Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems

Feng, Chung Wei; Liu, Liang Y.; Burns, Scott

doi:10.1061/(asce)0887-3801(1997)11:3(184)

Cited by 370 publications

(244 citation statements)

References 11 publications

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“…The activity relationship for the model project consists of 18 activities and three modes of construction for each activity and their associated time and cost are presented in [6,16]. Indirect cost rate was $1000/day.…”

Section: Case Studymentioning

confidence: 99%

“…The GA based-approach is to minimize the project overall time and cost and RKTCO is applied in the case study of a project of eighteen activities originally introduced by Feng et al [6], see Figure 2. The activity relationship for the model project consists of 18 activities and three modes of construction for each activity and their associated time and cost are presented in [6,16].…”

Section: Case Studymentioning

confidence: 99%

“…Based on Feng et al [6] five new problems are generated: three experiments using the parallel expansion of the 18-activity problem and two using the serial expansion. The parallel and serial problems consisting of 36, 54 and 180 activities are described in Golzarpoor [4].…”

Section: Case Studymentioning

confidence: 99%

“…However, for large number of activity in network and complex problem, integer programming needs a lot of computation effort (Feng et al [6]). …”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have tried to introduce evolutionary algorithms to find global optima such as genetic algorithm (GA) (Feng et al [6]; Gen and Cheng [21]; Zheng et al [10]; Zheng and Ng [9]; Mendes [16,18] and Parveen and Saha [32]) the particle swarm optimization algorithm (Yang [10]), ant colony optimization (ACO) (Xiong and Kuang [28]; Ng and Zhang [24]; Afshar et al [2]) and harmony search (HS) (Geem [29]). In this paper, the optimal time and cost generated by the GA techniques are compared with those produced by other techniques through some problems obtained from literature.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

Magalhães-Mendes¹

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

“…However, for large number of activity in network and complex problem, integer programming needs a lot of computation effort (Feng et al [6]). …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

Magalhães-Mendes¹

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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A new selection metric for multiobjective hydrologic model calibration

Asadzadeh

Tolson

Burn

2014

Water Resources Research

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A novel selection metric called Convex Hull Contribution (CHC) is introduced for solving multiobjective (MO) optimization problems with Pareto fronts that can be accurately approximated by a convex curve. The hydrologic model calibration literature shows that many biobjective calibration problems with a proper setup result in such Pareto fronts. The CHC selection approach identifies a subset of archived nondominated solutions whose map in the objective space forms convex approximation of the Pareto front. The optimization algorithm can sample solely from these solutions to more accurately approximate the convex shape of the Pareto front. It is empirically demonstrated that CHC improves the performance of Pareto Archived Dynamically Dimensioned Search (PA-DDS) when solving MO problems with convex Pareto fronts. This conclusion is based on the results of several benchmark mathematical problems and several hydrologic model calibration problems with two or three objective functions. The impact of CHC on PA-DDS performance is most evident when the computational budget is somewhat limited. It is also demonstrated that 1,000 solution evaluations (limited budget in this study) is sufficient for PA-DDS with CHC-based selection to achieve very high quality calibration results relative to the results achieved after 10,000 solution evaluations.

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Trade-off ranking method for multi-criteria decision analysis

Jaini

Utyuzhnikov

2016

J. Multi-Crit. Decis. Anal.

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Multi-criteria decision analysis presumes trade-off between different criteria. As a result, the optimal solution is not unique and can be represented by the Pareto frontier in the objective space.Each Pareto solution is a compromise between different objectives. Despite a limited number of Pareto optimal solutions, the decision-maker eventually has to choose only one option. Such a choice has to be made with the use of additional preferences not included in the original formulation of the optimization problem. The paper represents a new approach to an automatic ranking that can help the decision-maker. In contrast to the other methodologies, the proposed method is based on the minimization of trade-off between different Pareto solutions. To be realized, the approach presumes the existence of a well-distributed Pareto set representing the entire Pareto frontier. In the paper, such a set is generated with the use of the directed search domain algorithm.The method is applied to a number of test cases and compared against two existing alternative approaches. KEYWORDSdirected search domain algorithm, multi-criteria decision analysis, multi-objective optimization, Pareto optimal solution, trade-off, ranking INTRODUCTIONIn the real-life design, it is required to improve different objectives simultaneously. A trade-off between the objectives is usually unavoidable because of the constraints. As a result, the optimal solution is not unique and corresponds to a so-called Pareto solution. In the objective space, all Pareto solutions create a Pareto frontier. For a practical decision-making analysis, the Pareto frontier is represented by a Pareto set that contains a finite number of optimal solutions. Eventually, the decision-maker has to choose only one solution. This leads to the problem of ranking because the formal definition of the Pareto solution does not presume any preferences. An additional algorithm is required to introduce the ranking.In the multi-criteria decision analysis, the ranking problem has been developed for the last 20 years. However, there are no universal approaches. Each method stands on its own background and principles.The most natural approach is to introduce individual preferences. One of the basic and simplest multi-criteria decision analysis techniques is the sum of weightage calculation model. In this technique, a weight is assigned to each criterion to denote its importance. Each aggregate function is then calculated as the sum of weightage criteria. A classic work on the weight determination is by Eckenrode (1965). Eckenrode worked with 24 expert judges, who were required to put a weightage on six criteria in a specified experiment related to an air-defence system.Another well-known decision-making method is the analytic hierarchy process (AHP). AHP was proposed by Saaty (1980). The essence of this method is that human judgement is used in performing evaluations. AHP structures a decision problem into a hierarchy with the goal, decision criteria, and alternatives. Then, it uses the pairwise ...

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Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems

Cited by 370 publications

References 11 publications

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

Multiobjective Optimization Using Genetic Algorithms in Time-Cost Construction Project Scheduling Problem

A new selection metric for multiobjective hydrologic model calibration

Trade-off ranking method for multi-criteria decision analysis

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