Crew optimization in planning and control of earthmoving operations using spatial technologies

Abstract

This paper presents a model designed for planning, tracking and control of earthmoving operations. The paper briefly describes the main modules and components of the developed model to maintain continuity and focuses primarily on the Crew Optimization Module of the model. The proposed model utilizes and combines genetic algorithm and spatial technologies including Global Positioning System (GPS) and Geographic Information System (GIS) to support the management functions of the developed model. The model assists in selecting near optimum crew formations in the planning phase using genetic algorithm (GA) supported by GIS. GA is used in conjunction with a set of rules developed to speedup the optimization process and to avoid generating and evaluating hypothetical and unrealistic crew formations. GIS is employed to automate data acquisition and to analyze the collected spatial data. GPS is used for site data collection and for tracking construction equipment in near real time. The model accounts for resources that are available to contractors and it is capable of reconfiguring crew formations dynamically during the construction phase while site operations are in progress. The optimization of crew formation considers: 1) construction time, 2) construction cost, or 3) construction time and cost. The model is also capable of generating crew formations to meet, if possible, specified time and/or cost constraints. It is capable of generating graphical and tabular reports with various degrees of detail to suit the requirements of project teams. At the core of the proposed model is its central database that houses the needed data for the analysis. The developed model has been implemented in prototype software, using object-oriented programming and Microsoft Foundation Classes (MFC), and has been coded using visual C++ V.6.The developed software operates in Microsoft Windows’ environment. To illustrate its capabilities in crew optimization, an example project, drawn from the literature, is analyzed.

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