| dc.description.abstract | Abstract
The success or failure of civil engineering is a lot of factors that are interrelated, the transport process is smooth, is the possession of a very important key, at the same time in the face of the price of sand and gravel constantly rising, how to ensure the quality and the smooth supply of materials, so that the cost control in the most reasonable, to maximize the profit situation, has become the management of sand and gravel plant important. At present, the designation of the general sand and gravel vehicles by the senior engineering staff, with the experience of the past to determine the transport plan, the lack of scientific data corroboration, resulting in many unreasonable places, resulting in cost of waste.
This research is based on the transportation assignment of the sand and gravel plant, provide the gravel material of ready-mixed concrete factory as the research scope, set up a mathematical optimization model with integer programming structure, assist the owner of the gravel plant to manage the transportation and dispatch of the gravel truck. Using a variety of basic data and various conditions to set the limit parameters, written in Excel data and then input Lingo 12.0 software package, through the operation of the program and sensitivity analysis, to find the most cost-effective transport solutions. At the same time, if because of the external environment change or the demand planning restriction condition changes, this research model may according to the newest condition adjustment parameter, obtains the new sand and gravel transportation regulation painting. In order to verify the applicability of the model, this study was carried out to 5 ready-mixed concrete plants from 15 gravel trucks in 2 gravel mills for example test, the results obtained from the whole program are compared with the method of artificial experience decision assignment, and the result is good in both the assigned time and the cost of transportation.
Keywords: Gravel Factory, Gravel Truck, Assignment, Optimization, Overall Planning. | en_US |