期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 196, 期 2, 页码 509-515出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2008.03.023
关键词
Transportation; Stochastic vehicle routing; Approximate dynamic programming
This paper examines approximate dynamic programming algorithms for the single-vehicle routing problem with stochastic demands from a dynamic or reoptimization perspective. The methods extend the rollout algorithm by implementing different base sequences (i.e. a priori solutions), look-ahead policies, and pruning schemes. The paper also considers computing the cost-to-go with Monte Carlo simulation in addition to direct approaches. The best new method found is a two-step lookahead rollout started with a stochastic base sequence. The routing cost is about 4.8% less than the one-step rollout algorithm started with a deterministic sequence. Results also show that Monte Carlo cost-to-go estimation reduces computation time 65% in large instances with little or no loss in solution quality. Moreover, the paper compares results to the perfect information case from solving exact a posteriori solutions for sampled vehicle routing problems. The confidence interval for the overall mean difference is (3.56%, 4.11%). (C) 2008 Elsevier B.V. All rights reserved.
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