Robust improvement schemes for road networks under demand uncertainty

Yin, Yafeng; Madanat, Samer; Lu, Xiao Yun

doi:10.1016/j.ejor.2008.09.008

Cited by 132 publications

(71 citation statements)

References 36 publications

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“…The decision-makers' attitudes to risk should be considered as well when deciding the shapes and size of the uncertainty sets. It is important to make a trade-off between the system performance and the level of robustness achieved [13].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Interval Constraint [13]. The travel demand between each O-D pair which is assumed varies independently within a given interval of = [ , ].…”

Section: Robust Network Capacity Estimation Under Demand Uncertaintymentioning

confidence: 99%

“…Considering the exact probability distribution of the O-D demand is still hard to be obtained, the robust optimization is more effective in dealing with this problem. If a limited number of discrete scenarios of O-D demand patterns are detected, the scenario-based robust optimization [13] is conducted, which is a practical approach usually implemented in transportation projects. It is more general to assume the possibility of the travel demand to be a continuous variable within a bounded set, and the set-based robust optimization can be used for decision-making [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Jiang

Cheng

et al. 2017

Journal of Advanced Transportation

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As more and more cities in worldwide are facing the problems of traffic jam, governments have been concerned about how to design transportation networks with adequate capacity to accommodate travel demands. To evaluate the capacity of a transportation system, the prescribed origin and destination (O-D) matrix for existing travel demand has been noticed to have a significant effect on the results of network capacity models. However, the exact data of the existing O-D demand are usually hard to be obtained in practice. Considering the fluctuation of the real travel demand in transportation networks, the existing travel demand is represented as uncertain parameters which are defined within a bounded set. Thus, a robust reserve network capacity (RRNC) model using min-max optimization is formulated based on the demand uncertainty. An effective heuristic approach utilizing cutting plane method and sensitivity analysis is proposed for the solution of the RRNC problem. Computational experiments and simulations are implemented to demonstrate the validity and performance of the proposed robust model. According to simulation experiments, it is showed that the link flow pattern from the robust solutions to network capacity problems can reveal the probability of high congestion for each link.

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Section: Introductionmentioning

confidence: 99%

“…(1) Interval Constraint [13]. The travel demand between each O-D pair which is assumed varies independently within a given interval of = [ , ].…”

Section: Robust Network Capacity Estimation Under Demand Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Jiang

Cheng

et al. 2017

Journal of Advanced Transportation

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“…where ½x + = maxf0, xg The semi-deviation, but not the variance (or standard deviation), is used as an indicator for the risk measure because variance is a symmetric statistic and gives equal weight to deviations above and below the mean without addressing the risks associated with extreme outcomes, 39,53,54 and because mean-variance does not preserve the convexity of the objective function. 51 The semi-deviation risk measure defined in equation (37) overcomes these shortcomings.…”

Section: Chancementioning

confidence: 99%

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

Zhang

Huang

et al. 2015

Advances in Mechanical Engineering

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This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO 2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss-Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

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“…Traditionally, these problems are considered in two forms: the Discrete NDP (DNDP) and the Continuous NDP (CNDP). The DNDP, such as street construction and street orientation, is concerned with the discrete decision variables (e.g., Poorzahedy and Turnquist 1982;Wong and Yang 1997;Chen et al 2002Chen et al , 2007Chen et al , 2010aChen and Yang 2004;Gao et al 2005;Meng and Khoo 2008;, and the CNDP, such as setting signal timing and toll, is concerned with continuous attributes of the network (e.g., Yin 2000;Meng et al 2001;Meng and Yang 2002;Gao and Song 2002;Chiou 2008aChiou ,b, 2009aYin et al 2009;Chen et al 2010b). Another less investigated form is the Mixed Network Design Problem (MNDP) which deals with both continuous and discrete decision variables.…”

Section: Introductionmentioning

confidence: 99%

Bi-objective bimodal urban road network design using hybrid metaheuristics

Miandoabchi

Farahani

Szeto

2011

Cent Eur J Oper Res

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In this paper a bimodal discrete urban road network design problem with bus and car modes is investigated. The problem consists of decision making for lane addition to the existing streets, new street constructions, converting some two-way streets to one-way streets, lane allocation for two-way streets, and the allocation of some street lanes for exclusive bus lanes. Two objectives are considered in the problem: maximization of consumer surplus, and maximization of the demand share of the bus mode. The interaction of automobile and bus flows are explicitly taken into account and a modal-split/assignment model is used to obtain the automobile and bus flows in the deterministic user equilibrium state. The main contribution of the paper lies in proposing a new network design problem that combines the road network design decisions with the decision making for bus networks. The problem is formulated as a mathematical program with equilibrium constraints. A hybrid of genetic algorithm and simulated annealing, a hybrid of particle swarm optimization and simulated annealing, and a hybrid of harmony search and simulated annealing are proposed to solve the problem. Computational results for a number of test networks are presented and investigated.Keywords Bimodal network design · Multi-objective · Elastic demand · Hybrid metaheuristics · Exclusive bus lanes

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Robust improvement schemes for road networks under demand uncertainty

Cited by 132 publications

References 36 publications

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

Robust Evaluation for Transportation Network Capacity under Demand Uncertainty

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

Bi-objective bimodal urban road network design using hybrid metaheuristics

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