In the field of construction management, project crashing is an approach to shortening the project duration by reducing the duration of several critical project activities to less than their normal activity duration. The goal of crashing is to shorten the project duration while minimizing the crashing cost. In this research, a novel method for construction project crashing is proposed. The method is named as novel improved differential evolution (NIDE). The proposed NIDE is developed by an integration of the differential evolution (DE) and a new probabilistic similarity-based selection operator (PSSO) that aims at improving the DE’s selection process. The PSSO has the role as a scheme for preserving the population diversity and fending off the premature convergence. The experimental result has demonstrated that the newly established NIDE can successfully escape from local optima and achieve a significantly better optimization performance. 1. Introduction In the field of construction management, a construction project can be typically defined as a set of individual activities with their technical/managerial constraints. The nature of construction projects, which is characterized by constant changes in the environment, pressures to maintain schedules/costs with increasingly complex construction techniques, makes the task of project management a significant challenge [1]. Due to the complexity of construction projects, schedule management is proved to be very challenging [2]. Therefore, schedule overrun is not unusual in the construction field. Moreover, there can be a great motivation for the construction contractor and the project owner to reduce the project time [3]. The reason is that as a project progresses, it consumes indirect costs, consisting of the cost of facilities, equipment, and machinery, interest on investment, utilities, labor, and the loss of skills and labor of the project team who are not working at their regular jobs [4]. There also may be severe financial penalties for not completing a project on time; many construction and government contracts have penalty clauses for exceeding the project completion date. In addition, the project owner may desire to reduce the completion time in order to put the facility into operation sooner. In practice, to shorten the project schedule, the manager may accelerate some of the activities at an additional cost, that is, by allocating more or better resources, such as labor and equipment. Minimizing the sum of activity direct costs while meeting a specified deadline is of practical need and this has
References
[1]
M.-Y. Cheng, N.-D. Hoang, A. F. V. Roy, and Y.-W. Wu, “A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion,” Engineering Applications of Artificial Intelligence, vol. 25, no. 4, pp. 744–752, 2012.
[2]
S. Mubarak, Construction Project Scheduling and Control, John Wiley & Sons, 2010.
[3]
R. J. Dzeng, “Identifying a design management package to support concurrent design in building wafer fabrication facilities,” Journal of Construction Engineering and Management, vol. 132, no. 6, pp. 606–614, 2006.
[4]
K. Sears, G. Sears, and R. Clough, Construction Project Management: A Practical Guide to Field Construction Management, John Wiley & Sons, Hoboken, NJ, USA, 5th edition, 2008.
[5]
I.-T. Yang, “Performing complex project crashing analysis with aid of particle swarm optimization algorithm,” International Journal of Project Management, vol. 25, no. 6, pp. 637–646, 2007.
[6]
J. E. Falk and J. L. Horowitz, “Critical path problems with concave cost-time curves,” Management Science, vol. 19, no. 4, pp. 446–455, 1972.
[7]
S. Foldes and F. Soumis, “PERT and crashing revisited: mathematical generalizations,” European Journal of Operational Research, vol. 64, no. 2, pp. 286–294, 1993.
[8]
R. F. Deckro, J. E. Hebert, W. A. Verdini, P. H. Grimsrud, and S. Venkateshwar, “Nonlinear time/cost tradeoff models in project management,” Computers and Industrial Engineering, vol. 28, no. 2, pp. 219–229, 1995.
[9]
P. De, E. James Dunne, J. B. Ghosh, and C. E. Wells, “The discrete time-cost tradeoff problem revisited,” European Journal of Operational Research, vol. 81, no. 2, pp. 225–238, 1995.
[10]
M.-Y. Cheng, N.-D. Hoang, and Y.-W. Wu, “Hybrid intelligence approach based on LS-SVM and differential evolution for construction cost index estimation: a Taiwan case study,” Automation in Construction, vol. 35, pp. 306–313, 2013.
[11]
S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011.
[12]
L.-C. Lien and M.-Y. Cheng, “A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization,” Expert Systems with Applications, vol. 39, no. 10, pp. 9642–9650, 2012.
[13]
K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution A Practical Approach to Global Optimization, Springer, 2005.
[14]
Y. Wang, Z. Cai, and Q. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 55–66, 2011.
[15]
S. Das, A. Abraham, and A. Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 38, no. 1, pp. 218–237, 2008.
[16]
A. Ponsich and C. A. C. Coello, “Differential Evolution performances for the solution of mixed-integer constrained process engineering problems,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 399–409, 2011.
[17]
R. Mallipeddi, “Harmony search based parameter ensemble adaptation for differential evolution,” Journal of Applied Mathematics, vol. 2013, Article ID 750819, 12 pages, 2013.
[18]
R. Mallipeddi, P. N. Suganthan, Q. K. Pan, and M. F. Tasgetiren, “Differential evolution algorithm with ensemble of parameters and mutation strategies,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 1679–1696, 2011.
[19]
M.-Y. Cheng and N.-D. Hoang, “Risk score inference for bridge maintenance project using evolutionary fuzzy least squares support vector machine,” Journal of Computing in Civil Engineering, vol. 28, no. 3, Article ID 04014003, 2014.
[20]
M. Cheng and N. Hoang, “Groutability estimation of grouting processes with microfine cements using an evolutionary instance-based learning approach,” Journal of Computing in Civil Engineering, vol. 28, no. 4, Article ID 04014014, 2014.
[21]
H.-H. Tran and N.-D. Hoang, “An artificial intelligence approach for groutability estimation based on autotuning support vector machine,” Journal of Construction Engineering, vol. 2014, Article ID 109184, 9 pages, 2014.
[22]
R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
[23]
H. Ahuja, Project Management Techniques in Planning and Controlling Construction Projects, Wiley, New York, NY, USA, 1984.
[24]
R. Landa Becerra and C. A. Coello, “Cultured differential evolution for constrained optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 195, no. 33–36, pp. 4303–4322, 2006.
[25]
E. Mezura-Montes, C. A. C. Coello, and E. I. Tun-Morales, “Simple feasibility rules and differential evolution for constrained optimization,” in Proceedings of the 3rd Mexican International Conferenceon Artificial Intelligence (MICAI '04), pp. 707–716, April 2004.
[26]
H.-H. Tran and N.-D. Hoang, “A novel resource-leveling approach for construction project based on differential evolution,” Journal of Construction Engineering, vol. 2014, Article ID 648938, 7 pages, 2014.
[27]
J. Zhang and A. C. Sanderson, “JADE: self-adaptive differential evolution with fast and reliable convergence performance,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 2251–2258, September 2007.
[28]
M. Zhang, W. Luo, and X. Wang, “Differential evolution with dynamic stochastic selection for constrained optimization,” Information Sciences, vol. 178, no. 15, pp. 3043–3074, 2008.
[29]
K. A. de Jong, Analysis of the behavior of a class of genetic adaptive systems [Ph.D. dissertation], University of Michigan, Ann Arbor, Mich, USA, 1975.
[30]
R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, John Wiley & Sons, 2004.
[31]
J. Zhang and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation, vol. 13, pp. 945–958, 2009.