An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application

We addressed the problem of developing a model to simulate at a high level of detail the movements of over 6,000 drivers for Schneider National, the largest truckload motor carrier in the United States. The goal of the model was not to obtain a better solution but rather to closely match a number of...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Transportation science Ročník 43; číslo 2; s. 178 - 197
Hlavní autoři: Simao, Hugo P, Day, Jeff, George, Abraham P, Gifford, Ted, Nienow, John, Powell, Warren B
Médium: Journal Article
Jazyk:angličtina
Vydáno: Baltimore INFORMS 01.05.2009
Transportation Science & Logistic Society of the Institute for Operations Research and Management Sciences
Institute for Operations Research and the Management Sciences
Témata:
Aggregation
Algorithms
approximate dynamic programming
Approximation
driver management
Drivers
Dynamic programming
Estimates
Fleet management
Freight
Load
Logistics
Machine learning
Marginal value
Modeling
Motor carriers
Motor vehicle fleets
Objective functions
Optimization
Programming
Road transport
Simulation
Simulation training
Statistics
Studies
Transport economics
Transportation
Trucking
truckload trucking
Trucks
U.S.A
United States > US
ISSN:0041-1655, 1526-5447
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:We addressed the problem of developing a model to simulate at a high level of detail the movements of over 6,000 drivers for Schneider National, the largest truckload motor carrier in the United States. The goal of the model was not to obtain a better solution but rather to closely match a number of operational statistics. In addition to the need to capture a wide range of operational issues, the model had to match the performance of a highly skilled group of dispatchers while also returning the marginal value of drivers domiciled at different locations. These requirements dictated that it was not enough to optimize at each point in time (something that could be easily handled by a simulation model) but also over time. The project required bringing together years of research in approximate dynamic programming, merging math programming with machine learning, to solve dynamic programs with extremely high-dimensional state variables. The result was a model that closely calibrated against real-world operations and produced accurate estimates of the marginal value of 300 different types of drivers.
Bibliografie:ObjectType-Case Study-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Report-1
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:0041-1655
1526-5447
DOI:10.1287/trsc.1080.0238