Case study: solving a rental fleet sizing model with a large time-space network.: An article from: Engineering Economist
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Book Details
Author(s)
Peiling Wu, Joseph C. Hartman
ISBN / ASIN
B003F1P48O
ISBN-13
978B003F1P480
Marketplace
France 🇫🇷
Description
This digital document is an article from Engineering Economist, published by Institute of Industrial Engineers, Inc. (IIE) on January 1, 2010. The length of the article is 8567 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.
From the author: This article addresses a rental fleet sizing problem (RFS) in the context of the truck rental industry, subject to uncertain customer travel time and nonstationary customer demand that is dependent on geographical location, time, and the economic cycle of the industry. We integrate tactical (asset purchases and sales) and operational (empty truck movement and vehicle assignment) decisions, with the explicit incorporation of an asset age factor, to achieve lower cost solutions. Typically, the length of time horizon and number of locations under consideration are quite large, which makes the RFS model computationally challenging to solve. Aggregation procedures are employed for location clustering and end-of-horizon effects are examined through demand scenario-based analyses. For the reduced time-space networks, decision analyses are conducted for the RFS model to provide insights into the truck rental business regarding asset movement decisions and asset procurement/disposal decisions over time and locations.
Citation Details
Title: Case study: solving a rental fleet sizing model with a large time-space network.
Author: Peiling Wu
Publication:Engineering Economist (Magazine/Journal)
Date: January 1, 2010
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: 55 Issue: 1 Page: 71(34)
Distributed by Gale, a part of Cengage Learning
From the author: This article addresses a rental fleet sizing problem (RFS) in the context of the truck rental industry, subject to uncertain customer travel time and nonstationary customer demand that is dependent on geographical location, time, and the economic cycle of the industry. We integrate tactical (asset purchases and sales) and operational (empty truck movement and vehicle assignment) decisions, with the explicit incorporation of an asset age factor, to achieve lower cost solutions. Typically, the length of time horizon and number of locations under consideration are quite large, which makes the RFS model computationally challenging to solve. Aggregation procedures are employed for location clustering and end-of-horizon effects are examined through demand scenario-based analyses. For the reduced time-space networks, decision analyses are conducted for the RFS model to provide insights into the truck rental business regarding asset movement decisions and asset procurement/disposal decisions over time and locations.
Citation Details
Title: Case study: solving a rental fleet sizing model with a large time-space network.
Author: Peiling Wu
Publication:Engineering Economist (Magazine/Journal)
Date: January 1, 2010
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: 55 Issue: 1 Page: 71(34)
Distributed by Gale, a part of Cengage Learning
