An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems

Mohammad Azim Eirgash

doi:doi:10.11648/j.ijmfs.20210702.12

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An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems

Mohammad Azim Eirgash

Published in International Journal of Management and Fuzzy Systems (Volume 7, Issue 2)

Received: 15 July 2021 Accepted: 24 July 2021 Published: 29 July 2021

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Abstract

In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.

Published in	International Journal of Management and Fuzzy Systems (Volume 7, Issue 2)
DOI	10.11648/j.ijmfs.20210702.12
Page(s)	28-40
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Time-cost-trade-off Problems, Pareto-front, Construction Management, TOPSIS

References

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[3]	Gumus, A. T. “Evaluation of hazardous waste transportation firms by using a two-step fuzzy-AHP and TOPSIS methodology”. Expert Systems with Applications, 36: 4067–4074, (2009).
[4]	Yong, D. (2006), “Plant location selection based on fuzzy TOPSIS”. The International Journal of Advanced Manufacturing Technology, 28 (7-8): 839–844.
[5]	Roy, B. (1991) “The outranking approach and the foundations of Electre methods”. Theory and Decision, 31 (1): 49-73.
[6]	Chaudhuri, S., & Deb, K. (2010), “An interactive evolutionary multi-objective optimization and decision making procedure”. Applied Soft Computing, 10 (2): 496–511.
[7]	Bazargan-Lari, M. R. (2014), “An evidential reasoning approach to optimal monitoring of drinking water distribution systems for detecting deliberate contamination events”. Journal of Cleaner Production, 78 (1): 1-14.
[8]	Monghasemi, S., Nikoo, M. R., Fasaee, M. A. K., & Adamowski, J. (2015), “A Novel Multi Criteria Decision Making Model for Optimizing Time-Cost-Quality Trade-off Problems in Construction Projects”. Expert Systems with Applications, 42 (6): 3089-3104.
[9]	Perera, A., Attalage, R., Perera, K., & Dassanayake, V. (2013), “A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems”. Applied Energy, 107: 412-425.
[10]	Eirgash, M. A., and Dede, T. (2018). “A multi-objective improved teaching learning-based optimization algorithm for time-cost trade-off problems.” J. Constr. Eng. Manage. Innovation, 1 (3): 118-128.
[11]	Toğan, V., and Eirgash, M. A. (2019). “Time-cost trade-off optimization of construction projects using teaching learning-based optimization.” KSCE J. Civ. Eng., 23 (1), 10–20.
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[13]	Ng, T. S., & Zhang, Y. (2008), “Optimizing construction time and cost using ant colony optimization approach”. Journal of Construction Engineering and Management, 134 (9): 721-728.
[14]	Mohammad Ammar Al-Zarrad and Daniel Fonseca (2018) “A New Model to Improve Project Time-Cost Trade-Off in Uncertain Environments”, Contemporary Issues and Research in Operations Management, DOI: 10.5772/intechopen.74022.
[15]	Eirgash, M. A. Pareto-Front Performance of Multiobjective Teaching Learning Based Optimization Algorithm on Time-Cost Trade-Off Optimization Problems. Master of Science Thesis, Karadeniz Technical University, (2018), Turkey.
[16]	Rao, R. V., & Patel, V. (2011). “Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms”. Engineering Optimization, 44 (8): 965-983.
[17]	Eirgash, M. A., Toğan, V., and Dede, T. (2019). “A multi-objective decision-making model based on TLBO for the time–cost trade-off problems.” Struct. Eng. Mech., 71 (2), 139-151.
[18]	Feng, C.-W., Liu, L., & Burns, S. A. (1997), “Using genetic algorithms to solve construction time-cost trade-off problems”. Journal of Computing in Civil Engineering, 11 (3): 184-189.

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APA Style

Mohammad Azim Eirgash. (2021). An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. International Journal of Management and Fuzzy Systems, 7(2), 28-40. https://doi.org/10.11648/j.ijmfs.20210702.12

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ACS Style

Mohammad Azim Eirgash. An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. Int. J. Manag. Fuzzy Syst. 2021, 7(2), 28-40. doi: 10.11648/j.ijmfs.20210702.12

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AMA Style

Mohammad Azim Eirgash. An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. Int J Manag Fuzzy Syst. 2021;7(2):28-40. doi: 10.11648/j.ijmfs.20210702.12

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@article{10.11648/j.ijmfs.20210702.12,
  author = {Mohammad Azim Eirgash},
  title = {An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {7},
  number = {2},
  pages = {28-40},
  doi = {10.11648/j.ijmfs.20210702.12},
  url = {https://doi.org/10.11648/j.ijmfs.20210702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20210702.12},
  abstract = {In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.},
 year = {2021}
}

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TY  - JOUR
T1  - An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems
AU  - Mohammad Azim Eirgash
Y1  - 2021/07/29
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmfs.20210702.12
DO  - 10.11648/j.ijmfs.20210702.12
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 28
EP  - 40
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20210702.12
AB  - In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.
VL  - 7
IS  - 2
ER  -

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Author Information

Mohammad Azim Eirgash

Department of Civil Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon, Turkey

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APA Style

Mohammad Azim Eirgash. (2021). An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. International Journal of Management and Fuzzy Systems, 7(2), 28-40. https://doi.org/10.11648/j.ijmfs.20210702.12

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ACS Style

Mohammad Azim Eirgash. An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. Int. J. Manag. Fuzzy Syst. 2021, 7(2), 28-40. doi: 10.11648/j.ijmfs.20210702.12

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AMA Style

Mohammad Azim Eirgash. An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems. Int J Manag Fuzzy Syst. 2021;7(2):28-40. doi: 10.11648/j.ijmfs.20210702.12

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@article{10.11648/j.ijmfs.20210702.12,
  author = {Mohammad Azim Eirgash},
  title = {An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {7},
  number = {2},
  pages = {28-40},
  doi = {10.11648/j.ijmfs.20210702.12},
  url = {https://doi.org/10.11648/j.ijmfs.20210702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20210702.12},
  abstract = {In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems
AU  - Mohammad Azim Eirgash
Y1  - 2021/07/29
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmfs.20210702.12
DO  - 10.11648/j.ijmfs.20210702.12
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 28
EP  - 40
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20210702.12
AB  - In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.
VL  - 7
IS  - 2
ER  -

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