A New Model to Improve Project Time-Cost Trade-Off in Uncertain Environments

Alzarrad, Ammar; Fonseca, Daniel

doi:10.5772/intechopen.74022

Cited by 7 publications

(10 citation statements)

References 14 publications

(22 reference statements)

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“…Decision analysis considers the paradigm in which decision-makers contemplate a choice of action in a risky environment. Decision analysis is designed to help decision-makers choose between a set of predetermined alternatives [12]. e variety in the quality of the available data about a decision-related problem calls for models and tools that can help in data processing.…”

Section: Fuzzy Multicriteria Decision-making (Fmcdm)mentioning

confidence: 99%

“…is parameter (a 1 , a M , a 2 ) signifies the smallest possible value, the most promising value, and the largest possible value, respectively [12]. In FAHP, the pairwise evaluations of both criteria and the alternatives are completed using linguistic terms, which are represented by TFN.…”

Section: Fuzzy Multicriteria Decision-making (Fmcdm)mentioning

confidence: 99%

“…To fuzzify this numeric scale, TFN is used to represent uncertainty in the traditional AHP approach. is model uses the linguistic variables and the fuzzy triangular scale that are shown in Table 2, as suggested by Alzarrad and Fonseca [12]. e decision-makers compare the criteria or alternatives using the linguistic terms shown in Table 2, according to the matching TFN of these terms.…”

Section: Fmcdm Model Stagementioning

confidence: 99%

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Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects

Alzarrad

Moynihan

Hatamleh

et al. 2019

Advances in Civil Engineering

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As is often the case in project scheduling, when the project duration is shortened to decrease total cost, the total float is lost resulting in added critical or nearly critical activities. This, in turn, results in decreasing the probability of completing the project on time and increases the risk of schedule delays. To solve this problem, this research developed a fuzzy multicriteria decision-making (FMCDM) model. The objective of this model is to help project managers improve their decisions regarding time-cost-risk trade-offs (TCRTO) in construction projects. In this model, an optimization algorithm based on fuzzy logic and analytic hierarchy process (AHP) has been used to analyze the time-cost-risk trade-off alternatives and select the best one based on selected criteria. The algorithm was implemented in the MATLAB software and applied to two case studies to verify and validate the presented model. The presented FMCDM model could help produce a more reliable schedule and mitigate the risk of projects running overbudget or behind schedule. Further, this model is a powerful decision-making instrument to help managers reduce uncertainties and improve the accuracy of time-cost-risk trade-offs. The presented FMCDM model employed fuzzy linguistic terms, which provide decision-makers with the opportunity to give their judgments as intervals comparing to fixed value judgments. In conclusion, the presented FMCDM model has high robustness, and it is an attractive alternative to the traditional methods to solve the time-cost-risk trade-off problem in construction.

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Section: Fuzzy Multicriteria Decision-making (Fmcdm)mentioning

confidence: 99%

Section: Fuzzy Multicriteria Decision-making (Fmcdm)mentioning

confidence: 99%

Section: Fmcdm Model Stagementioning

confidence: 99%

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Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects

Alzarrad

Moynihan

Hatamleh

et al. 2019

Advances in Civil Engineering

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“…This schedule compression strategy may adversely impact project cost performance because the overstaffing of critical activities may result in wasted or idle time in these activities. Trade-offs between elapsed time and the associated cost of crew is required to determine the best crew configurations [1]. This is not a trivial issue because of the complex relationships between elapsed time, crew configurations, and their associated costs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

Alzarrad¹

2021

Concepts, Applications and Emerging Opportunities in Industrial Engineering

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Resources planning and operations are essential concerns and specialty areas within industrial engineering and project management. Crew configuration plays a significant role in resource planning and operations. Crew configuration inefficiency is one of the most common reasons for the low productivity of manpower. Resources planning contains some inherent uncertainties and risks because it is an estimate of unknown values. Many factors affect resource planning. Some of these factors are fuzzy variables such as expert's judgment, and some of them are random variables such as direct cost of equipment. The objective of this chapter is to present a method that combines fuzzy logic and Monte Carlo simulation (MCS) for the selection of the best crew configuration to perform a certain task. The model presented in this chapter is a joint propagation method based on both the probability theory of MCS and the possibility theory of fuzzy arithmetic. The research outcomes indicate that the presented model can reduce the duration and cost of a certain task, which will help reduce the cost and duration of the project.

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“…Al-Zarrad and Fonseca [14] examined the fuzzy activity-based costing method that takes into account uncertainty in time and cost values. According to the results of the analysis, the established model is easy to apply, gives better results than GA, and can increase the reliability of time-cost trade-off decisions.…”

Section: Introductionmentioning

confidence: 99%

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

Eırgash¹

2021

IJMFS

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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.

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A New Model to Improve Project Time-Cost Trade-Off in Uncertain Environments

Cited by 7 publications

References 14 publications

Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects

Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects

Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

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

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