Door Allocations to Origins and Destinations at Less-than-Truckload Trucking Terminals

Document Type : Research Paper

Authors

1 Department of Industrial management, National Taiwan University of Science and Technology, Taiwan

2 Equity Trading Lab, Morgan Stanley, 1585 Broadway Ave, New York, NY 10036

3 Department of Industrial and Operations Engineering, University of Michigan, USA

Abstract

For an LTL (Less-than Truckload) carrier, the allocation of doors at a consolidation facility to outbound trailers assigned to various destinations, and to inbound trailers in the continuous stream arriving from various origins, has a significant impact on its operations, and on the nightly man-hours needed for consolidation. In the past literature door allocations to destinations of outbound trailers are determined using deterministic mathematical models based on average volumes of shipments between origin-destination pairs. The online nature of allocation of doors to inbound trailers is either ignored or simple rules like FCFS (First Come First Served) are assumed that do not take advantage of the data on the trailer's actual contents readily available at the time of its arrival. In reality the actual shipment volume between any origin-destination pair varies significantly from day to day. Due to this wide variation destination door allocations that are optimal for the average volume tend to be far from optimum for most nights. Also, very simple on-line policies for door allocation to each inbound truck at the time of its arrival based on its actual contents can significantly reduce the man-hours needed to consolidate its contents. In this paper we develop a new model that uses such an on-line policy for door allocations to inbound trailers, and determines doors to allocate to destinations to minimize the expected man-hours for consolidating freight nightly taking the random variation in freight volumes into account. Computational results on data from an actual facility indicate that the man-hour requirement can be reduced by over 20% compared to current practice.

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[1] Ahuja R.K., Magnanti T.L., Orlin J.B. (1993), Network Flows: Theory, Algorithms, and Applications.
Prentice Hall.
[2] Ahuja R.K., Orlin J.B., Tiwari A. (2000), A greedy genetic algorithm for the quadratic assignment
problem; Computers and Operations Research 27; 917-934.
[3] Bartholdi J.J., Gue K.R. (2000), Reducing labor costs in an LTL crossdocking terminal; Operations
Research 48; 823-832.
[4] Bermudez R., Cole M.H. (2000), A genetic algorithm approach to LTL breakbulk terminal door
assignment; In Proceedings of the 2000 Industrial Engineering Research Conference; Cleveland, Ohio.
[5] Bureau of Transportation Statistics, USA (2003), National transportation statistics.
[6] Davis L. (1991), Handbook of Genetic Algorithms; Van Nostrand, New York.
[7] Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning; Addison
Wesley.
[8] Gue K.R. (1999), The effects of trailer scheduling on the layout of freight terminals; Transportation
Science 33; 419-428.
[9] Holland J.H. (1975), Adaptations in Natural and Artificial Systems; The University of Michigan Press,
Ann Arbor.
[10] Peck K.E. (1983), Operational Analysis of Freight Terminals Handling Less than Container Load
Shipments; Ph.D. thesis, University of Illinois at Urbana-Champaign.
[11] Savage S., Scholtes S., Zweidler D. (2006), Probability management; ORMS Today 33(1); 20-28.
[12] Tsui L.Y., Chang C.-H. (1990), A microcomputer-based decision support tool for assigning dock doors
in freight yards; Computers and Industrial Engineering 19; 309-312