Optimizing the development schedule of resort projects by integrating simulation and genetic algorithm

Dzeng, Ren‐Jye; Lee, Hsin Yun

doi:10.1016/j.ijproman.2006.11.008

Cited by 24 publications

(16 citation statements)

References 7 publications

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“…The experiments were conducted using training and testing sets prepared on the basis of 150 actual sale transactions made in a residential section of a Polish city. Dzeng et al [6] applied simulation and a polyploidy genetic algorithm to help in the development of resort projects, which require a continuous and larger investment of resources compared to conventional real estate projects. The proposal allows investors to achieve increased profits, as well as maintaining the cash flow at a secure level.…”

Section: Related Workmentioning

confidence: 99%

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

et al. 2018

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Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

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Section: Related Workmentioning

confidence: 99%

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

et al. 2018

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“…Computer based modeling is widely used for the simulation and analysis of economic problems and is known to be an efficient tool for the optimization of project productivity, represented here in terms of the schedule, and the quantity and quality of the output [60,61]. In the context of project management, a project management tool based on 4D simulation is an effective and popular method which facilitates the exploration, description and interpretation of the processes and behaviors involved in construction projects.…”

Section: Bim-based 4d For Managing Module Manufacturingmentioning

confidence: 99%

BIM-Based 4D Simulation to Improve Module Manufacturing Productivity for Sustainable Building Projects

Lee

Kim

2017

Sustainability

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Modular construction methods, where products are manufactured beforehand in a factory and then transported to the site for installation, are becoming increasingly popular for construction projects in many countries as this method facilitates the use of the advanced technologies that support sustainability in building projects. This approach requires dual factory-site process management to be carefully coordinated and the factory module manufacturing process must therefore be managed in a detailed and quantitative manner. However, currently, the limited algorithms available to support this process are based on mathematical methodologies that do not consider the complex mix of equipment, factories, personnel, and materials involved. This paper presents three new building information modeling-based 4D simulation frameworks to manage the three elements-process, quantity, and quality-that determine the productivity of factory module manufacturing. These frameworks leverage the advantages of 4D simulation and provide more precise information than existing conventional documents. By utilizing a 4D model that facilitates the visualization of a wide range of data variables, manufacturers can plan the module manufacturing process in detail and fully understand the material, equipment, and workflow needed to accomplish the manufacturing tasks. Managers can also access information about material quantities for each process and use this information for earned value management, warehousing/storage, fabrication, and assembly planning. By having a 4D view that connects 2D drawing models, manufacturing errors and rework can be minimized and problems such as construction delays, quality lapses, and cost overruns vastly reduced.

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“…Wang [36] has elaborated a decision support system that, through a data envelopment analysis, converts numerical data into information that can be used to evaluate real estate investments. Dzeng e Lee [10] have proposed a model to optimize the development schedule of resort projects using a polyploidy genetic algorithm.…”

Section: Introduction*mentioning

confidence: 99%

Property Valuations in Times of Crisis: Artificial Neural Networks and Evolutionary Algorithms in Comparison

Tajani

Morano

Locurcio

et al. 2015

Computational Science and Its Applications -- ICCSA 2015

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Abstract. In the current economic situation, characterized by a high uncertainty in the appraisal of property values, the need of "slender" models able to operate even on limited data, to automatically capture the causal relations between explanatory variables and selling prices and to predict property values in the short term, is increasingly widespread. In addition to Artificial Neural Networks (ANN), that satisfy these prerogatives, recently, in some fields of Civil Engineering an hybrid data-driven technique has been implemented, called Evolutionary Polynomial Regression (EPR), that combines the effectiveness of Genetic Programming with the advantage of classical numerical regression. In the present paper, ANN methods and the EPR procedure are compared for the construction of estimation models of real estate market values. With reference to a sample of residential apartments recently sold in a district of the city of Bari (Italy), two estimation models of market value are implemented, one based on ANN and another using EPR, in order to test the respective performance. The analysis has highlighted the preferability of the EPR model in terms of statistical accuracy, empirical verification of results obtained and reduction of the complexity of the mathematical expression.

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Optimizing the development schedule of resort projects by integrating simulation and genetic algorithm

Cited by 24 publications

References 7 publications

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms

BIM-Based 4D Simulation to Improve Module Manufacturing Productivity for Sustainable Building Projects

Property Valuations in Times of Crisis: Artificial Neural Networks and Evolutionary Algorithms in Comparison

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