Journal ArticleDOI
An approximate dynamic programming approach for the vehicle routing problem with stochastic demands
Clara Novoa,Robert H. Storer +1 more
Reads0
Chats0
TLDR
Results show that Monte Carlo cost-to-go estimation reduces computation time 65% in large instances with little or no loss in solution quality, and compares results to the perfect information case from solving exact a posteriori solutions for sampled vehicle routing problems.About:
This article is published in European Journal of Operational Research.The article was published on 2009-07-16. It has received 271 citations till now. The article focuses on the topics: Vehicle routing problem & Routing (electronic design automation).read more
Citations
More filters
Journal ArticleDOI
A review of dynamic vehicle routing problems
TL;DR: This survey classifies routing problems from the perspective of information quality and evolution and presents a comprehensive review of applications and solution methods for dynamic vehicle routing problems.
Journal ArticleDOI
Big data analytics in logistics and supply chain management: Certain investigations for research and applications
TL;DR: In this article, the authors classify the literature on the application of big data business analytics (BDBA) on logistics and supply chain management (LSCM) based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations).
Journal ArticleDOI
The vehicle routing problem
TL;DR: This classification is the first to categorize the articles of the VRP literature to this level of detail and is based on an adapted version of an existing comprehensive taxonomy.
Journal ArticleDOI
Dynamic vehicle routing problems: Three decades and counting
TL;DR: A taxonomy of DVRP papers is developed according to 11 criteria by developing a comment on technological vis-i-vis methodological advances for this class of problems and suggest directions for further research.
References
More filters
Book
Dynamic Programming and Optimal Control
TL;DR: The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.
Book
Integer and Combinatorial Optimization
TL;DR: This chapter discusses the Scope of Integer and Combinatorial Optimization, as well as applications of Special-Purpose Algorithms and Matching.
Neuro-Dynamic Programming.
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Book
Neuro-dynamic programming
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.