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

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 t...

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Bibliographic Details
Published in:2006 IEEE International Conference on Evolutionary Computation pp. 1370 - 1377
Main Authors: Cheong, C.Y., Tan, K.C., Liu, D.K., Xu, J.X.
Format: Conference Proceeding
Language:English
Published: IEEE 2006
Subjects:
Constraint optimization
Costs
Evolutionary computation
Optimization methods
Robustness
Routing
Stochastic processes
Time factors
Transportation
Vehicle driving
ISBN:9780780394872, 0780394879
ISSN:1089-778X
Online Access:Get full text
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Summary: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.
ISBN:9780780394872
0780394879
ISSN:1089-778X
DOI:10.1109/CEC.2006.1688468