A spreadsheet decision support optimization model for railcar storage at Canadian Pacific Railway

Canadian railway companies operate in a capital-intensive segment of the transportation industry. In most railway companies, the covered hopper railcar fleet is one of the larger fleets due to its use in moving grain and potash, commodities that move large volumes of product. This railcar fleet is a...

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Bibliographic Details
Published in:The Journal of the Operational Research Society 2012-02, Vol.63 (2), p.139-150
Main Authors: Espey, R L, Balakrishnan, J
Format: Article
Language:English
Subjects:
aggregate planning
Applied sciences
Automobile leases
Business and Management
Business structures
Cost efficiency
Costs
Customer satisfaction
Decision making
Decision support systems
Decision theory. Utility theory
Emission standards
Exact sciences and technology
Fleet management
General Paper
General Papers
Ground, air and sea transportation, marine construction
Inventory control, production control. Distribution
Lessors
Management
Market positioning
Mathematical programming
Modeling
Operational research and scientific management
Operational research. Management science
Operations research
Operations Research/Decision Theory
Optimization
Order processing
Planning
Potash
Rail industry
Railroads
Railway transportation and traffic
railways
spreadsheet optimization
Spreadsheets
storage
Studies
transport
Vehicle fleets
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Summary:Canadian railway companies operate in a capital-intensive segment of the transportation industry. In most railway companies, the covered hopper railcar fleet is one of the larger fleets due to its use in moving grain and potash, commodities that move large volumes of product. This railcar fleet is also difficult to manage due to demand seasonality and joint commodity use. This paper demonstrates how an aggregate planning model can be used to support decision making related to optimization of covered hopper railcar storage. Exploratory research prior to model development involved interviews with company personnel. The model was developed through quantitative research and implemented using spreadsheet optimization. The results indicate that using this model can reduce the total cost of storage through effective planning. The model also provided insight to improve railcar storage such as the elimination of excess storage locations and the need to do further investigation. The company is in the process of implementing suggestions from this paper.
ISSN:0160-5682
1476-9360
DOI:10.1057/jors.2010.178