A hybrid meta-heuristic algorithm for solving real-life transportation network design problems

Bagloee, Saeed Asadi; Tavana, Madjid; Ceder, Avishai; Bozic, Claire; Asadi, Mohsen

doi:10.1504/ijlsm.2013.055561

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…In terms of solution methodologies, due to the intrinsic complexity of the general nonconvex bilevel formulation of the NDP, metaheuristic approaches are primarily employed. The use of metaheuristic evolutionary algorithms for multiobjective optimization problems has been exploited over the last 30 years, including genetic algorithms (GAs) (Chakroborty, ; Zhang et al., ), ant colony optimization (Poorzahedy and Abulghasemi, ), simulated annealing algorithms (Friesz et al., ), and hybrid heuristic (Bagloee et al., ). Among all these algorithms, the GA is the most prevalent due to its parallelism and compatibility (Drezner and Salhi, ; Sun et al., ; Unnikrishnan and Lin, ; Farahani et al., ; Zhang et al., ).…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Multitype Recharge Facility Location for Electric Vehicles

Zhang

Rey

Waller

2018

Computer aided Civil Eng

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“…Among these methods one can mention the genetic algorithm (Xiong and Schneider, 1995;Hsieh and Liu, 2004;, simulated annealing (Lee and Yang, 1994), the ant colony (Poorzahedy and Abulghasemi, 2005;Vitins and Axhausen, 2008). In other cases, a combination of several heuristics was used (Bagloee et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

A parsimonious heuristic for the discrete network design problem

Haas

Bekhor

2015

Transportmetrica A: Transport Science

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The problem of selecting the optimal set of transportation projects out of a given set of projects, known as the Network Design Problem (NDP), has been researched for many years. Typical transportation projects are interdependent in their nature, which turns the problem into a very Downloaded by [University of Nebraska, Lincoln] at 04:33 08 October 2015 A c c e p t e d M a n u s c r i p t Haas and Bekhor 2 complex one. When a certain objective is sought, an exact solution of the problem can be derived only by enumerating each possible project combination.Therefore, when a large set of possible combinations is involved an alternative approach must be taken. Meta-heuristic methods usually used for this purpose do not make use of the special properties of the given problem.This paper proposes an alternative heuristic that simplifies significantly the solution process. The benefit of a certain combination of projects is inferred based on a subset (pairs or triplets) of projects. The proposed heuristic is tested on simple networks and applied for a real-size network. The paper also discusses the trade-offs between solution accuracy and computation time.

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“…Examples are: relaxing the UE traffic flow to a system optimal [ 4 , 5 ], replacing the objective function with some less expensive-to-evaluate (surrogate) function [ 6 ] employing metaheuristic algorithm such as genetic algorithm, ant system, simulated annealing [ 7 – 9 ], biologically inspired model [ 10 , 11 ]. A thorough discussion of this can be found in [ 12 – 15 ]. Furthermore, in recent years, rapid expansions of developing and emerging economies (largely in Asia and the Middle East) have made the DNDP relevant more than ever [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

An outer approximation method for the road network design problem

Bagloee

Sarvi

2018

PLoS ONE

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Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.

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A hybrid meta-heuristic algorithm for solving real-life transportation network design problems

Cited by 11 publications

References 29 publications

Multitype Recharge Facility Location for Electric Vehicles

Multitype Recharge Facility Location for Electric Vehicles

A parsimonious heuristic for the discrete network design problem

An outer approximation method for the road network design problem

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