THESIS DEFENSE :: Driver Management for Less-than-Truckload Carriers

Event Details
  • Date/Time:
    • Thursday December 14, 2006 - Wednesday December 13, 2006
      12:00 pm - 11:00 pm
  • Location: Conference Room 226, Groseclose Building
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    N/A
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Contact
Barbara Christopher
Industrial and Systems Engineering
Contact Barbara Christopher
404.385.3102
Summaries

Summary Sentence: THESIS DEFENSE :: Driver Management for Less-than-Truckload Carriers

Full Summary: THESIS DEFENSE :: Driver Management for Less-than-Truckload Carriers

The trucking industry is vitally important to the economy. It provides an essential service by transporting goods from business to business and from business to consumer. The less-than-truckload (LTL) industry is an important segment, serving businesses that ship quantities ranging from 150 lbs to 10,000 lbs. Large LTL carriers use thousands of drivers daily to move loads between terminals in the linehaul network, and each driver may be used for multiple consecutive load dispatches between rest periods.

Driver wages are a major component of transportation costs. Consequently, cost-effective driver management is of crucial importance for the profitability of LTL carriers. This thesis investigates a variety of issues related to driver management and has a strong focus on practice. Much of the research concentrates on developing methodologies and implementations that can be converted to commercial strength decision support tools, and on conducting studies that generate and provide useful practical insights.

In this thesis, we describe a scheme for the dynamic scheduling of linehaul drivers developed for one of the largest LTL carriers in the United States. Dynamic driver scheduling is challenging because driver schedules must satisfy a complex set of rules, most of which are specified by government requirements and union regulations. In addition, trucking moves, unlike commercial airline flights or train dispatches, are not pre-scheduled; typically, a truck is dispatched when a sufficient amount of freight has accumulated at a terminal and truck capacity can be utilized effectively. The technology developed in this dissertation combines greedy search with enumeration of time-feasible driver duties, and is capable of generating cost-effective driver schedules covering 15,000-20,000 loads in a matter of minutes.

This thesis describes a tactical tool for determining the allocation of drivers in a trucking terminal network. One of the key tactical questions faced by an LTL carrier is how many drivers to locate (or domicile) at each terminal. Determining an effective driver allocation can be especially difficult due to union rules. Most carriers are unionized and a portion of their drivers, called bid drivers, can only move loads between their domicile (home location) and a designated region. The driver allocation technology developed determines the number of drivers to allocate to each terminal as well as the designated region for each bid driver. Computational experiments using truck movement data representative of operations at a major U.S. LTL carrier demonstrate the effectiveness of our domiciling methodology, and show that union restrictions may result in substantially larger driver fleets, in some cases even up to 50% larger.

Finally, we investigate questions related to the number of drivers required to fulfill a given set of loaded truck moves in a more academic setting in order to obtain some fundamental insights. To facilitate the analysis, some simplifying assumptions are introduced: the terminal network consists of only two terminals and the exact dispatch times of the loaded moves are known. The goal is to determine the minimum number of drivers required to cover all the loaded moves, and the resultant dispatch schedule for these drivers. The problem is analyzed with a number of different restrictions imposed on driver schedules. Without any restrictions on schedules, the problem is shown to be polynomially solvable. For more restricted variants, several easily computable lower bounds on the minimum number of drivers are derived, integer programming formulations are presented, and fast heuristics are designed and analyzed. A computational study provides insights into the quality of the lower bounds, the quality of the heuristics, and the computational requirements of the integer programming formulations.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Status
  • Created By: Barbara Christopher
  • Workflow Status: Published
  • Created On: Oct 8, 2010 - 7:32am
  • Last Updated: Oct 7, 2016 - 9:52pm