A robust genetic algorithm for the resource-constrained project scheduling problem under uncertainty

• Main Authors: Chia-Wei Liu, 劉家瑋
• Other Authors: Rong-Ho (Ron) Lin
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/g93ch2

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 98 === Along with the progress of civilization and technological advancements, projects have experienced rapid growth in terms of scale and complexity. Successful execution of projects therefore comes to play an increasingly crucial role in business survival and development. In project implementation, a planned schedule is often delayed by some uncontrollable factors like cost, time, resources, and manpower. So how to reduce the impact of the project runs out of control factor to scheduling is one of the important management issues in enterprise project management. In this paper we develop a good performance and consider the realistic uncertainty project scheduling techniques to enable a robust optimize mechanism to decision-making. Over the past literature most of the publisher use traditional algorithms mode of calculation and only consider the duration of uncertainty for solve the problem under uncertainty. However, not just one uncertainties factor encountered in real world and in the problem effectiveness that algorithms performance have a major influence. Therefore, we integrates the use in resource-constrained project scheduling problem of the genetic algorithm, in order to propose a modified genetic algorithm to minimize total project duration that consider the changes of the duration, changes of the resources amount and changes of the resources availability and combined with robust optimization approach for the resource-constrained project scheduling problem under uncertainty.The results show that modified genetic algorithm and other relevant international journal compared with a significant competitive force. Among them, the random and dynamic environment, the use of robust optimization methods of operation derived sort significantly reduce the effect of uncertainty risks.