The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework

Possel, Bastiaan; Wismans, Luc Johannes Josephus; Berkum, Eric C. van; Bliemer, Michiel C.J.

doi:10.1007/s11116-016-9738-y

Cited by 30 publications

(17 citation statements)

References 38 publications

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“…A multi-objective network design formulation is proposed by Possel et al (2018). The objectives are based on total emissions, the number of traffic accident fatalities and the total travel time.…”

Section: Network Designmentioning

confidence: 99%

Network design for line-based autonomous bus services

2021

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The maturing of autonomous driving technology in recent years has led to several pilot projects and the initial integration of autonomous pods and buses into the public transport (PT) system. An emerging field of interest is the design of public transport networks operating autonomous buses and the potential to attract higher levels of travel demand. In this work a multi-objective optimization and multi-agent simulation framework is developed to study potential changes in the network design and frequency settings compared to conventional PT systems when autonomous vehicles (AV) systems are deployed on fixed-route networks. During the optimization process multiple deployment scenarios (network configurations and service frequency) are evaluated and optimized considering the operator cost, user cost and infrastructure preparation costs of the system. User-focused network design and operator-focused network design are studied for a real-world urban area in Sweden. The results provide insights into the network design and level of service implications brought about by the deployment of autonomous bus (AB) when those are integrated in route-based PT systems. We show that the deployment of autonomous buses result with a network design that increases service ridership. In the context of our case study this increase is likely to primarily substitute walking.

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Section: Network Designmentioning

confidence: 99%

Network design for line-based autonomous bus services

2021

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“…According to the explanation introduced in the previous paragraphs, the model can be characterized as a multi-objective model with linear and non-linear constraints (see Table 2). Since the Evolutionary Algorithms (EA metaheuristic algorithms) present good results for the resolution of models of this nature [45], the NSGA-II algorithm is used (see Table 3). The parameters shown in Table 1 are the 'NSGA-II population's chromosomes'.…”

Section: Resolution Of the Modelmentioning

confidence: 99%

Joint Optimization of Routes and Container Fleets to Design Sustainable Intermodal Chains in Chile

Martínez-López

Chica

2020

Sustainability

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This paper introduces a decision support tool for sustainable intermodal chains with seaborne transport, in which the optimization of a multi-objective model enables conflicting objectives to be handled simultaneously. Through the assessment of ‘door-to-door’ transport in terms of costs, time, and environmental impact, the most suitable maritime route and the optimized fleet are jointly calculated to maximize the opportunities for success of intermodal chains versus trucking. The resolution of the model through NSGA-II algorithms permits to obtain Pareto fronts that offer groups of optimized solutions. This is not only useful to make decisions in the short term, but also to establish long-term strategies through assessment of the frontiers’ behavior obtained when a sensitivity analysis is undertaken. Thus, consequences of transport policies on intermodal performance can be analyzed. A real-life case is studied to test the usefulness of the model. From the application case, not only the most suitable Motorway of the Seas with their optimized fleets are identified for Chile, but also significant general findings are provided for both policy makers and heads of ports to promote the intermodal option regardless of their geographical locations.

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“…In this study, the lower level uses P1: MTAP‐EMR , which models travellers’ path choice behavior with multitype recharge and derives network equilibrium flow patterns in response to different design options from the upper level. In this study, we employ the static setting of OD demands and proportions of different vehicle classes as a representation of expectation, which has been widely adopted for transport planning purposes (Unnikrishnan and Lin, ; He et al., ; Possel et al., ; He et al., ). The demand of EVs can be estimated by studying the factors influencing take‐up of EVs and market penetration, such as car purchase decisions, average vehicle life, subsidies, projected pricing structures, and media influences (Eppstein et al., ; Shepherd et al., ).…”

Section: Model Formulationmentioning

confidence: 99%

Multitype Recharge Facility Location for Electric Vehicles

Zhang

Rey

Waller

2018

Computer aided Civil Eng

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The motivation of this study is to minimize the system-level travel time costs and greenhouse emissions, which include tailpipe emissions by internal combustion engine vehicles (ICEVs) and smokestack emissions indirectly caused by electric vehicles (EVs), while satisfying EVs' replenishment need in transport networks subject to financial restraints for infrastructure development. In this study, we address recharge facility locations of EVs, where two types of recharge services are taken into account, that is, traditional charging stations and modern charging lanes. The multitype recharge facility location problem is formulated by employing the bilevel framework of the network design problem. In the lower-level program, the mixed-vehicular traffic assignment problem with en-route multitype recharge is employed, which accounts for both ICEVs and EVs with various driving ranges. The upper-level program aims to minimize the total system travel costs by selecting the optimal solution from a set of infrastructure design options considering both expansions of road capacities and provisions of multitype recharge facilities for EVs. In the algorithmic framework, we propose a tailored metaheuristic to solve medium to large instances. Systematic evaluation is conducted to test the efficacy of the proposed approach. The results highlight the impacts of traffic composition, distance ranges of EVs, budget levels and facility expenses on the project selection and evaluation. The results indicate that the two design objectives, to respectively minimize the network-wide travel time and greenhouse emissions, are conflicting for certain scenarios. Additionally, the results demonstrate the advantages of the network design problem (NDP) considering both multitype recharge service provision and road capacity

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The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework

Cited by 30 publications

References 38 publications

Network design for line-based autonomous bus services

Network design for line-based autonomous bus services

Joint Optimization of Routes and Container Fleets to Design Sustainable Intermodal Chains in Chile

Multitype Recharge Facility Location for Electric Vehicles

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