Dimensionality Reduction Approach for Many-Objective Vehicle Routing Problem with Demand Responsive Transport

Mendes, Renan Santos; Wanner, Elizabeth F.; Martins, Flávio Vinícius Cruzeiro; Sarubbi, Joao F. M.

doi:10.1007/978-3-319-54157-0_30

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods in this category are reported in previous surveys (B. Li et al, 2015;von Lücken et al, 2014). Recently, clustering techniques were used with objective number reduction to aid many-objective optimization (Mendes, Wanner, Martins, & Sarubbi, 2017;Pal, Saha, & Bandyopadhyay, 2016;von Lücken, Monzón, Brizuela, & Barán, 2015) showing improved results in several practical and benchmark problems. The machine learning community proposed several dimensionality reduction methods (Cunningham & Ghahramani, 2015), thus, collaborative efforts with this community to ameliorate evolutionary methods based on this approach are encouraged.…”

Section: Many-objective Evolutionary Methodsmentioning

confidence: 99%

An overview on evolutionary algorithms for many‐objective optimization problems

Lücken

Brizuela

Barán

2018

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Multiobjective evolutionary algorithms (MOEAs) effectively solve several complex optimization problems with two or three objectives. However, when they are applied to many‐objective optimization, that is, when more than three criteria are simultaneously considered, the performance of most MOEAs is severely affected. Several alternatives have been reported to reproduce the same performance level that MOEAs have achieved in problems with up to three objectives when considering problems with higher dimensions. This work briefly reviews the main search difficulties, visualization, evaluation of algorithms, and new procedures in many‐objective optimization using evolutionary methods. Approaches for the development of evolutionary many‐objective algorithms are classified into: (a) based on preference relations, (b) aggregation‐based, (c) decomposition‐based, (d) indicator‐based, and (e) based on dimensionality reduction. The analysis of the reviewed works indicates the promising future of such methods, especially decomposition‐based approaches; however, much still need to be done to develop more robust, faster, and predictable evolutionary many‐objective algorithms. This article is categorized under: Technologies > Computational Intelligence

show abstract

Section: Many-objective Evolutionary Methodsmentioning

confidence: 99%

An overview on evolutionary algorithms for many‐objective optimization problems

Lücken

Brizuela

Barán

2018

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…The proposed DRT feeder system in the current study performs as a complementary transit system with a rail system. A feeder system should follow routes and timetables to match demand requests to maximize demand coverage while improving the quality of service by maximizing fleet efficiency [6]. However, the perspectives of passengers and operators using a DRT system are different.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

Nickkar,

Lee

2023

Algorithms

View full text Add to dashboard Cite

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.

show abstract

An Efficient Hybrid Evolutionary Algorithm for the Smart Vehicle Routing Problem

Ben-Romdhane

Krichen

2020

Smart Technologies for Smart Cities

View full text Add to dashboard Cite

Dimensionality Reduction Approach for Many-Objective Vehicle Routing Problem with Demand Responsive Transport

Cited by 5 publications

References 16 publications

An overview on evolutionary algorithms for many‐objective optimization problems

An overview on evolutionary algorithms for many‐objective optimization problems

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

An Efficient Hybrid Evolutionary Algorithm for the Smart Vehicle Routing Problem

Contact Info

Product

Resources

About