Genetic algorithm-based multi-objective model for scheduling of linear construction projects

Senouci, Ahmed; Al-Derham, Hassan

doi:10.1016/j.advengsoft.2007.08.002

Cited by 61 publications

(35 citation statements)

References 8 publications

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“…Senouci and H. Al-Derham developed a model that generates not only resource utilization plans that optimize construction time and cost but also visualizing the trade-offs among project time and cost in order to support decision makers in evaluating the impact of various resource utilization plans. Flexible genetic algorithm, based on parameters of string size, number of generations, population size, mutation and crossover rates, is also proposed (Senouci & Al-Derham, 2008).…”

Section: Discussionmentioning

confidence: 99%

Genetic Algorithm of Network Graph Multi-Objective Optimization as an Instrument of Project Monitoring

Prokopyev¹

2014

ASS

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Proper tracking of progress remains a vital part of modern project management, defining prospects of successful implementation of planned tasks. There are several popular concepts of project monitoring, such as logical framework approach (LFA), earned value management (EVM), etc., and each of them depends on properly optimized network graph that represents dependences between project tasks. Article describes the features and problems of multi-objective optimization in project management with reference to network graphs. The significant role of network graph optimization for project monitoring systems is proved and the model of multi-objective optimization of the network graph on criterion functions of duration and project cost based on NSGA-II genetic algorithm is proposed as the main purpose of research. Model takes into account the reserves of time on a critical way of the network graph, possibility of decreasing the load of available resources at the expense of time reserves on non-critical ways of the network graph, variety of used resources and options of delegation. One of its main advantages is quite low laboriousness of implementation, that depends on number of nodes on the network graph of the project and on number of possible options of delegation for the project tasks with several alternatives of delegation. Model has been tested on sample project with real data and results have been analyzed.

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Section: Discussionmentioning

confidence: 99%

Genetic Algorithm of Network Graph Multi-Objective Optimization as an Instrument of Project Monitoring

Prokopyev¹

2014

ASS

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“…Another group of researchers developed their own multiobjective GA to reach a set of project schedules with nearoptimum duration, cost, and resource allocation and embedded their algorithm inside MS Project as a macro [12]. In 2008, a multi-objective GA was introduced for scheduling linear construction projects; this focused on optimizing both project cost and time as its objectives [69]. Hooshyar et al [35] presented a GA time-cost tradeoff problem solver with higher calculation speed than the Siemens algorithm.…”

Section: Related Research Studiesmentioning

confidence: 99%

Automation in construction scheduling: a review of the literature

Faghihi

Nejat

Reinschmidt

et al. 2015

Int J Adv Manuf Technol

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Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or optimization tool has been introduced, researchers in the construction field have tried to use it to find the answer to one of their key problems-the "better" construction schedule. Each researcher defines this "better" slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, genetic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling challenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicability of genetic algorithms to the different aspects of managing a project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.

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“…As a kind of common intelligent algorithm, genetic algorithm is based on the natural law of "survival of the fittest" in biological word, and can help decision makers to identify the optimal or near-optimal one from numerous solutions (Aldwaik, Adeli 2014;Senouci, Al-Derham 2008). Its calculation process is generally composed of five steps: select an appropriate coding mode, population initialization, calculate the fitness of each individual, generate next generation population and set termination conditions (Alcaraz, Maroto 2001;Groba et al 2015).…”

Section: Improved Genetic Algorithmmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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Abstract. Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building.

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Genetic algorithm-based multi-objective model for scheduling of linear construction projects

Cited by 61 publications

References 8 publications

Genetic Algorithm of Network Graph Multi-Objective Optimization as an Instrument of Project Monitoring

Genetic Algorithm of Network Graph Multi-Objective Optimization as an Instrument of Project Monitoring

Automation in construction scheduling: a review of the literature

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

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