Contract bundling considerations in urban road project scheduling

Miralinaghi, Mohammad; Davatgari, Amir; Seilabi, Sania E.; Labi, Samuel

doi:10.1111/mice.12740

Cited by 14 publications

(17 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, GA, which was introduced into engineering in the 1990s by Adeli and Cheng (1993, 1994a, 1994b, is most widely used. The most widely used multi-objective optimization algorithms are based on GAs (Adeli & Kumar, 1995;Hegazy, 1999;Kim & Adeli, 2001) and PSO (Gutierrez & Kennedy & Eberhart, 1995), with representatives being Non-dominated Sorting Genetic Algorithm II (NSGA-II) (Kociecki & Adeli, 2014;Miralinaghi et al, 2021;Zawidzki & Jankowski, 2019) and multi-objective PSO (MOPSO), respectively. Studies in the field of scheduling (Nemati-Lafmejani et al, 2019) have shown that the solution quality and efficiency of PSO are better than those of GAs.…”

Section: Related Algorithmsmentioning

confidence: 99%

Segment‐condition‐based railway track maintenance schedule optimization

Chang

Liu

Tang

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level with days as the time units. Second, they fail to consider the bidirectional feedback and dynamic impact between predicted condition and maintenance scheduling. Third, actual on‐site operation strategies (e.g., opportunistic and centralized maintenance) are neglected. To address these issues, this paper proposes a novel operation‐level maintenance scheduling framework based on segment condition considering both opportunistic and centralized maintenance strategies. According to the problem characteristics, ae hybrid multi‐objective optimization algorithm based on a quantum‐behaved particle swarm optimization was designed. Furthermore, by utilizing actual practical cases, the effectiveness of the model and algorithm was verified via comparison with MOPSO, NSGA‐II, NSGA‐III, and Gurobi in different scenarios.

show abstract

Section: Related Algorithmsmentioning

confidence: 99%

Segment‐condition‐based railway track maintenance schedule optimization

Chang

Liu

Tang

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…The first category addresses the scheduling of projects with durations of several days or even months (Bagloee et al, 2018;Gong & Fan, 2016;Lee, 2009;Lethanh et al, 2017;Li & Fan, 2021;Miralinaghi et al, 2020Miralinaghi et al, , 2021D. Yang et al, 2018;Zheng et al, 2012Zheng et al, , 2014.…”

Section: The Road Scheduling Problemmentioning

confidence: 99%

“…D. Yang et al (2018) considered the nonequilibrium state of traffic and its development during the construction period to an equilibrium state. Miralinaghi et al (2020Miralinaghi et al ( , 2021 developed different frameworks for scheduling long-term road projects in terms of a few months. In this context, static network modeling is used to capture the route choice of travelers.…”

Section: The Road Scheduling Problemmentioning

confidence: 99%

“…F I G U R E 9 Sensitivity of the travel and road project costs to value of time For the rest of the analysis, according to Patidar et al (2007) and Miralinaghi et al (2021), the weights of the agency cost and user cost are assumed to be 0.65 and 0.35, respectively. The sensitivity of (i) total travel cost incurred by road users and (ii) total road project cost incurred by the city agency to the value of travel time (VOT) is analyzed.…”

Section: Sensitivity Analysis Of Within-day Road Project Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning

Saneii

Kazemeini

Seilabi

et al. 2022

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

City infrastructure agencies routinely implement road projects that address various elements of urban infrastructure. The majority of these projects are shortterm in nature (e.g., utility repair), as they are completed in a few hours within 8:00 a.m. to 5:00 p.m. of a workday. The implementation of these projects during working hours, in spite of the inconvenience imposed on road users, helps the agency avoid extra labor costs associated with nonregular working hours. Careful scheduling of these projects can prevent unduly increased travel delays (road users' interest) while keeping project costs low (the agency's interest). This study presents a bi-level framework for scheduling short-term urban road projects that analyzes the implicit tradeoffs between the two stakeholders' interests. The upper-level model establishes the optimal schedule considering the project characteristics, such as cost and duration. The lower-level model captures the dynamic user equilibrium conditions that yield the road users' path and departure time choices. The bi-level model is a mixed-integer program with nonlinear constraints. Recognizing the relatively low efficiency of traditional solution methods, this paper proposes a deep-neural-network-ensemble-assisted active learning (DN2EA2L) algorithm and adopts a fixed-point algorithm for solving the bi-level model. The numerical experiment uses the Sioux Falls network to demonstrate the efficiency of the DN2EA2L, compared to conventional metaheuristic methods. It is shown that travel time increases due to the project implementation during the peak hours can outweigh the agency's saving in wage costs. Further, it is shown that a significant reduction in the road users' value of time enables the agency to schedule more projects during regular working hours. INTRODUCTIONRoad project implementation continues to pose a major cause of nonrecurring congestion in urban road networks (FHWA, 2022). These projects, regardless of their shortterm or long-term nature, cause an increase in road user travel times. Since the pioneering research of Adeli

show abstract

“…Bundling projects in the same corridor can lead to lower project costs than when bundling projects at different locations ( 5 ). The bundling of projects that are in different locations also has an adverse effect on the travel time, resulting in public inconvenience ( 7 ). STAs can benefit from the project bundling method if the appropriate bundle size is selected that is feasible to attract a larger number of bidders, increasing the competition in the market and reducing the project cost significantly ( 8, 9 ).…”

mentioning

confidence: 99%

Mapping of State Transportation Agencies’ Practices and Perceptions in Project Bundling

Shrestha

Shan

Goodrum

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

State transportation agencies (STAs) have been using the project bundling method primarily on bridge projects. While many studies suggest that bundling bridges can reduce project costs and expedite project delivery, more research is needed to help STAs to achieve advanced use of project bundling by understanding the current state of the practice and perceptions about project bundling among STA representatives. This paper presents the detailed findings of a survey, including the perceived benefits, challenges, and risks involved in the bundling method among STA representatives. The survey found that project bundling helps reduce individual project costs in a bundle as well as agency administrative costs and provides an individual project with the flexibility to schedule around other projects in a bundle. Project bundling was found to have a major impact on the cost and delivery time of the project. The major factors that are considered for bundling effectiveness were found to be work type, project type, and spatial proximity. The major concern that most of the STAs had during bundling was that the bundled projects may eliminate the competition from smaller contractors. However, the concerns over the bundling method were found to be less among agencies with a high-level experience of bundling. The survey also found that the majority of STAs have not quantified the impact of the project bundling yet. The results from this study suggest that further research on bundling mixed-type projects and risks associated with bundled contracts is necessary.

show abstract

Contract bundling considerations in urban road project scheduling

Cited by 14 publications

References 59 publications

Segment‐condition‐based railway track maintenance schedule optimization

Segment‐condition‐based railway track maintenance schedule optimization

A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning

Mapping of State Transportation Agencies’ Practices and Perceptions in Project Bundling

Contact Info

Product

Resources

About