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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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| Published in: | The Journal of the Operational Research Society 2012-02, Vol.63 (2), p.139-150 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c502t-5fddd8f206552e8255b87a6bfbaddaa548a8b0c554dc8fc7f71297b194a8d7d93 |
| container_end_page | 150 |
| container_issue | 2 |
| container_start_page | 139 |
| container_title | The Journal of the Operational Research Society |
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| creator | Espey, R L Balakrishnan, J |
| description | 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. |
| doi_str_mv | 10.1057/jors.2010.178 |
| format | article |
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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.</description><identifier>ISSN: 0160-5682</identifier><identifier>EISSN: 1476-9360</identifier><identifier>DOI: 10.1057/jors.2010.178</identifier><identifier>CODEN: JORSDZ</identifier><language>eng</language><publisher>London: Taylor & Francis</publisher><subject>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</subject><ispartof>The Journal of the Operational Research Society, 2012-02, Vol.63 (2), p.139-150</ispartof><rights>Copyright © 2011, Operational Research Society 2011</rights><rights>2012 Operational Research Society Ltd</rights><rights>Operational Research Society 2011</rights><rights>2015 INIST-CNRS</rights><rights>Operational Research Society 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-5fddd8f206552e8255b87a6bfbaddaa548a8b0c554dc8fc7f71297b194a8d7d93</citedby><cites>FETCH-LOGICAL-c502t-5fddd8f206552e8255b87a6bfbaddaa548a8b0c554dc8fc7f71297b194a8d7d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/915009577/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/915009577?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,11667,27901,27902,36037,44339,58213,58446,74638</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25533186$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/paljorsoc/v_3a63_3ay_3a2012_3ai_3a2_3ap_3a139-150.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Espey, R L</creatorcontrib><creatorcontrib>Balakrishnan, J</creatorcontrib><title>A spreadsheet decision support optimization model for railcar storage at Canadian Pacific Railway</title><title>The Journal of the Operational Research Society</title><addtitle>J Oper Res Soc</addtitle><description>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.</description><subject>aggregate planning</subject><subject>Applied sciences</subject><subject>Automobile leases</subject><subject>Business and Management</subject><subject>Business structures</subject><subject>Cost efficiency</subject><subject>Costs</subject><subject>Customer satisfaction</subject><subject>Decision making</subject><subject>Decision support systems</subject><subject>Decision theory. Utility theory</subject><subject>Emission standards</subject><subject>Exact sciences and technology</subject><subject>Fleet management</subject><subject>General Paper</subject><subject>General Papers</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Inventory control, production control. Distribution</subject><subject>Lessors</subject><subject>Management</subject><subject>Market positioning</subject><subject>Mathematical programming</subject><subject>Modeling</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Operations research</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization</subject><subject>Order processing</subject><subject>Planning</subject><subject>Potash</subject><subject>Rail industry</subject><subject>Railroads</subject><subject>Railway transportation and traffic</subject><subject>railways</subject><subject>spreadsheet optimization</subject><subject>Spreadsheets</subject><subject>storage</subject><subject>Studies</subject><subject>transport</subject><subject>Vehicle fleets</subject><issn>0160-5682</issn><issn>1476-9360</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kU1v1DAQhiMEEkvhyBHJQuKYxY7j2DlWK6BFlUAIztbEH61X2TjY3lbLr2eilOUCh_Hn49cz71TVa0a3jAr5fh9T3jZ02Ur1pNqwVnZ1zzv6tNpQ1tFadKp5Xr3IeU8p7SnrNxVckjwnBzbfOVeIdSbkECeSj_McUyFxLuEQfkFZDg_RupH4mEiCMBpIJJeY4NYRKGQHE9gAE_kKJvhgyDdkHuD0snrmYczu1eN8Uf34-OH77qq--fLpend5UxtBm1ILb61VvqGdEI1TjRCDktANfgBrAUSrQA3UCNFao7yRXrKmlwPrW1BW2p5fVG9X3TnFn0eXi97HY5rwS90zgfUKKRGqV8ikmHNyXs8pHCCdNKN6MVEvJurFRI0mIv955ZObnTnDM4wLF42-1xw6jsMJA581OIVliTFjMN5r_F3flQOKvXvMELKB0SeY0O2zKJbMOVMdctuVw86E6dalv5X8L8s364P90o-zYMu44D1bqlbrfZiweQd4iGm0usBpjOlPFvzf0r8BJ2K5nQ</recordid><startdate>20120201</startdate><enddate>20120201</enddate><creator>Espey, R L</creator><creator>Balakrishnan, J</creator><general>Taylor & Francis</general><general>Palgrave Macmillan</general><general>Palgrave Macmillan UK</general><general>Taylor & Francis Ltd</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7RQ</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>K9.</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>U9A</scope></search><sort><creationdate>20120201</creationdate><title>A spreadsheet decision support optimization model for railcar storage at Canadian Pacific Railway</title><author>Espey, R L ; Balakrishnan, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-5fddd8f206552e8255b87a6bfbaddaa548a8b0c554dc8fc7f71297b194a8d7d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>aggregate planning</topic><topic>Applied sciences</topic><topic>Automobile leases</topic><topic>Business and Management</topic><topic>Business structures</topic><topic>Cost efficiency</topic><topic>Costs</topic><topic>Customer satisfaction</topic><topic>Decision making</topic><topic>Decision support systems</topic><topic>Decision theory. 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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.</abstract><cop>London</cop><pub>Taylor & Francis</pub><doi>10.1057/jors.2010.178</doi><tpages>12</tpages></addata></record> |
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| identifier | ISSN: 0160-5682 |
| ispartof | The Journal of the Operational Research Society, 2012-02, Vol.63 (2), p.139-150 |
| issn | 0160-5682 1476-9360 |
| language | eng |
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| source | ABI/INFORM global; JSTOR Archival Journals and Primary Sources Collection; Taylor and Francis Science and Technology Collection |
| 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 |
| title | A spreadsheet decision support optimization model for railcar storage at Canadian Pacific Railway |
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