A multi-objective integrated model for selecting, scheduling, and budgeting road construction projects

Hosseininasab, Seyyed-Mohammadreza; Shetab-Boushehri, Seyyed-Nader; Hejazi, Seyed Reza; Karimi, Hadi

doi:10.1016/j.ejor.2018.04.051

Cited by 49 publications

(33 citation statements)

References 45 publications

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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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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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“…Multi-objective discrete optimization problems (MODOPs) [1] are the operation optimization problems with discrete variables and multiple objectives, which together with the multi-objective continuous optimization problems constitute the field of multi-objective optimization problems (MOPs) [2]. Compared with continuous MOPs, there is relatively little research on MODOPs although these problems exist widely in real-world applications, such as 0-1 knapsack problems [3], production planning [4], vehicle routing [5], location selection [6], project scheduling [7], resource allocation [8]. Since most of these engineering application problems are subject to constraints, this can be categorized into constrained multi-objective discrete optimization problems (CMODOPs).…”

Section: Introductionmentioning

confidence: 99%

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Wang

Xiong

et al. 2021

Complex Intell. Syst.

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Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

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“…Santos et al [3] proposed a novel adaptive hybrid GA that incorporated local search techniques to improve the overall efficiency and effectiveness of the search. Hosseininasab et al [16] proposed two multiobjective evolutionary approaches to solve problems of road construction within a reasonable time. These approaches were combinations of different techniques, such as GA, NSGA-II, the Frank-Wolfe algorithm, and the ordered logit model.…”

Section: Introductionmentioning

confidence: 99%

Pavement Maintenance Decision Making Based on Optimization Models

et al. 2021

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Pavement maintenance prioritization considering both quality and cost is an important decision-making problem. In this paper, the actual pavement condition index of city roads was calculated using municipal patrol data. A linear optimization model that maximized maintenance quality with limited maintenance costs and a multi-objective optimization model that maximized maintenance quality while minimizing maintenance costs were developed based on the pavement condition index. These models were subsequently employed in making decisions for actual pavement maintenance using sequential quadratic programming and a genetic algorithm. The results showed that the proposed decision-making models could effectively address actual pavement maintenance issues. Additionally, the results of the single-objective linear optimization model verified that the multiobjective optimization model was accurate. Thus, they could provide optimal pavement maintenance schemes for roads according to actual pavement conditions. The reliability of the models was investigated by analyzing their assumptions and validating their optimization results. Furthermore, their applicability in pavement operation-related decision making and preventive maintenance for roads of different grades was confirmed.

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A multi-objective integrated model for selecting, scheduling, and budgeting road construction projects

Cited by 49 publications

References 45 publications

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Pavement Maintenance Decision Making Based on Optimization Models

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