Publications / 2013 Proceedings of the 30th ISARC, Montréal, Canada

Optimum Planning for Multi-Project Earthmoving Operations

S. Abu-Samra, A. H. El Hakea, C. Khalil, O. Hosny, A. Elhakeem
Pages 1394-1403 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844)

By virtue of its complex nature, the construction industry comprises a wide spectrum of interrelated variables and factors. This multifaceted character of the construction industry puts the use of engineering modeling tools and techniques on top of project management necessities. This research introduces a macro-level earthmoving management system using Genetic Algorithms (GAs) to reach the optimum allocation of earthmoving equipment. The Earthmoving Equipment Management System (EEMS) functions through four integrated modules: (1) A Central Database containing information about projects and available equipment; (2) An Equipment per Segment Selection module that calculates the cut and fill quantities, plots the Mass Haul Diagrams, and selects the equipment types to be used for each segment; (3) An Equipment Pool module which determines the production and cost for each available equipment, based upon the project site conditions; and (4) An Optimization Engine equipped with a GA optimization solver. This engine formulates the optimum earthmoving cost for all projects, by changing the number of allocated equipment. The Optimization Engine takes into account the number of available equipment and calculates the weekly equipment allocation. The EEMS model was implemented on an earthmoving company conducting five different projects on hand. The model proved to be an effective tool in providing decision makers with optimum equipment utilization on a multi-project scale to minimize the earthmoving cost. The results were then compared with the company’s existing micro-level management system, demonstrating better performance on the level of savings, amounting to 13% of total earthmoving cost.

Keywords: Earthmoving equipment, Multi-project optimization, Genetic Algorithms, Macro-level management