Degree Granting Department
Ali Yalcin, Ph.D.
Jose L. Zayas-Castro, Ph.D.
Suresh Khator, Ph.D.
Mixed integer quadratic model, gate assignment, assignment heuristic, real time assignment, efficiency improvement in transportation, terminal operations
Less-than-truckload industry has a valuable potential for applications of operations research in two areas, network design and efficiency improvement within existing networks. This thesis focuses on the latter, specifically the less-than-truckload terminals where cross docking operations occur.
The assignment of incoming trailers to inbound docks is one of the critical decisions that affect the performance of less-than-truckload terminals. This research reviews existing models in literature and introduces an optimal mixed integer quadratic model with the objective of generating assignments that are robust against variability in system parameters such as truck arrival and service times, terminal characteristics and trailer load content. The computational limitations of the optimal model are discussed.
A dock assignment heuristic is developed to overcome the computational difficulties reported with the optimal model to solve realistic size problems. It is concluded that the heuristic is generally applicable and is robust against system variably. A dynamic dock assignment heuristic is later introduced to implement the decision process at real time. It is concluded that the dynamic dock assignment heuristic is also robust against system variability.
The last part presents a case study that benchmarks the dynamic dock assignment heuristic and existing static assignments at a real terminal. The results show that the dynamic dock assignment heuristic outperforms the static assignment under system variability. Conclusions and future research areas are finally addressed in the last chapter.
Scholar Commons Citation
Acar, Mesut Korhan, "Robust Dock Assignments at Less-Than-Truckload Terminals" (2004). USF Tampa Graduate Theses and Dissertations.