Improved ant colony optimization for solving dial-a-ride-problem

The dial-a-ride problem (DARP) is a combinatorial optimization problem in which passengers claim requests in the form of their departure location, destination and the specific time windows during which they must be picked up and dropped off. A certain number of vehicles are assigned to serve these r...

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Main Author: Peng, Guohao
Other Authors: Keveh Azizan
Format: Theses and Dissertations
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/73130
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-731302023-07-04T15:05:51Z Improved ant colony optimization for solving dial-a-ride-problem Peng, Guohao Keveh Azizan Justin Dauwels School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The dial-a-ride problem (DARP) is a combinatorial optimization problem in which passengers claim requests in the form of their departure location, destination and the specific time windows during which they must be picked up and dropped off. A certain number of vehicles are assigned to serve these requests while ensuring that the maximum capacities of vehicles are not exceeded and the maximum ride time constraints of passengers, if any, are not violated. In this thesis, an improved ant colony optimization (IACO) algorithm is proposed to address the dial-a-ride-problem. The proposed algorithm works by pre-processing the requests to eliminate the ones that are infeasible from the beginning. In order to select the requests, the algorithm considers factors like time windows, local heuristics and vehicle load. Also, an adjust function is introduced to improve the quality of solutions and chaotic perturbation is used to prevent premature convergence. Numerous simulations have been carried out to demonstrate the efficiency of the proposed algorithm. Master of Science (Computer Control and Automation) 2018-01-03T07:10:47Z 2018-01-03T07:10:47Z 2018 Thesis http://hdl.handle.net/10356/73130 en 85 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Peng, Guohao
Improved ant colony optimization for solving dial-a-ride-problem
description The dial-a-ride problem (DARP) is a combinatorial optimization problem in which passengers claim requests in the form of their departure location, destination and the specific time windows during which they must be picked up and dropped off. A certain number of vehicles are assigned to serve these requests while ensuring that the maximum capacities of vehicles are not exceeded and the maximum ride time constraints of passengers, if any, are not violated. In this thesis, an improved ant colony optimization (IACO) algorithm is proposed to address the dial-a-ride-problem. The proposed algorithm works by pre-processing the requests to eliminate the ones that are infeasible from the beginning. In order to select the requests, the algorithm considers factors like time windows, local heuristics and vehicle load. Also, an adjust function is introduced to improve the quality of solutions and chaotic perturbation is used to prevent premature convergence. Numerous simulations have been carried out to demonstrate the efficiency of the proposed algorithm.
author2 Keveh Azizan
author_facet Keveh Azizan
Peng, Guohao
format Theses and Dissertations
author Peng, Guohao
author_sort Peng, Guohao
title Improved ant colony optimization for solving dial-a-ride-problem
title_short Improved ant colony optimization for solving dial-a-ride-problem
title_full Improved ant colony optimization for solving dial-a-ride-problem
title_fullStr Improved ant colony optimization for solving dial-a-ride-problem
title_full_unstemmed Improved ant colony optimization for solving dial-a-ride-problem
title_sort improved ant colony optimization for solving dial-a-ride-problem
publishDate 2018
url http://hdl.handle.net/10356/73130
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