A Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Stochastic Demand

Cheong, C; Tan, K; Liu, D; Xu, J

A Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Stochastic Demand

Cheong, C Tan, K Liu, D

Xu, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2006 IEEE Congress on Evolutionary Computation, 2006, pp. 5519 - 5526
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Cheong, C	en_US
dc.contributor.author	Tan, K	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Xu, J	en_US
dc.contributor.editor	Yen, GG	en_US
dc.date	2006-07-16	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Proceedings of 2006 IEEE Congress on Evolutionary Computation, 2006, pp. 5519 - 5526	en_US
dc.identifier.isbn	0-7803-9487-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2496
dc.description.abstract	This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as vehicle time window and capacity. To solve such a multiobjective combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A novel way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions which are robust to the stochastic nature of the problem.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of 2006 IEEE Congress on Evolutionary Computation	en_US
dc.relation.ispartof	IEEE Congress on Evolutionary Computation	en_US
dc.relation.isbasedon	10.1109/CEC.2006.1688468	en_US
dc.rights	© 2006 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.	en_US
dc.title	A Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Stochastic Demand	en_US
dc.type	Conference Proceeding
utslib.location	Vancouver, Canada	en_US
utslib.location.activity	Vancouver, Canada	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Vancouver, Canada	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Vancouver, Canada	en_US
pubs.start-date	2006-07-16	en_US

Abstract:

This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as vehicle time window and capacity. To solve such a multiobjective combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A novel way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions which are robust to the stochastic nature of the problem.

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http://hdl.handle.net/10453/2496