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Time-Cost Trade-Off Optimization with a New Initial Population Approach

Year 2019, Volume: 30 Issue: 6, 9561 - 9580, 01.11.2019
https://doi.org/10.18400/tekderg.410934

Abstract

Considering the competitive environment in all
industries, completion on time is crucial for the stakeholders of a project.
This favorable target is achieved by finding the optimal set of time-cost
alternatives and this is known as time-cost trade-off problem (TCTP) in the
literature. In this study, a new initial population approach is presented to improve
the quality of the optimal set of time-cost alternatives. It put a predefined
number of the solutions of the single objective TCTP into the initial
population of teaching learning-based algorithm, which is utilized as an
optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to
descend the randomness on initial population and to decrease the searching
effort to catch the optimal set of time-cost alternatives in the search space.
The proposed methodology is tested on a series of benchmark problems and the
obtained results are compared with those available in the technical literature.
It can produce good solutions as effective as with other techniques applied for
simultaneous optimization of TCTPs.

References

  • [1] Meyer, W.L., Shaffer, L.R. Extending CPM for Multiform Project Time-Cost Curves. Journal of Construction Division 91(1), 45-68, 1965.
  • [2] De, P., Dunne, E.J., Ghosh, J.B., Wells, C.E. Complexity of the discrete time-cost trade-off problem for project networks. Operations Research 45(2), 302–306, 1997.
  • [3] Demeulemeester, E., De Reyck, B., Foubert, B., Herroelen, W., Vanhoucke, M. New computational results on the discrete time/cost trade-off problem in Project networks. Journal of the Operational Research Society 49(11), 1153-1163, 1998.
  • [4] Yang, H.H., Chen, Y.L. Finding the critical path in an activity network with time-switch constraints. European Journal of Operational Research 120(3), 603-613, 2000.
  • [5] Vanhoucke, M. New computational results for the discrete time/cost trade-off problem with time-switch constraints. European Journal of Operational Research 165(2), 359-374, 2005.
  • [6] Feng, C.W., Liu, L., Burns, S.A. Using genetic algorithms to solve construction time-cost trade-off problems. Journal of Computing in Civil Engineering 11(3), 184 –189, 1997.
  • [7] Siemens N. A Simple CPM Time-Cost Trade off Algorithm. Management Science 17(6), 354–363, 1971.
  • [8] Vanhoucke, M., Debels, D. The discrete time/cost trade-off problem: extensions and heuristic procedures. Journal of Scheduling 10(5), 311-326, 2007.
  • [9] Zheng, D., Ng, S., Kumaraswamy, M. applying pareto ranking and niche formation to genetic algorithm-based multiobjective time–cost optimization. Journal of Construction Engineering and Management 131(1), 81–91, 2005.
  • [10] Sönmez, R., Bettemir, O.H. A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert Systems with Applications 39(13), 11428-11434, 2012.
  • [11] Anagnostopoulos, K.P., Kotsikas, L. Experimental evaluation of simulated annealing algorithms for the time-cost trade-off problem. Applied Mathematics and Computation 217(1), 260-270, 2010.
  • [12] Yang, I.T. Performing complex project crashing analysis with aid of particle swarm optimization algorithm. International Journal of Project Management 25(6), 637-646, 2007.
  • [13] Zhang, H., Xing, F. Fuzzy-multi-objective particle swarm optimization for time –cost – quality trade-off in construction. Automation in Construction 19(8), 1067-1075. 2010.
  • [14] Aminbakhsh, S. Hybrid particle swarm optimization algorithm for obtaining Pareto front of discrete time–cost trade-off problem. M.Sc. Thesis, Middle East Technical University, Ankara, Turkey, 2013.
  • [15] Aminbakhsh, S., Sönmez, R. Applied discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem. Expert Systems with Applications 51, 177-185, 2016.
  • [16] Colorni, M., Dorigo, V.M., Trubian, M. Ant system for job-shop scheduling. Journal of Operations Research, Statistics and Computer Science 34, 39–53, 1994.
  • [17] Ng, S.T., Zhang, Y.S. Optimizing construction time and cost using ant colony optimization approach. Journal of Construction Engineering and Management 134(9), 721-728, 2008.
  • [18] Xiong, Y., Kuang, Y. Applying an ant colony optimization algorithm-based multiobjective approach for time–cost trade-off. Journal of Construction Engineering and Management 134(2), 153–156, 2008.
  • [19] Afshar, A., Ziaraty, A., Kaveh, A., Sharifi, F. Nondominated archiving multicolumn ant algorithm in time–cost trade-off optimization. Journal of Construction Engineering and Management 135(7), 668-674, 2009.
  • [20] Elbeltagi, E., Hegazy, T., Grierson, D. A modified shuffled frog-leaping optimization algorithm: Applications to project management. Structure and Infrastructure Engineering 3(1), 53–60, 2007.
  • [21] Hafizoğlu, A. B. Discrete time/cost trade-off problem in project scheduling. M.Sc. Thesis, Middle East Technical University, Turkey, 2006.
  • [22] Abdel-Raheem, M., Khalafallah, A. Using Electimize to solve the time cost trade-off problem in construction engineering. PhD dissertation, University of Central Florida Orlando, USA, 2011.
  • [23] Narayanan, A.S., Suribabu, C.R. Multiobjective optimization of construction project time cost-quality trade-off using differential evolution algorithm. Journal of Civil Engineering 8(4), 375-392, 2014.
  • [24] Eirgash, M.A. Pareto-front performance of multiobjective teaching learning based optimization algorithm on time-cost trade-off optimization problems. M.Sc. Thesis, Karadeniz Technical University, Turkey, 2018.
  • [25] Deb, K., Pratab, A., Agrawal, S., Meyarivan, T. A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Transaction on Evolution of Computing 6, 182–197, 2000.
  • [26] Rao, R.V., Savsani, V.J., Vakharia, D.P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computation Aided Design 43(3), 303-315, 2011.
  • [27] Rao, R.V., Patel, V. Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms. Engineering Optimization 44(8), 965-983, 2011.
  • [28] Rao, R.V., Savsania, V.J., Balic, J. Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization 44(12), 1447-1462, 2012.
  • [29] Toğan, V.“Design of planar steel frames using teaching-learning based optimization. Engineering Structures 34, 225-232, 2012.
  • [30] Dede, T., Toğan, V. A teaching learning based optimization for truss structures with frequency constraints. Structural Engineering of Mechanics 53(4), 833-845. 2015.
  • [31] Bettemir, Ö.H. Optimization of time-cost-resource trade-off problems in project scheduling using meta-heuristic algorithms. PhD dissertation, Middle East Technical University, Turkey, 2009.

Time-Cost Trade-Off Optimization with a New Initial Population Approach

Year 2019, Volume: 30 Issue: 6, 9561 - 9580, 01.11.2019
https://doi.org/10.18400/tekderg.410934

Abstract

Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project. This favorable target is achieved by finding the optimal set of time-cost alternatives and this is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It put a predefined number of the solutions of the single objective TCTP into the initial population of teaching learning-based algorithm, which is utilized as an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

References

  • [1] Meyer, W.L., Shaffer, L.R. Extending CPM for Multiform Project Time-Cost Curves. Journal of Construction Division 91(1), 45-68, 1965.
  • [2] De, P., Dunne, E.J., Ghosh, J.B., Wells, C.E. Complexity of the discrete time-cost trade-off problem for project networks. Operations Research 45(2), 302–306, 1997.
  • [3] Demeulemeester, E., De Reyck, B., Foubert, B., Herroelen, W., Vanhoucke, M. New computational results on the discrete time/cost trade-off problem in Project networks. Journal of the Operational Research Society 49(11), 1153-1163, 1998.
  • [4] Yang, H.H., Chen, Y.L. Finding the critical path in an activity network with time-switch constraints. European Journal of Operational Research 120(3), 603-613, 2000.
  • [5] Vanhoucke, M. New computational results for the discrete time/cost trade-off problem with time-switch constraints. European Journal of Operational Research 165(2), 359-374, 2005.
  • [6] Feng, C.W., Liu, L., Burns, S.A. Using genetic algorithms to solve construction time-cost trade-off problems. Journal of Computing in Civil Engineering 11(3), 184 –189, 1997.
  • [7] Siemens N. A Simple CPM Time-Cost Trade off Algorithm. Management Science 17(6), 354–363, 1971.
  • [8] Vanhoucke, M., Debels, D. The discrete time/cost trade-off problem: extensions and heuristic procedures. Journal of Scheduling 10(5), 311-326, 2007.
  • [9] Zheng, D., Ng, S., Kumaraswamy, M. applying pareto ranking and niche formation to genetic algorithm-based multiobjective time–cost optimization. Journal of Construction Engineering and Management 131(1), 81–91, 2005.
  • [10] Sönmez, R., Bettemir, O.H. A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert Systems with Applications 39(13), 11428-11434, 2012.
  • [11] Anagnostopoulos, K.P., Kotsikas, L. Experimental evaluation of simulated annealing algorithms for the time-cost trade-off problem. Applied Mathematics and Computation 217(1), 260-270, 2010.
  • [12] Yang, I.T. Performing complex project crashing analysis with aid of particle swarm optimization algorithm. International Journal of Project Management 25(6), 637-646, 2007.
  • [13] Zhang, H., Xing, F. Fuzzy-multi-objective particle swarm optimization for time –cost – quality trade-off in construction. Automation in Construction 19(8), 1067-1075. 2010.
  • [14] Aminbakhsh, S. Hybrid particle swarm optimization algorithm for obtaining Pareto front of discrete time–cost trade-off problem. M.Sc. Thesis, Middle East Technical University, Ankara, Turkey, 2013.
  • [15] Aminbakhsh, S., Sönmez, R. Applied discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem. Expert Systems with Applications 51, 177-185, 2016.
  • [16] Colorni, M., Dorigo, V.M., Trubian, M. Ant system for job-shop scheduling. Journal of Operations Research, Statistics and Computer Science 34, 39–53, 1994.
  • [17] Ng, S.T., Zhang, Y.S. Optimizing construction time and cost using ant colony optimization approach. Journal of Construction Engineering and Management 134(9), 721-728, 2008.
  • [18] Xiong, Y., Kuang, Y. Applying an ant colony optimization algorithm-based multiobjective approach for time–cost trade-off. Journal of Construction Engineering and Management 134(2), 153–156, 2008.
  • [19] Afshar, A., Ziaraty, A., Kaveh, A., Sharifi, F. Nondominated archiving multicolumn ant algorithm in time–cost trade-off optimization. Journal of Construction Engineering and Management 135(7), 668-674, 2009.
  • [20] Elbeltagi, E., Hegazy, T., Grierson, D. A modified shuffled frog-leaping optimization algorithm: Applications to project management. Structure and Infrastructure Engineering 3(1), 53–60, 2007.
  • [21] Hafizoğlu, A. B. Discrete time/cost trade-off problem in project scheduling. M.Sc. Thesis, Middle East Technical University, Turkey, 2006.
  • [22] Abdel-Raheem, M., Khalafallah, A. Using Electimize to solve the time cost trade-off problem in construction engineering. PhD dissertation, University of Central Florida Orlando, USA, 2011.
  • [23] Narayanan, A.S., Suribabu, C.R. Multiobjective optimization of construction project time cost-quality trade-off using differential evolution algorithm. Journal of Civil Engineering 8(4), 375-392, 2014.
  • [24] Eirgash, M.A. Pareto-front performance of multiobjective teaching learning based optimization algorithm on time-cost trade-off optimization problems. M.Sc. Thesis, Karadeniz Technical University, Turkey, 2018.
  • [25] Deb, K., Pratab, A., Agrawal, S., Meyarivan, T. A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Transaction on Evolution of Computing 6, 182–197, 2000.
  • [26] Rao, R.V., Savsani, V.J., Vakharia, D.P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Computation Aided Design 43(3), 303-315, 2011.
  • [27] Rao, R.V., Patel, V. Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms. Engineering Optimization 44(8), 965-983, 2011.
  • [28] Rao, R.V., Savsania, V.J., Balic, J. Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization 44(12), 1447-1462, 2012.
  • [29] Toğan, V.“Design of planar steel frames using teaching-learning based optimization. Engineering Structures 34, 225-232, 2012.
  • [30] Dede, T., Toğan, V. A teaching learning based optimization for truss structures with frequency constraints. Structural Engineering of Mechanics 53(4), 833-845. 2015.
  • [31] Bettemir, Ö.H. Optimization of time-cost-resource trade-off problems in project scheduling using meta-heuristic algorithms. PhD dissertation, Middle East Technical University, Turkey, 2009.
There are 31 citations in total.

Details

Primary Language English
Subjects Civil Engineering
Journal Section Articles
Authors

Vedat Toğan 0000-0001-8734-6300

Mohammad Azim Eırgash 0000-0001-5399-115X

Publication Date November 1, 2019
Submission Date March 29, 2018
Published in Issue Year 2019 Volume: 30 Issue: 6

Cite

APA Toğan, V., & Eırgash, M. A. (2019). Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi, 30(6), 9561-9580. https://doi.org/10.18400/tekderg.410934
AMA Toğan V, Eırgash MA. Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi. November 2019;30(6):9561-9580. doi:10.18400/tekderg.410934
Chicago Toğan, Vedat, and Mohammad Azim Eırgash. “Time-Cost Trade-Off Optimization With a New Initial Population Approach”. Teknik Dergi 30, no. 6 (November 2019): 9561-80. https://doi.org/10.18400/tekderg.410934.
EndNote Toğan V, Eırgash MA (November 1, 2019) Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi 30 6 9561–9580.
IEEE V. Toğan and M. A. Eırgash, “Time-Cost Trade-Off Optimization with a New Initial Population Approach”, Teknik Dergi, vol. 30, no. 6, pp. 9561–9580, 2019, doi: 10.18400/tekderg.410934.
ISNAD Toğan, Vedat - Eırgash, Mohammad Azim. “Time-Cost Trade-Off Optimization With a New Initial Population Approach”. Teknik Dergi 30/6 (November 2019), 9561-9580. https://doi.org/10.18400/tekderg.410934.
JAMA Toğan V, Eırgash MA. Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi. 2019;30:9561–9580.
MLA Toğan, Vedat and Mohammad Azim Eırgash. “Time-Cost Trade-Off Optimization With a New Initial Population Approach”. Teknik Dergi, vol. 30, no. 6, 2019, pp. 9561-80, doi:10.18400/tekderg.410934.
Vancouver Toğan V, Eırgash MA. Time-Cost Trade-Off Optimization with a New Initial Population Approach. Teknik Dergi. 2019;30(6):9561-80.