A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem

Mehdi Abedi; Raymond Chiong; Rukshan Athauda; Hany Seidgar; Zbigniew Michalewicz; Andrew Sturt

doi:10.1109/CEC.2019.8790003

A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem

Mehdi Abedi
, Raymond Chiong
, Rukshan Athauda
, Hany Seidgar
, Zbigniew Michalewicz
, Andrew Sturt

School of Science & Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO ₂ emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker's preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.

Original language	English
Title of host publication	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Place of Publication	United States of America
Publisher	IEEE
Pages	2082-2089
ISBN (Print)	9781728121536, 9781728121529
DOIs	https://doi.org/10.1109/CEC.2019.8790003
Publication status	Published - 2019
Event	2019 IEEE CEC: Congress on Evolutionary Computation (CEC) Conference - Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019

Conference

Conference	2019 IEEE CEC: Congress on Evolutionary Computation (CEC) Conference
Country/Territory	New Zealand
City	Wellington
Period	10/06/19 → 13/06/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/CEC.2019.8790003

https://hdl.handle.net/1959.11/61419

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@inproceedings{11929ac57f034894a3bebd52e7bac412,

title = "A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem",

abstract = "Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO 2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker's preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.",

author = "Mehdi Abedi and Raymond Chiong and Rukshan Athauda and Hany Seidgar and Zbigniew Michalewicz and Andrew Sturt",

year = "2019",

doi = "10.1109/CEC.2019.8790003",

language = "English",

isbn = "9781728121536",

pages = "2082--2089",

booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings",

publisher = "IEEE",

note = "2019 IEEE CEC: Congress on Evolutionary Computation (CEC) Conference ; Conference date: 10-06-2019 Through 13-06-2019",

}

Abedi, M, Chiong, R, Athauda, R, Seidgar, H, Michalewicz, Z & Sturt, A 2019, A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. IEEE, United States of America, pp. 2082-2089, 2019 IEEE CEC: Congress on Evolutionary Computation (CEC) Conference, Wellington, New Zealand, 10/06/19. https://doi.org/10.1109/CEC.2019.8790003

A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem. / Abedi, Mehdi; Chiong, Raymond; Athauda, Rukshan et al.
2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. United States of America: IEEE, 2019. p. 2082-2089.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem

AU - Abedi, Mehdi

AU - Chiong, Raymond

AU - Athauda, Rukshan

AU - Seidgar, Hany

AU - Michalewicz, Zbigniew

AU - Sturt, Andrew

PY - 2019

Y1 - 2019

N2 - Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO 2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker's preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.

AB - Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO 2 emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker's preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.

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BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

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ER -

A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial-A-Ride Problem

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