Optimizing the Door Assignment in LTL-Terminals

• Published in: Transportation science Vol. 43; no. 2; pp. 198 - 210
• Main Authors: Chmielewski, Annette, Naujoks, Boris, Janas, Michael, Clausen, Uwe
• Format: Journal Article
• Language: English
• Published: Baltimore INFORMS 01-05-2009; Transportation Science & Logistic Society of the Institute for Operations Research and Management Sciences; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Analysis; Archives; column generation; Combinatorial optimization; Combinatorics; door assignment; Evolution; Forklift trucks; Freight; Freight transport; Genetic algorithms; Logistics; Logistics services; Management; Mathematical models; Mathematical optimization; Motor carriers; multiobjective evolutionary algorithms; Objective functions; Operations management; Optimization; Shipments; Studies; Transport economics; Transportation; Transportation problem (Operations research); Transshipment; Trucking; Trucks; United States
• ISSN: 0041-1655; 1526-5447
• DOI: 10.1287/trsc.1090.0271

In less-than-truckload (LTL) terminals, arriving trucks have to be assigned to inbound doors and to suitable time slots for unloading. Simultaneously, waiting trucks have to be allocated to outbound doors. During a couple of hours, shipments from all incoming trucks are unloaded, sorted according to their relation, transported to the right outbound door, and loaded on the outgoing truck. (The term "relation" is an equivalent for destination; it originates from the German logistics vocabulary that uses the term to specify a certain transport offered between a source and a sink.) The first and the most important optimization aim is to minimize the total distance when transshipping units, because this leads to reduction in operational costs, which are usually very high. The second, and minor, aim is to minimize the waiting time for each truck. Usually the operator of an LTL transshipment building works with subcontractors when collecting and delivering goods. Therefore, no penalties have to be paid by the operators in case waiting times are too long. The logistical optimization task is modeled as a time-discrete, multicommodity flow problem with side constraints. Based on the applicable model, a decomposition approach and a modified column-generation approach are developed. In parallel, an evolutionary algorithm (EA) was implemented to tackle the problem at hand. Both algorithms—from the field of discrete mathematics, as well as from the field of computational intelligence—are applied to 10 test scenarios. A comparison of the solution process, as well as a comparison of the solution quality, concludes the work.