Optimization for Roads' Construction: Selection,Prioritization, and Scheduling |
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Authors: | Saeed Asadi Bagloee Majid Sarvi Michael Patriksson Mohsen Asadi |
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Affiliation: | 1. Melbourne School of Engineering, The University of Melbourne, Victoria 3010, Australia;2. Chalmers University of Technology and University of Gothenburg, SE‐412 96 Gothenburg, Sweden;3. Mechanical Engineering Department, University of Saskatchewan, SK S7N 5A9, Canada |
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Abstract: | 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. |
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