A general variable neighbourhood search for the commodity constrained split delivery vehicle routing problem
The commodity constrained split delivery vehicle routing problem (C-SDVRP) is a relaxed version of the classical VRP. This problem emanates where customers request several commodities that can be delivered separately using a set of vehicles. With limited capacity, these vehicles can convey any mixed commodities set. More than one vehicle may visit one customer, and in each visit, each product must be delivered in its entirety. The objective is to minimise the total cost of the vehicle routes. To solve the C-SDVRP, we propose a general variable neighbourhood search (GVNS) that uses a random variable neighbourhood descent (RVND), in which we explore five neighbourhood structures. Computational experiments on large-sized instances show that the proposed approach finds several new best-known solutions, together with some improvement on the number of used vehicles. The results demonstrate that GVNS has a fast convergence rate and high computational accuracy.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17427967
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Supplemental Notes:
- Copyright © 2023 Inderscience Enterprises Ltd.
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Authors:
- Cheikh, Mohamed
- Loukil, Taicir Moalla
- Publication Date: 2023
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 249-267
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Serial:
- International Journal of Logistics Systems and Management
- Volume: 45
- Issue Number: 2
- Publisher: Inderscience Enterprises Limited
- ISSN: 1742-7967
- EISSN: 1742-7945
- Serial URL: http://www.inderscience.com/jhome.php?jcode=ijlsm
Subject/Index Terms
- TRT Terms: Commodity flow; Cost effectiveness; Delivery service; Fleet management; Neighborhoods; Routes and routing
- Identifier Terms: Vehicle Routing Problem
- Subject Areas: Finance; Freight Transportation; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01897300
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 24 2023 9:35AM