Multi-Objective Time-Cost Optimization Using Cobb-Douglas Production Function and Hybrid Genetic Algorithm

Shen, Zhigang; Hassani, Ashkan; Shi, Qian

doi:10.3846/13923730.2014.897966

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…While classical genetic algorithm is often claimed to be deficient in terms of sufficient search intensification, memetic algorithm (MA) takes advantage of capability of efficient heuristics by combining domain knowledge and population-based search approaches like GA (Pishvaee et al 2009;Shen et al 2016). MA has been widely used in optimization problems such as distribution problems (Boudia and Prins 2009), timing problem (Moghaddam et al 2009), and etc.…”

Section: Solution Techniquementioning

confidence: 99%

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

et al. 2016

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This paper proposes a model for obnoxious waste location-routing problem (LRP) considering various types of wastes and several treatment technologies. The investigated distribution network includes three echelons of generation nodes, treatment and disposal facilities. A multi-objective LRP model is developed with three objective functions minimizing the treatment and disposal facility undesirability, different costs related to the problem, and eventually the risk associated with transportation of untreated materials. An effective memetic algorithm is developed in which a tabu search algorithm performs the local search. Comparison of exact and meta-heuristic methods run times confirms that the proposed method is effective. Eventually, the developed algorithm is tested on a real-life case study.

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Section: Solution Techniquementioning

confidence: 99%

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

et al. 2016

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“…This type of hybridization provides effective optimal solutions to real-life construction problems. Additionally, in an attempt to account for labor and equipment allocation during time-cost optimization in construction projects, Shen et al [93] implemented a hybrid algorithm that combined a genetic algorithm and the Cobb-Douglas production function (CDPF). The function relates technology to labor input and capital input to compute the total production.…”

Section: Hybrid Algorithm Modelsmentioning

confidence: 99%

Systematic Review of the Time-Cost Optimization Models in Construction Management

2023

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The construction sector is a crucial contributor to the national and global economy. Therefore, improving the efficiency and effectiveness of construction projects can have a significant impact on gross domestic product (GDP). However, managing construction projects can be challenging due to the uncertainties and complexities involved. The three primary interrelated constraints of construction projects, namely, time, scope, and cost, require effective management to ensure successful completion. To optimize the time and cost of construction projects, various optimization models and techniques have been proposed in the literature. This paper presents a systematic review of the time-cost optimization models in construction management and proposes some future work to improve the solution of the considered problem. The review categorizes the existing models into three categories: exact models, approximate models, and hybrid algorithm models. The exact models provide optimal solutions but require a lot of computational time and may not be efficient in solving multi-objective and large-scale problems. The approximate models provide near-optimal solutions and reduce computational effort but may not be efficient in solving large-scale projects. The hybrid algorithm models combine the good properties of different algorithms to provide high-quality and efficient solutions. The purpose of this paper is accomplished through a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The paper analyzes the contribution, advantages, and limitations of each category and provides recommendations for future work. Based on the review, several recommendations for future work are provided, including the development of hybrid models that combine different optimization techniques, the incorporation of risk management into optimization models, and the use of advanced data analytics techniques to improve the accuracy of optimization models. Overall, this paper provides an up-to-date comprehensive review of the time-cost optimization models used in construction management and offers valuable insights for researchers and practitioners in this field. The findings of this review can be used to guide future research and improve the effectiveness of optimization models for construction projects.

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“…The results show that both of these algorithms find reasonable solutions; however, CBO could find the result in a less computational time having a better quality. Shen, Hassani, & Shi (2016) considered the problem with Coub-Douglas production function and hybrid GA. Hou, Zhao, Wu, Moon, & Wang (2017) formulated a FA to target the optimal combination of the project makespan (start time, finish time) and execution mode of each project activity by using a series of unique mathematical models. Elloumi, Fortemps, & Loukil (2017) On the other hand, the researchers who studied multi objective optimization problems have focused on the hybrid metaheuristic methods.…”

Section: State Of the Artmentioning

confidence: 99%

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Albayrak

Özdemir

2018

rdlc

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Completion of the activities within optimal time and cost plays a significant role in construction projects. Recently, project managers have to decrease the total durations and costs of the projects more than before due to the competitive environment. Mostly, decision makers usually seek different alternatives which reduce time and cost. As well as being one of the most major topics of construction management, this problem called time-cost trade-off (TCTO) which is extremely difficult to solve with traditional mathematical methods. In recent years, metaheuristic algorithms are outstanding methods in this field due to their flexible and adaptable structure. This paper presents a new algorithm called F-PSO which consists of hybridizing Firefly Algorithm (FA) with Particle Swarm Optimization (PSO). In this method, the problem is modelled with various execution modes to select the optimal one for each activity. The applicability and validity of the proposed method is confirmed by performing 18-activity project as a benchmark problem. Comparison of numerical results with different metaheuristic algorithms demonstrates the effectiveness and efficiency of F-PSO with regard to optimality of time and cost outcomes.

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Multi-Objective Time-Cost Optimization Using Cobb-Douglas Production Function and Hybrid Genetic Algorithm

Cited by 10 publications

References 44 publications

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

Systematic Review of the Time-Cost Optimization Models in Construction Management

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

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