A space biased-sampling approach for the vehicle routing problem with stochastic demands
Abstract
The vehicle routing problem with stochastic demands consists of designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distribution. We propose a metaheuristic that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set-partitioning formulation to sample the solution space and find solutions for the problem. Computational experiments show that our approach is competitive with state-of-the-art algorithms for the problem in terms of both accuracy and efficiency.
Domains
Operations Research [math.OC]
Origin : Files produced by the author(s)
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