<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-18T22:34:59Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/68820" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/68820</identifier><datestamp>2026-06-06T01:03:40Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>com_1721.1_101402</setSpec><setSpec>col_1721.1_131023</setSpec><setSpec>col_1721.1_101610</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Jarrod Goentzel.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Banik, Jaya</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Rinehart, Kyle</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Engineering Systems Division</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2012-01-30T16:52:06Z</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2011</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2011</dim:field>
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   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 36-37).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Freight vehicle capacity, whether it be road, ocean or air transport, is highly underutilized. This under-utilization presents an opportunity for companies to reduce their vehicular traffic and reduce their carbon footprint through greater supply chain integration. This thesis describes the impact of ordering guidelines on the transport efficiency of a large firm and how those guidelines and associated practices can be changed in order to gain better efficiency. To that end, we present three recommendations on improving the guidelines based on the shipment data analysis. First, we discuss the redundancy of one of the company's fill metrics based on a scatter plot analysis and a chi-square independence test. Second, we explore the impact of using linear programming to allocate SKUs to different shipment, highlighting the reduction in the number of shipments through better truck mixing. Finally, we divide the SKUs into three groups: cube-constrained, neutral, and weight-constrained. Based on this segmentation, we present a basic model that mixes different SKUs and helps a shipment to achieve a much higher utilization rate. The application of the last two findings can be further explored to address under-utilization in freight carriers across different industries.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Jaya Banik and Kyle Rinehart.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">M.Eng. in Logistics</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">37 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">M.I.T. theses are protected by 
copyright. They may be viewed from this source for any purpose, but 
reproduction or distribution in any format is prohibited without written 
permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
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   <dim:field mdschema="dc" element="title" lang="en_US">Modeling order guidelines to improve truckload utilization</dim:field>
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   	&lt;Title>Modeling order guidelines to improve truckload utilization&lt;/Title>
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   	&lt;PublicationDate>2011&lt;/PublicationDate>
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   	&lt;Abstract>Freight vehicle capacity, whether it be road, ocean or air transport, is highly underutilized. This under-utilization presents an opportunity for companies to reduce their vehicular traffic and reduce their carbon footprint through greater supply chain integration. This thesis describes the impact of ordering guidelines on the transport efficiency of a large firm and how those guidelines and associated practices can be changed in order to gain better efficiency. To that end, we present three recommendations on improving the guidelines based on the shipment data analysis. First, we discuss the redundancy of one of the company&amp;apos;s fill metrics based on a scatter plot analysis and a chi-square independence test. Second, we explore the impact of using linear programming to allocate SKUs to different shipment, highlighting the reduction in the number of shipments through better truck mixing. Finally, we divide the SKUs into three groups: cube-constrained, neutral, and weight-constrained. Based on this segmentation, we present a basic model that mixes different SKUs and helps a shipment to achieve a much higher utilization rate. The application of the last two findings can be further explored to address under-utilization in freight carriers across different industries.&lt;/Abstract>
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