Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation

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...

Full description

Saved in:
Bibliographic Details
Published in:European journal of operational research 2007-03, Vol.177 (2), p.813-839
Main Authors: Tan, K.C., Cheong, C.Y., Goh, C.K.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Evolutionary algorithms
Exact sciences and technology
Flows in networks. Combinatorial problems
Heuristic
Logistics
Multiobjective optimization
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Routing
Simulation
Stochastic
Stochastic models
Studies
Vehicle routing problems
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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 time windows and vehicle capacity. To solve such a multiobjective and multi-modal 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 new 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 for the VRPSD and the solutions are robust to the stochastic nature of the problem. The developed algorithm is further validated on a few VRPSD instances adapted from Solomon’s vehicle routing problem with time windows (VRPTW) benchmark problems.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2005.12.029