An approximate dynamic programming approach for the vehicle routing problem with stochastic demands

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...

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Bibliographic Details
Published in:European journal of operational research Vol. 196; no. 2; pp. 509 - 515
Main Authors: Novoa, Clara, Storer, Robert
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.07.2009
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Approximate dynamic programming
Dynamic programming
Exact sciences and technology
Logistics
Monte Carlo simulation
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Routing
Stochastic models
Stochastic vehicle routing
Studies
Transportation
Transportation Stochastic vehicle routing Approximate dynamic programming
Stochastic vehicle routing
Approximate dynamic programming
Transportation
Monte Carlo method
Filtering
Vehicle routing problem
Routing
Modeling
Confidence interval
Exact solution
Computation time
Pruning(tree)
Cost estimation
Dynamic programming
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary: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%).
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2008.03.023