A Genetic Algorithm for the Capacitated Arc Routing Problem and Its Extensions
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
SSJ: SSJ: a framework for stochastic simulation in Java
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Arc-Routing Models for Small-Package Local Routing
Transportation Science
A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands
Operations Research Letters
A Branch-and-Price Algorithm for the Capacitated Arc Routing Problem with Stochastic Demands
Operations Research Letters
Development and assessment of the SHARP and RandSHARP algorithms for the arc routing problem
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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This paper proposes a new hybrid algorithm for solving the Arc Routing Problem with Stochastic Demands (ARPSD). Our approach combines Monte Carlo simulation (MCS) with the RandSHARP algorithm, which is designed for solving the Capacitated Arc Routing Problem (CARP) with deterministic demands. The RandSHARP algorithm makes use of a CARP-adapted version of the Clarke and Wright Savings heuristic, which was originally designed for the Vehicle Routing Problem. The RandSHARP algorithm also integrates a biased-randomized process, which allows it to obtain competitive results for the CARP in low computational times. The RandSHARP algorithm is then combined with MCS to solve the ARPSD. Some numerical experiments contribute to illustrate the potential benefits of our approach.