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Optimization for Roads' Construction: Selection, Prioritization, and Scheduling

Journal article
Authors Saeed Asadi Bagloee
Majid Sarvi
Michael Patriksson
Mohsen Asadi
Published in Computer-Aided Civil and Infrastructure Engineering
Volume 33
Issue 10
Pages 833-848
ISSN 1093-9687
Publication year 2018
Published at Department of Mathematical Sciences
Pages 833-848
Language en
Subject categories Computational Mathematics, Control Engineering, Computer Science


Computer-Aided Civil and Infrastructure Engineering Limited resources (budget, labor, machinery) have a significant toll on the roads' construction. The question of interest is: given variations of resources over a lengthy construction time, what would be the best construction scheduling plan, or how to optimize the Gantt chart while considering two highly challenging features (1) prerequisite conditions and (2) the interdependency of the benefit of the projects’ completions. We formulate it as a bilevel problem where the objective function is to minimize generalized costs and the lower level accounts for the drivers’ route choice. We employ a solution algorithm based on a supervised learning technique (a linear regression model of machine-learning) and an integer programming problem and it is applied to the datasets of Winnipeg and Chicago. The regression model was found to be a tight approximation which resulted in an efficient algorithm (the CPU time is almost a linear function of the number of iterations). Moreover, the proposed methodology can render promising results (at least locally optimal solutions). This article is the first to formulate the Gantt chart using linear binary constraints and optimize it tailored to real-life case studies.

Page Manager: Webmaster|Last update: 9/11/2012

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