Time-Cost-Quality Optimization using an Invasive Weed Algorithm with Activity Preemption in Construction Projects

Document Type : Research Article

Authors

1 Assistant professor, Department of civil engineering, Higher Education Institute of Pardisan

2 PhD student, Industrial Engineering, Mazandaran University of Science and Technology

3 MSc in construction engineering and management, Tabari university of babol

Abstract

In the last decade, various methods are created to optimize time, cost, and quality. Solving such a problem on large scale is too hard using traditional methods in logical time. Recently, researchers are focused on a meta-heuristic algorithm to solve time-cost-quality tradeoff problems. How to make a balance among time, cost, and quality parameters is so critical in construction project management. In this study, an invasive weed optimization algorithm is applied to solve the problem. In the proposed model, activity time is changed so that maximum usage of resources is obtained. In other words, it is possible to perform some activity simultaneously if their duration is increased which causes to decrease time, cost and increase project quality. Obtained results indicate the advantages of the proposed algorithm. Finally, to validate the proposed model a small size instance problem is created and solved by GAMS software optimally and compared with proposed algorithm results in MATLAB software. Results show that both Pareto solution obtained is almost identical, then it validates the algorithms for large scale problem.

Keywords

Main Subjects


[1]. El-Rayes K., Kandil A., (2005), “Time-cost-quality trade off analysis for highway construction”, Journal of Construction Engineering and Management, Vol. 131, No. 4, PP. 477-486
[2]. Burns S., Liu L., Feng C., (1996), “The LP/IP hybrid method for construction time-cost trade-off analysis", Construction Management and Economics, Vol. 14, PP. 199-215.
[3]. Elmaghraby S.E., “Resource allocation via dynamic programming in activity networks”, (1995), European Journal of Operational Research, Vol. 64, PP. 199-215.
[4]. De P., Dunne E.J., Gosh J.B., Wells C.E., (1995), “The discrete time-cost trade-off problem revisited”, European Journal of Operational, Vol. 81, No. 2, PP. 225-238.
[5]. Siemens N., (1971), “A simple CPM time-cost trade-off algoritm”, Management Science, Vol. 18, No. 3B, PP. 354-363.
[6]. Moselhi O., (1993), “Schedule compression using the direct stiffness method”, Canidian Journal of Civil Engineering, Vol.20, PP. 65-72.
[7]. Hegazy T., (1999), “Optimization of construction time-cost trade-off analysis using genetic algorithms”, Canidian Journal of Civil Engineering, Vol.26, PP. 685-697.
[8]. Zheng D.X.M., Ng S.T., Kumaraswamy M., (2005), “Applying Pareto ranking and niche formation to genetic algorithm-based multi objective time-cost optimization”, Journal of Construction Engineering and Management, Vol.131, No. 11, 2005, PP. 81-91.
[9]. De, P., Dunne, E., Ghosh, J., & Wells, C., (1997), "Complexity of the discrete time/cost trade-off problem for project networks", Operations Research, Vol. 45, PP. 302–306.
[10]. Taheri Amiri, M.J, Haghighi, F, Eshtehardian, E, Abessi, O, (2017), “Optimization of Time, Cost, and Quality in Critical Chain Method Using Simulated Annealing”, International Journal of  Engineering, Vol 30, No 5, pp. 705-713, Doi: 10.5829/idosi.ije.2017.30.05b.00.
[11]. Mungle, S, Benyoucef, L, Son, Y.J, Tiwari, M.K, (2013)  “A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems:A case study of highway construction project”, Engineering Applications of Artificial Intelligence, Vol 26, 1953–1966.
[12]-Li, H., Zhang, H., (2013), "Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraint", Automation in construction, Vol. 35, PP. 431-438.
[13]- Afshar-Nadjafi, B., (2014), "Multi-mode resource availability cost problem with recruitment and release dates for resources", Applied Mathematical Modelling, Vol. 38, PP. 5347-5355.
[14]-Coughlan, E.T., Lubbecke, M.K., Schulz, J., (2015), "A branch-price-cut algorithm for multi-mode resource leveling", European Journal of Operational Research, Vol. 245, PP. 70-80.
[15]-Nabipoor Afruzi, E., Roghanian, E., Najafi, A. A., Mazinani, M., (2013), "A multi-mode resource constraint discrete time-cost trade off problem solving using an adjusted fuzzy dominance genetic algorithm", Scientia Iranica, Vol. 20, No. 3, PP. 931-944.
[16]-Jaboui, B., Damak, N., Siarry, P., Rebai, A., (2008), "A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problem", Applied mathematics and computation, Vol. 195, PP. 299-308.
[17]-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, PP. 409-418.
[18]. Taheri Amiri, M.J, Haghighi, F, Eshtehardian, E, Hematian, M, Kordi, H, (2017), “Optimization of Time and Costs in Critical Chain Method Using Genetic Algorithm”, Journal of Engineering and Applied Sciences, Vol 12, No 4, 871-876, Doi: 10.3923/jeasci.2017.871.876.
[19]. Taheri Amiri, M.J, Haghighi, F, Eshtehardian, E, Abessi, O, (2018), “Multi-project time-cost optimization in critical chain with resource constraints”, KSCE Journal of Civil Engineering, Vol.22, No. 10, 3738-3752.
[20]. Creemers, S., (2019), "The preemptive stochastic resource-constrained project scheduling problem", European Journal of Operational Research",Vol.227, No. 1, 238-247.
[21]. Vanhoucke, M., Coelho, J., (2019), "Resource-constrained project scheduling with activity splitting and setup times", Computers & Operations Research, Vol.109, 230-249.
[22]. Mehrabian A.R. and Lucas C., (2006), “A novel numerical optimization algorithm inspired from weed colonization”, Ecological Informatics, Vol. 1, pp. 355-366.
[23]. Long, L,D., Ohsato, A., (2009), "A genetic algorithm-based method for scheduling repetitive construction projects", Automation in Construction, Vol.18, No. 4, 499-511.