A global repair operator for capacitated arc routing problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Memetic algorithm with extended neighborhood search for capacitated arc routing problems
IEEE Transactions on Evolutionary Computation
An evolutionary approach to the multidepot capacitated arc routing problem
IEEE Transactions on Evolutionary Computation
Particle swarm optimization with composite particles in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A developmental solution to (dynamic) capacitated arc routing problems using genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
One-way urban traffic reconfiguration using a multi-objective harmony search approach
Expert Systems with Applications: An International Journal
Challenges and opportunities in dynamic optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Highway authorities in marginal winter climates are responsible for the precautionary gritting/salting of the road network in order to prevent frozen roads. For efficient and effective road maintenance, accurate road surface temperature prediction is required. However, this information is useless if an effective means of utilizing this information is unavailable. This is where gritting route optimization plays a crucial role. The decision whether to grit the road network at marginal nights is a difficult problem. The consequences of making a wrong decision are serious, as untreated roads are a major hazard. However, if grit/salt is spread when it is not actually required, there are unnecessary financial and environmental costs. The goal here is to minimize the financial and environmental costs while ensuring roads that need treatment will. In this article, a salting route optimization (SRO) system that combines evolutionary algorithms with the neXt generation Road Weather Information System (XRWIS) is introduced. The synergy of these methodologies means that salting route optimization can be done at a level previously not possible.