Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation
This paper proposes an ant colony optimization (ACO) based algorithm to minimise the fleet size required to solve dial-a-ride problem (DARP). In this work, a static multi-vehicle case of DARP is considered where routes of multiple vehicles are designed to serve customer requests which are known a pr...
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sg-ntu-dr.10356-1403302020-05-28T03:35:42Z Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation Tripathy, Twinkle Nagavarapu, Sarat Chandra Azizian, Kaveh Pandi, Ramesh Ramasamy Dauwels, Justin School of Electrical and Electronic Engineering 17th UK Workshop on Computational Intelligence (UKCI 2017) Engineering::Electrical and electronic engineering Dial-a-ride Problem Fleet Size Minimisation This paper proposes an ant colony optimization (ACO) based algorithm to minimise the fleet size required to solve dial-a-ride problem (DARP). In this work, a static multi-vehicle case of DARP is considered where routes of multiple vehicles are designed to serve customer requests which are known a priori. DARP necessitates the need of high quality algorithm to provide optimal feasible solutions. We employ an improved ACO algorithm called ant colony system (ACS) to solve DARP. The fleet minimisation is also achieved by using ACS. In summary, multiple ACS are employed to minimise the fleet size while generating feasible solutions for DARP. Furthermore, the theoretical results are also validated through simulations. 2020-05-28T03:35:41Z 2020-05-28T03:35:41Z 2017 Conference Paper Tripathy, T., Nagavarapu, S. C., Azizian, K., Pandi, R. R., & Dauwels, J. (2018). Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation. Proceedings of 17th UK Workshop on Computational Intelligence, 325-336. doi:10.1007/978-3-319-66939-7_28 9783319669380 https://hdl.handle.net/10356/140330 10.1007/978-3-319-66939-7_28 2-s2.0-85029587102 325 336 en © 2018 Springer International Publishing AG. All rights reserved. |
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Engineering::Electrical and electronic engineering Dial-a-ride Problem Fleet Size Minimisation Tripathy, Twinkle Nagavarapu, Sarat Chandra Azizian, Kaveh Pandi, Ramesh Ramasamy Dauwels, Justin Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
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This paper proposes an ant colony optimization (ACO) based algorithm to minimise the fleet size required to solve dial-a-ride problem (DARP). In this work, a static multi-vehicle case of DARP is considered where routes of multiple vehicles are designed to serve customer requests which are known a priori. DARP necessitates the need of high quality algorithm to provide optimal feasible solutions. We employ an improved ACO algorithm called ant colony system (ACS) to solve DARP. The fleet minimisation is also achieved by using ACS. In summary, multiple ACS are employed to minimise the fleet size while generating feasible solutions for DARP. Furthermore, the theoretical results are also validated through simulations. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Tripathy, Twinkle Nagavarapu, Sarat Chandra Azizian, Kaveh Pandi, Ramesh Ramasamy Dauwels, Justin |
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Conference or Workshop Item |
author |
Tripathy, Twinkle Nagavarapu, Sarat Chandra Azizian, Kaveh Pandi, Ramesh Ramasamy Dauwels, Justin |
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Tripathy, Twinkle |
title |
Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
title_short |
Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
title_full |
Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
title_fullStr |
Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
title_full_unstemmed |
Solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
title_sort |
solving dial-a-ride problems using multiple ant colony system with fleet size minimisation |
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2020 |
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https://hdl.handle.net/10356/140330 |
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1681058862744993792 |