The Multistage Stochastic Integer Load Planning Problem
Time-definite freight delivery common carriers, who pickup, consolidate and deliver small shipments are a key third-party logistics service provider in the supply chains. Under uncertain demands, carrier's multistage stochastic integer load planning in the pure hub-and-spoke line-haul operations network is to determine multistage freight paths and distribute trailers over time while meeting operational restrictions, service requirements, and balancing trailer inventory at the termination of planning horizon. The authors developed a heuristic approach, scenario aggregation with embedded branching on the binary variables. The numerical experiments showed a proactive operations strategy with a lower operating cost than the conventionally deterministic approach.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
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Supplemental Notes:
- Abstract reprinted with permission from Elsevier
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Authors:
- Lin, Cheng-Chang
- Lin, Jeffery Sung-Jan
- Publication Date: 2007-3
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 143-156
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Serial:
- Transportation Research Part E: Logistics and Transportation Review
- Volume: 43
- Issue Number: 2
- Publisher: Elsevier
- ISSN: 1366-5545
- Serial URL: http://www.sciencedirect.com/science/journal/13665545
Subject/Index Terms
- TRT Terms: Forecasting; Freight transportation; Heuristic methods; Hub and spoke systems; Integer programming; Inventory; Line haul; Mathematical models; Numerical analysis; Operating costs; Pickup and delivery service; Planning methods; Route choice; Small shipments; Stochastic programming; Strategic planning; Supply chain management; Third party logistics providers; Time windows; Trailers; Variables
- Subject Areas: Finance; Freight Transportation; Highways; I10: Economics and Administration;
Filing Info
- Accession Number: 01049957
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2007 1:28PM