Web-based construction equipment fleet management system: cost-effective global and local allocation

Abstract

Over the last two decades, construction contractors have been gradually making more investments in construction equipment to meet their needs associated with increasing volumes of construction projects. At present, from an operational perspective, almost all contractors pay more attention to maintaining their equipment fleets in well-sustained workable conditions and having a high accessibility of the necessary equipment pieces. However, such an approach alone is not enough to maintain an efficient and sustainable business. In particular, for large-scale construction companies that operate in multiple sites in the U.S. or overseas, the problem extends to an optimal allocation of available equipment. Given the current state of the construction industry in the U.S., this problem can be solved by geographically locating equipment pieces and then wisely re-allocating them among projects. Identifying equipment pieces geographically is a relatively easy task. The difficulty arises when informed decision-making is required for equipment allocation among job sites. The actual allocation of equipment should be both economically feasible and technologically preferable. To help in informed decision-making, an optimization model is developed as a mixed integer program. This model is formed based on a previously successfully developed decision-support model for construction equipment selection. The proposed model incorporates logical strategies of supply chain management to optimally select construction equipment for any construction site while taking into account the costs, availability, and transportation-related issues as constraints. The model benefits those responsible for informed decision-making for construction equipment selection and allocation. It also benefits the owners of construction companies, owing to its cost-minimization objective.

Keywords

Construction equipment / Equipment assignment optimization / Web-based asset management

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2017 The Author(s) 2017. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)