Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144433
Title: Deterministic annealing for depot optimization : applications to the dial-a-ride problem
Authors: Pandi, Ramesh Ramasamy
Ho, Song Guang
Nagavarapu, Sarat Chandra
Dauwels, Justin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Pandi, R. R., Ho, S. G., Nagavarapu, S. C., & Dauwels, J. (2018). Deterministic annealing for depot optimization : applications to the dial-a-ride problem. Proceedings of 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 88-95. doi:10.1109/SSCI.2018.8628642
Project: CRP-2
Abstract: This paper introduces a novel meta-heuristic approach to optimize depot locations for multi-vehicle shared mobility systems. Dial-a-ride problem (DARP) is considered as a case study here, in which routing and scheduling for door to- door passenger transportation are performed while satisfying several constraints related to user convenience. Existing literature has not addressed the fundamental problem of depot location optimization for DARP, which can reduce cost, and in turn promote the use of shared mobility services to minimize carbon footprint. Thus, there is a great need for fleet management systems to employ a multi-depot vehicle dispatch mechanism with intrinsic depot location optimization. In this work, we propose a deterministic annealing meta-heuristic to optimize depot locations for the dial-a-ride problem. Numerical experiments are conducted on several DARP benchmark instances from the literature, which can be categorized as small, medium and large based on their problem size. For all tested instances, the proposed algorithm attains solutions with travel cost better than that of the best-known solutions. It is also observed that the travel cost is reduced up to 6.13% when compared to the best-known solutions.
URI: https://hdl.handle.net/10356/144433
ISBN: 978-1-5386-9276-9
DOI: 10.1109/SSCI.2018.8628642
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/SSCI.2018.8628642
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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