Optimizing the selection and scheduling of multi-class projects using a Stackelberg framework

Miralinaghi, Mohammad; Seilabi, Sania E.; Chen, Sikai; Hsu, Yu-Ting; Labi, Samuel

doi:10.1016/j.ejor.2020.03.051

Cited by 21 publications

(7 citation statements)

References 35 publications

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“…Relatively recent works include Bai et al. (2015), Gharaibeh, Chiu, and Gurian (2006), Mild and Salo (2009), Miralinaghi, Seilabi, Chen, Hsu, and Labi (2020), Miralinaghi, Woldemariam et al. (2020), Mrawira and Amador (2009), Roshandeh et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

Bai

Miralinaghi

Labi

et al. 2020

Computer aided Civil Eng

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Trade‐off analysis, one of the key tenets of multi‐objective optimization for the project selection problem in Transportation Asset Management (TAM), can help decision‐makers quantify and comprehend the consequences of different resource allocations in terms of the multiple measures of system performance. In analyzing TAM trade‐offs, it is vital to account duly for the uncertainties associated with these system‐wide performance measures. In this paper, we present a methodology that integrates chance‐constraint programming, the Lindeberg Central Limit Theorem, and a hybrid NSGA II method, to address the performance uncertainties associated with the TAM multi‐objective optimization problem. Through analyzing the trade‐offs between expenditure and performance, and between different performance measures, we generate Pareto frontiers at different confidence levels using a hybrid NSGA II method. We demonstrate the proposed methodology using a case study involving real‐life assets and the expected cost and performance benefits of projects associated with these assets. Regarding the trade‐off between cost and performance, we determine the extent to which the strengths of these relationships vary across different confidence levels. We find that, generally, for a given network performance level, a higher expenditure is needed to achieve a high confidence level compared to the expenditure needed to achieve a low confidence level, and more importantly, measures these sensitivities. This is the “Price of Confidence” concept. Regarding the trade‐off between different pairs of performance measures under budgetary constraints, we show how these relationships vary with the confidence level specified for the analysis, and we measure the extent to which higher confidence level requirements translate into lower levels of overall system‐wide performance.

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Section: Literature Reviewmentioning

confidence: 99%

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

Bai

Miralinaghi

Labi

et al. 2020

Computer aided Civil Eng

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“…The optimization framework has an inherently bi-level structure. The bi-level framework has been in different contexts of transportation system problems [11]- [17]. At the upper level, the decision-maker is the transport agency who seeks to minimize the total system vehicle emissions by adopting policies that lead to the development (via private sector investment) of optimal locations of EV charging stations and the replacement of existing gas stations, over the planning horizon.…”

Section: Designing a Network Of Electric Charging Stations To Mitigate Vehicle Emissionsmentioning

confidence: 99%

Designing a Network of Electric Charging Stations to Mitigate Vehicle Emissions

Miralinaghi

Correia

Seilabi

et al. 2020

2020 Forum on Integrated and Sustainable Transportation Systems (FISTS)

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Metropolitan authorities continue to seek programs and initiatives to reduce emissions in their jurisdictions. It has been shown that transitioning from fossil fuel to electric propulsion of transportation can help realize this goal. However, the current market penetration of electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) remains very small. This paper proposes a framework to address this problem over a long-term analysis period. The paper accounts for consumers' vehicle-purchasing propensities and their route choices, locations of EV-charging and ICEVrefueling stations. In the proposed framework, new EV charging stations are provided at selected locations and/or existing gas stations are repurposed by the transport agency's decisionmaker (through policy) in conjunction with the private sector (through investment). The paper presents a bi-level mathematical model to capture the decision-making processes of the transport agency and the travelers. Underlying the framework is a solid theoretical foundation for the EV charging network design. The design problem is solved using an active-set algorithm. The study results can serve as guidance for metropolitan transport agencies to establish specific locations and capacities for EV stations and thereby to contribute to longterm reduction of emissions.

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“…Some researchers have investigated the effectiveness of AV dedicated lanes using a bi-level framework. The concept of a bi-level framework has been applied to several problems in the context of transportation systems (Hosseininasab and Shetab-Boushehri, 2015;Gong and Fan, 2016;Hosseininasab et al, 2018;Miralinaghi et al, 2019bMiralinaghi et al, , 2020a. In their framework, the metropolitan authority at the upper level decides on the optimal plan for AV dedicated lanes based on the travel decisions of travelers at the lower level.…”

Section: Lane Management For Avsmentioning

confidence: 99%

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

Seilabi

Tabesh

Davatgari

et al. 2020

Front. Built Environ.

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A key challenge facing cities of today is the persistent and growing urban congestion that has significant adverse effects on economic productivity, emissions, driver frustration, and quality of life. The concept of smart cities, which can revolutionize the management of metropolitan transportation operations and infrastructure, shows great promise in mitigating this problem. Specifically, the automation and connectedness (A&C) of smart city entities such as its infrastructure, services, and vehicles, can be helpful. In this regard, this paper focuses on the potential of autonomous vehicles (AVs) and AV infrastructure, particularly during prospective transition era where there will be mixed streams of AVs and human driven vehicles (HDVs). The paper considers two aspects of this potential: connectivity-enabled travel demand management and travel infrastructure supply through lane management. To demonstrate the opportunity associated with this potential, this paper first presents an AV-enabled tradable credit scheme (TCS) to manage travel demand. Here, the transportation authority distributes travel credits to travelers directly and instantaneously using the AV's A&C features. Travelers use their A&C features to pay these credits for travel at specific locations or times-of-day according to their choices of lane types and links. With regard to supply, this paper considers that the road network consists of two lane types: AV-dedicated, and mixed traffic lanes, and develops a scheme for travel demand and lane management strategies in the AV transition era (TLMAV). Firstly, the paper models the expected travel choices based on user equilibrium concepts, at different levels of AV market penetration. Then, the existence of the optimal solution in terms of link flows and the prevailing travel credit price is demonstrated. Then the paper establishes the optimal TLMAV that minimizes total travel time subject to user equity constraints. The results demonstrate the extent to which HDV users will suffer an increase in travel cost if equity is not considered in the model. The results also show how the transportation agency can use TLMAV to keep HDV travel costs to acceptable levels, particularly during the early stages of the AV transition period. Further, the paper shows how TLMAV could be designed to gradually diminish inequity effects so that travelers, in the long term, are motivated to shift to AVs.

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Optimizing the selection and scheduling of multi-class projects using a Stackelberg framework

Cited by 21 publications

References 35 publications

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

Methodology for analyzing the trade‐offs associated with multi‐objective optimization in transportation asset management under uncertainty

Designing a Network of Electric Charging Stations to Mitigate Vehicle Emissions

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

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