A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows
A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows
This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the Fleet Size and Mix Vehicle Routing Problem with Time Windows (F) and the Heterogeneous Fixed Fleet Vehicle Routing Problem with Time Windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented.
vehicle routing, time windows, heterogeneous fleet, genetic algorithm, neighbourhood search
11-27
Koc, C.
0580305f-af8c-49fa-b6a8-832f951c9e85
Bektas, T.
0db10084-e51c-41e5-a3c6-417e0d08dac9
Jabali, O.
7a91105c-3ff6-4a2c-bb86-0b5739af4faa
Laporte, G.
2cd560e2-79a4-4ee7-b883-ec02bc880328
1 December 2015
Koc, C.
0580305f-af8c-49fa-b6a8-832f951c9e85
Bektas, T.
0db10084-e51c-41e5-a3c6-417e0d08dac9
Jabali, O.
7a91105c-3ff6-4a2c-bb86-0b5739af4faa
Laporte, G.
2cd560e2-79a4-4ee7-b883-ec02bc880328
Koc, C., Bektas, T., Jabali, O. and Laporte, G.
(2015)
A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows.
Computers & Operations Research, 64, .
(doi:10.1016/j.cor.2015.05.004).
Abstract
This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the Fleet Size and Mix Vehicle Routing Problem with Time Windows (F) and the Heterogeneous Fixed Fleet Vehicle Routing Problem with Time Windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented.
Text
HVRPTW.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 8 May 2015
e-pub ahead of print date: 18 May 2015
Published date: 1 December 2015
Keywords:
vehicle routing, time windows, heterogeneous fleet, genetic algorithm, neighbourhood search
Organisations:
Centre of Excellence in Decision, Analytics & Risk Research
Identifiers
Local EPrints ID: 376980
URI: http://eprints.soton.ac.uk/id/eprint/376980
ISSN: 0305-0548
PURE UUID: 6f40cec2-fd86-41a1-bb82-8ed002978323
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Date deposited: 18 May 2015 12:19
Last modified: 14 Mar 2024 19:54
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Contributors
Author:
C. Koc
Author:
T. Bektas
Author:
O. Jabali
Author:
G. Laporte
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