Research on Optimization of Project Time-Cost-Quality Based on Particle Swarm Optimization

Research on Optimization of Project Time-Cost-Quality Based on Particle Swarm Optimization

Yanqing Song (Yellow River Conservancy Technical Institute, Kaifeng, China) and Genran Hou (Yellow River Conservancy Technical Institute, Kaifeng, China)
DOI: 10.4018/IJISSCM.2019040106

Abstract

In order to make proper time-cost-quality decisions for projects, an improved particle swarm optimization algorithm is applied. First, the optimal model of project time-cost-quality is constructed considering all factors. Second, the basic theory of particle swarm algorithms is summarized, and the improved particle swarm algorithm is put forward based on vector principle, and then the rotational base technology is introduced into the improved particle swarm algorithm to construct a multiple objective optimization algorithm. Finally, the simulation analysis is carried out using a project as example, and the optimal parameters are obtained.
Article Preview
Top

Introduction

The aims of project manager are to achieve the short time, low cost and high quality in engineering project. The three factors influence and control each other. The other factors are inevitably damaged while meeting optimal condition of a factor. Therefore, it is a research hotspot in engineering project management field of optimizing the time, cost and quality. In actual engineering construction, the project quality is generally affected to a certain extent due to time compression. Any real number between 0 and 1 is used to describe the quality of every activity to quantify it. The quality of whole project is a kind of function of every activity quality. In recent years, the project delays seriously in the period of work, and the cost is difficult to be controlled, and the quality is difficult to be ensured (Mayassa and Jully, 2018). Therefore the comprehensive optimization of project time-cost-quality is key work of project construction period. In recent years, many scientists have researched the time-cost-quality of project, and some excellent achievements are obtained. Some models were proposed to aid construction managers in developing practical and near optimal schedules for repetitive projects. The existing models have acquired good effect, however they lack the performance of pure simultaneous optimization because the single factor is considerer during optimal process, the final result can get only minimum duration, low total cost or good quality respectively. It is necessary to construct multiple objective optimal model to obtain optimal time, cost and quality of project at same time. The time-cost-quality optimization of project is high dimensional, dynamical, strong coupled and nonlinear problem. Traditional Nonlinear programming, dynamic programming and successive optimization and other methods are only applied to single objective optimization, it is difficult to be solved when complex degree increases. The constraint method and weight method can transfer the multiple objective problem to single objective problem, which can not obtain multiple feasible solutions at same time and are not suitable to engineering application (Tran et al., 2018; Tran et al., 2015). The intelligent algorithm has been widely applied in engineering optimization, which concludes genetic algorithm, ant colony algorithm, particle swarm algorithm. The particle swarm algorithm has quick convergence speed, however it is easy to premature and fall into local optimization. The rotation technology is introduced into the particle swarm algorithm to generate the improved particle swarm algorithm to improve the convergence and distribution performance of algorithm. The improved particle swarm algorithm can be applied into the optimization of project time-cost-quality.

The aim of multi objective coordination management and comprehensive optimization is to realize the goal of low cost, short work period, high quality and less environmental pollution under the premise of ensuring safety. For the traditional project management, the period, cost and quality are the three main objectives of control. Now, with the management concept and the change of the whole society, the progress of science and technology and the increasing of the people's living standard, the realization of these three goals can not meet the management goal of the current society. Environmental pollution has become one of the common concerns of all countries in the world. Safety goals and environmental protection targets have become an important part of the project management target system.

As far as environmental protection targets are concerned, the greater investment in environmental protection will inevitably result in the increase of investment cost and delay in the period of work. That is to say, the better realization of the environmental protection target in the short term and the income of the enterprise is the unity of opposites. The better realization of the cost target, the project time target and the quality goal is really related to the investment income of the construction enterprises, and the relations of mutual restriction and mutual influence also exist between the control targets which are closely related to the interests of the construction enterprises.

Therefore, how to find a balance between the various targets, and not to cause pressure on the natural environment, and to make the main control target of the project to be better realized is the main problem to be solved in the paper. The purpose of this paper is to provide better choice combination and decision basis for the decision-makers in the process of project management through the study of the multi-objective optimization of the project.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 13: 4 Issues (2020): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2019)
Volume 11: 4 Issues (2018)
Volume 10: 4 Issues (2017)
Volume 9: 4 Issues (2016)
Volume 8: 4 Issues (2015)
Volume 7: 4 Issues (2014)
Volume 6: 4 Issues (2013)
Volume 5: 4 Issues (2012)
Volume 4: 4 Issues (2011)
Volume 3: 4 Issues (2010)
Volume 2: 4 Issues (2009)
Volume 1: 4 Issues (2008)
View Complete Journal Contents Listing