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Construction projects are often associated with partial or full road closures, which result in user costs and community disruptions in terms of reduced business productivity. A number of studies have addressed the problem of scheduling construction projects based on a variety of stakeholder objectives. Yet still, there seems to exist a few gaps regarding (1) possible tradeoffs between road user cost reduction and business cost reduction associated with optimal scheduling, (2) role of the project type (rehabilitation and capacity expansion) on the solution methodology, and (3) lack of solution algorithm to address the problem complexity by deriving the optimal solution. In addressing these gaps, this article adopts a novel approach for developing an optimal project schedule for multiple road projects within a construction horizon. The goal is to minimize the overall cost of the projects to road users and adjacent businesses over the construction horizon. The project scheduling problem is formulated as a mixed-integer nonlinear program. We solve the problem using a local decomposition method. The methodology is demonstrated using the Sioux Falls city network with two project types: capacity expansion and rehabilitation. The results of the numerical experiment suggest that (1) the solution algorithm converges to optimal solution in finite iterations and (2) a network-wide scheduling of urban road projects using explicit optimization can yield a significant reduction in business disruption costs while incurring a relatively smaller increase in system travel time, and overall, is superior to a schedule developed only considering the total system travel time.
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Stored only when both papers are in the corpus — 38 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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