Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm

Kim, Kyungki; Walewski, John; Cho, Yong

doi:10.1061/(asce)me.1943-5479.0000374

Cited by 34 publications

(17 citation statements)

References 16 publications

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“…Kim et al [35] used NPGA to optimize cost, time and resource utilization. They optimized the three objectives at the same time.…”

Section: Niched Pareto Genetic Algorithm (Npga)mentioning

confidence: 99%

Overview of Multi-Objective Optimization Approaches in Construction Project Management

Alothaimeen¹,

Arditi²

2020

Multicriteria Optimization - Pareto-Optimality and Threshold-Optimality

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The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012-2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios.

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“…Kim et al [35] used NPGA to optimize cost, time and resource utilization. They optimized the three objectives at the same time.…”

Section: Niched Pareto Genetic Algorithm (Npga)mentioning

confidence: 99%

Overview of Multi-Objective Optimization Approaches in Construction Project Management

Alothaimeen¹,

Arditi²

2020

Multicriteria Optimization - Pareto-Optimality and Threshold-Optimality

View full text Add to dashboard Cite

show abstract

“…As we all know, the four indicators, such as quality, duration, cost and safety management are of great significance to maximize the interests of enterprises. These four critical indicators of construction projects are highly correlated and contradictory, but the engineering projects must meet these conflicting project objectives at the same time [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Construction Project Based on Improved Ant Colony Algorithm

Wang

2020

Teh. vjesn.

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It is the key and difficult problem for the current project management to consider the multi-objective optimization of the four elements, such as quality, duration, cost and safety. To improve the accuracy and efficiency of project management during the engineering construction, considering the advantages and disadvantages of the traditional quality-cost-time model, the four elements were regarded as a system, and a multi-objective optimization model was established. The improved ant colony algorithm was used to carry out multi-objectives of construction projects to overcome the premature defect of the traditional method. The optimal plan of the project was found and the overall efficiency of the construction project management was improved. Results show the optimized ant colony algorithm can avoid the low efficiency of the optimal solution search and the shortcoming of the initial pheromone. The engineering practice proves that the enhanced algorithm has solved the problem of the multiobjective optimization of quality, duration, cost and safety. The obtained conclusions are of significant reference value to direct the similar engineering practice.

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“…Many previous studies have attempted to find solutions to facilitate multiobjective optimization, such as minimization of construction duration, cost, and resource fluctuation [4,5]. Meanwhile, various methods have been introduced to optimize construction scheduling while considering constraints such as the workforce, resource availability, or construction site layout that have been considered in earlier studies [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Developing Shuffled Frog‐Leaping Algorithm (SFLA) Method to Solve Power Load‐Constrained TCRTO Problems in Civil Engineering

Tao

Mao

et al. 2019

Advances in Civil Engineering

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It is extensively acknowledged that excessive on-site electricity power load often causes power failure across a construction site and surrounding residential zones and can result in unforeseen schedule delay, construction quality problems, life inconvenience, and even property loss. However, energy management, such as power load optimization, has long been ignored in construction scheduling. This study aims to develop a modified shuffled frog-leaping algorithm (SFLA) approach in project scheduling to aid decision-makers in identifying the best Pareto solution for time-cost-resource trade-off (TCRTO) problems under the constraint of precedence, resource availability, and on-site peak electricity power load. A mathematical model including three objective functions and five constraints was established followed by the application of the modified SLFA on real-case multiobjective optimization problems in construction scheduling. The performance of SLFA was compared with that of the nondominated sorting genetic algorithm (NSGA II). The results showed that the developed new approach was superior in identifying optimal project planning solutions, which could essentially assist on-site power load-oriented schedule decision-making for construction teams.

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Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm

Cited by 34 publications

References 16 publications

Overview of Multi-Objective Optimization Approaches in Construction Project Management

Overview of Multi-Objective Optimization Approaches in Construction Project Management

Multi-objective Optimization of Construction Project Based on Improved Ant Colony Algorithm

Developing Shuffled Frog‐Leaping Algorithm (SFLA) Method to Solve Power Load‐Constrained TCRTO Problems in Civil Engineering

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