Scholar 

  • Huang R-y, Sun K-S (2005) System development for non-unit based repetitive project scheduling. Autom Constr 14(5):650–665

    Google Scholar 

  • Huang R-y, Sun K-S (2006) Non-unit-based planning and scheduling of repetitive construction projects. J Constr Eng Manag 132(6):585–597

    Google Scholar 

  • Huang R-y, Sun K-S (2009) A GA optimization model for workgroup-based repetitive scheduling (WoRSM). Adv Eng Softw 40(3):212–228

    Google Scholar 

  • Huang Y, Zou X, Zhang L (2016) Genetic algorithm-based method for the deadline problem in repetitive construction projects considering soft logic. J Manag Eng 32(4):04016002

    Google Scholar 

  • Ioannou PG, Yang IT (2016) Repetitive scheduling method: requirements, modeling, and implementation. J Constr Eng Manag 142(5):04016002

    Google Scholar 

  • Ipsilandis PG (2006) Multiobjective optimization in linear repetitive project scheduling. Oper Res Int J 6(3):255

    Google Scholar 

  • Kang F, Li J (2016) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng 30(3):04015040

    Google Scholar 

  • Kang F, Li J, Ma Z (2011) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181(16):3508–3531

    Google Scholar 

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Google Scholar 

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Google Scholar 

  • Khalied H, Khaled E-R (2006) Optimal planning and scheduling for repetitive construction projects. J Manag Eng 22(1):11–19

    Google Scholar 

  • Kiran MS, Gündüz M (2012) A novel artificial bee colony-based algorithm for solving the numerical optimization problems. Int J Innov Comput Inf Control 8(9):6107–6122

    Google Scholar 

  • Kıran MS, Gündüz M (2013) A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems. Appl Soft Comput 13(4):2188–2203

    Google Scholar 

  • Kris G Mattila, Amy P (2003) Comparison of linear scheduling model and repetitive scheduling method. J Constr Eng Manag 129(1):56–64

    Google Scholar 

  • Kwedlo W (2011) A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn Lett 32(12):1613–1621

    Google Scholar 

  • Lee W-P, Cai W-T (2011) A novel artificial bee colony algorithm with diversity strategy. In: Seventh international conference on natural computation (ICNC): IEEE, pp 1441–44

  • Li X, Yin M (2011) Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters. Int J Antennas Propag 2011:12

    Google Scholar 

  • Liu S-S, Wang C-J (2012) Optimizing linear project scheduling with multi-skilled crews. Autom Constr 24:16–23

    Google Scholar 

  • Long LD, Ohsato A (2009) A genetic algorithm-based method for scheduling repetitive construction projects. Autom Constr 18(4):499–511

    Google Scholar 

  • Luo J, Wang Q, Xiao X (2013) A modified artificial bee colony algorithm based on converge-onlookers approach for global optimization. Appl Math Comput 219(20):10253–10262

    Google Scholar 

  • Maravas A, Pantouvakis J-P (2011) Fuzzy repetitive scheduling method for projects with repeating activities. J Constr Eng Manag 137(7):561–564

    Google Scholar 

  • Mathew J, Paul B, Dileeplal J, Tinjumol M (2016) Multi objective optimization for scheduling repetitive projects using GA. Procedia Technol 25:1072–1079

    Google Scholar 

  • Price KV, Storn RM, Lampinen JA (2005) Differential evolution a practical approach to global optimization. Springer, Berlin

    Google Scholar 

  • Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9(2):625–631

    Google Scholar 

  • Srisuwanrat C, Ioannou PG (2007) Optimal scheduling of probabilistic repetitive projects using completed unit and genetic algorithms. In: 2007 winter simulation conference, pp 2151–58

  • Suhail SA, Neale RH (1994) CPM/LOB: new methodology to integrate CPM and line of balance. J Constr Eng Manag 120(3):667–684

    Google Scholar 

  • Sundar S, Singh A (2012) A hybrid heuristic for the set covering problem. Oper Res Int J 12(3):345–365

    Google Scholar 

  • Thabet WY, Beliveau YJ (1994) HVLS: horizontal and vertical logic scheduling for multistory projects. J Constr Eng Manag 120(4):875–892

    Google Scholar 

  • Tran D-H, Cheng M-Y, Cao M-T (2016) Solving resource-constrained project scheduling problems using hybrid artificial bee colony with differential evolution. J Comput Civ Eng 30(4):04015065

    Google Scholar 

  • Tran D-H, Chou J-S, Luong D-L (2019) Multi-objective symbiotic organisms optimization for making time-cost tradeoffs in repetitive project scheduling problem. J Civ Eng Manag 25(4):322–339

    Google Scholar 

  • Vanhoucke M (2006) Work continuity constraints in project scheduling. J Constr Eng Manag 132(1):14–25

    Google Scholar 

  • Verma BK, Kumar D (2013) A review on artificial bee colony algorithm. Int J Eng Technol 2(3):12

    Google Scholar 

  • Wang Y-J, Zhang J-S, Zhang G-Y (2007) A dynamic clustering based differential evolution algorithm for global optimization. Eur J Oper Res 183(1):56–73

    Google Scholar 

  • Whiteman WE, Irvvig HG (1988) Disturbance scheduling technique for managing renovation work. J Constr Eng Manag 114(2):191–213

    Google Scholar 

  • Xiang W-L, An M-Q (2013) An efficient and robust artificial bee colony algorithm for numerical optimization. Comput Oper Res 40(5):1256–1265

    Google Scholar 

  • Zhang L-H (2015) Repetitive project scheduling: theory and methods. Elsevier, Amsterdam

    Google Scholar 

  • Zou W, Zhu Y, Chen H, Sui X (2010) A clustering approach using cooperative artificial bee colony algorithm. Discrete Dyn Nat Soc 2010:16

    Google Scholar 

Download references

Funding

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

Author information

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

Authors
  1. Duc-Hoc Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jui-Sheng Chou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Duc-Long Luong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duc-Hoc Tran.

Ethics declarations

Conflict of interest

We declare that we have no any competing financial, professional, or personal interests from other parties.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation