Least cost optimization of scraper-pusher fleet operations

Scraper is an important piece of heavy construction equipment in road construction. The optimum use of such equipment can be a challenging task for project manager. Different techniques and models have been developed to address this challenge with different degree of success. This paper presents a newly developed model for optimizing scraper-pusher fleet operations. The model utilizes genetic algorithm (GA), linear programming (LP), geographic information system (GIS), and database (DB). The model has been implemented in prototype software and it was applied to two example projects to validate it and to illustrate it's the capabilities. The result obtained by the developed model demonstrates the effectiveness of (1) integrating LP, GA, and GIS to solve mix integer optimization problem, (2) integrating GIS in the optimization process; (3) selecting a near-optimal scraper loading time; and (4) dynamically determining the unit cost of the transported material while searching for near optimum fleet.