Research of profit optimization for paving road asphalt concrete

• Main Authors: Chung-wen Sun, 孫崇文
• Other Authors: Shang-yao Yan
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/59449557475909407257

碩士 === 國立中央大學 === 土木工程學系碩士在職專班 === 99 === The resources and constraints of road asphalt concrete paving projects vary from case to case. To achieve maximum returns of the costs and the goal of timely completion of projects, how to use the linear planning optimization model to obtain maximum returns under constraints and demands of road asphalt concrete paving efficiency has become an important issue of road asphalt concrete pavement project managers. With efficiency optimization for a MRT station road asphalt concrete paving project as an example, to provide JV industry with a reference in asphalt concrete purchase and paving projects, this study established a mathematical optimization model based on integer programming to help the JV industry to integrate currently available resources to plan the implementation consistently and achieve maximum returns of the costs. When external environment and constraints change, the research model can adjust parameters at any time according to the latest conditions to conduct new demand planning. In addition, by sensitivity analysis, the model can actively adjust relevant parameters according to the sensitivity change trends of various parameters to get new plans in response to practical changes. This study set parameters of various constraints according to basic data and limiting conditions of each case. Data are inputted into LINGO8.0 in the form of EXCEL to obtain the cost optimization results of various cases after planning. To verify the model rationality, this study tested the case of the implementation by a JV manufacturer of an asphalt concrete paving of MRT station road project, and compared with costs obtained using maximum cost returns of the optimization model. It was verified that the optimization model proposed in this study is more efficient and less costly than the experiential one. Hence, the proposed method can be flexibly applied to cost planning problems such as advance planning and review to help decision-makers in planning operations.