Solving urban transit route design problem using selection hyper-heuristics

Ahmed, Leena; Mumford, Christine L.; Kheiri, Ahmed

doi:10.1016/j.ejor.2018.10.022

Cited by 79 publications

(81 citation statements)

References 47 publications

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“…In this method, the routes are generated based on the shortest paths searching between OD pairs, and another procedure dealing with constraints is usually needed. For example, the construction and repair procedures used by Ahmed et al [35] and Szeto and Jiang [36], the initial solution generation procedure based on greedy algorithm used by Nikolić and Teodorović [24] and Nayeem et al [37], and the Floyd's algorithm and feasibility check used by Chew et al [22]. With the method, the direct demand between departure and destination stations can be met, but more vehicles on the network will be needed [38].…”

Section: Generation Of Initial Populationsmentioning

confidence: 99%

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An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Chai

Qing-huai

2020

Journal of Advanced Transportation

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The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.

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Section: Generation Of Initial Populationsmentioning

confidence: 99%

“…Firstly, the -shortest path search method in nonweighted road network is used to get the top paths between two endpoints of a line. All the top paths should meet the constraint about nodes number which can be expressed by the distance between the two endpoints of each path, as shown in Equation (35).…”

Section: Demand Assignmentmentioning

confidence: 99%

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Chai

Qing-huai

2020

Journal of Advanced Transportation

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“…A new field of hyper-heuristic methods embedding data science techniques has recently been developed (Asta andÖzcan, 2015). Experiments on a hyper-heuristic benchmark framework (Kheiri and Keedwell, 2015), urban transit route design problem (Ahmed et al, 2019), wind farm layout optimization problem (Wilson et al, 2018), high school timetabling problem (Kheiri and Keedwell, 2017) and on water distribution optimization problem (Kheiri et al, 2015) have shown that applying a sequence of low level heuristics can potentially improve the quality of solutions more than those that simply select and apply a single low level heuristic.…”

Section: A Sequence-based Selection Hyper-heuristic (Team: Akhe)mentioning

confidence: 99%

Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

Kheiri

Dragomir

Mueller

et al. 2019

EURO Journal on Transportation and Logistics

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This paper reports on the results of the VeRoLog Solver Challenge 2016-2017: the third solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics Optimization. The authors are the winners of second and third places, combined with members of the challenge organizing committee. The problem central to the challenge was a rich VRP: expensive and therefore scarce equipment was to be redistributed over customer locations within time windows. The difficulty was in creating combinations of pickups and deliveries that reduce the amount of equipment needed to execute the schedule, as well as the lengths of the routes and the number of vehicles used. This paper gives a description of the solution methods of the above-mentioned participants. The second place method involves sequences of 22 low level heuristics: each of these heuristics is associated with a transition probability to move to another low level heuristic. A randomly drawn sequence of these heuristics is applied to an initial solution, after which the probabilities are updated depending on whether

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“…A prominent example is the instance published by Mandl (1979) [used e.g. in Ahmed et al (2019), Arbex and da Cunha (2015) Baaj and Mahmassani (1991), Fan and Mumford (2010) and Nayeem et al (2014)]. Another often used test instance [e.g.…”

Section: Introductionmentioning

confidence: 99%

An adaptive scaled network for public transport route optimisation

2019

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We introduce an adaptive network for public transport route optimisation by scaling down the available street network to a level where optimisation methods such as genetic algorithms can be applied. Our scaling is adapted to preserve the characteristics of the street network. The methodology is applied to the urban area of Nottingham, UK, to generate a new benchmark dataset for bus route optimisation studies. All travel time and demand data as well as information of permitted start and end points of routes, are derived from openly available data. The scaled network is tested with the application of a genetic algorithm adapted for restricted route start and end points. The results are compared with the real-world bus routes.An adaptive scaled network for public transport routes As the layout of the street network is essential for the design of bus routes, it is important that an instance network sufficiently reflects the characteristics of the street network. We therefore propose a network design procedure which scales down the network to a size manageable for meta-heuristic-based optimisations, while at the same time preserving the characteristics of the urban street network. Scaling down an urban street network is desirable principally to restrict the computation times needed for the passenger objective, which is usually the main bottleneck and leads to an increase of the run time with f (N 3 ) , N being the number of nodes in the network [see Mumford (2013) for a full explanation]. For our modest desktop set up, 1 we determined 500 nodes, which would result in a runtime of about 600 h, to be the upper-most limit for practical work.The down-scaling of the street network is achieved by devising simple and robust rules applicable to all urban layouts. The procedure further includes the identification of potential terminal nodes, an aspect vital for route design in an urban context.We will limit this work to instance generation and route network optimisation with restricted start and end points, as it is part of an incremental approach to more realistic public transport network optimisation. Our generation procedure is used to produce an instance for bus route optimisation in the extended urban area of Nottingham, UK. Further, a route initialisation procedure and a modified heuristic route optimisation algorithm, both specialised for work with restricted route start and end points, are applied to the generated instance. The optimisation results are compared to the real-world bus routes. 2 The main contributions of this paper are as follows:1. A novel methodology for generating an instance dataset, by systematically scaling down a street network and utilising census data (see Sect. 2). The generated instance will be published online for free use for all researchers. 2. An additional methodology for transforming pre-existing public transport routes to fit the scaled-down street network (see Sect. 4). 3. A multi-objective genetic algorithm modified for restricted route start and end points, to allow direct compariso...

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Solving urban transit route design problem using selection hyper-heuristics

Cited by 79 publications

References 47 publications

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Tackling a VRP challenge to redistribute scarce equipment within time windows using metaheuristic algorithms

An adaptive scaled network for public transport route optimisation

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