On a typical day, more than 53 million tons of goods valued at about $36 million are moved on the US multimodal transportation network. An efficient freight transportation industry is the key in facilitating the required movement of raw materials and finished products. Among different modes of transportation, trucking remains the shipping choice for many businesses and is increasing its market share.Less-than-truckload (LTL) trucking companies provide a transportation service in which several customers are served simultaneously by using the same truck and shipments need to be consolidated at some terminals to build economical loads.Intelligent transportation system (ITS) technologies increase the flow of available data, and offer opportunities to control the transportation operations in realtime. Some research efforts have considered real-time acceptance/rejection of shipping requests, but they are mostly focused on truckload trucking operations. This study tries to use real-time information in decision making for LTL carriers in a dynamically changing environment.The dissertation begins with an introduction of LTL trucking operations and different levels of planning for this type of motor carriers, followed by the review of literature that are related to tactical and operational planning. Following a brief discussion on multi commodity network flow problems and their solution algorithm, a mathematical model is proposed to deal with the combined shipment and routing problem.Furthermore, a decision making procedure as well as a decision support application are developed and are presented in this dissertation. The main step in the decision making procedure is to solve the proposed mathematical problem. Three heuristic solution algorithms are proposed and the quality of the solutions is evaluated using a set of benchmark solutions.Three levels of numerical experiments are conducted considering an auto carrier that operates on a hub-and-spoke network. The accuracy of the mathematical model and the behavior of the system under different demand/supply situations are examined. Also, the performance of the solutions provided by the proposed heuristic algorithms is compared and the best solution method is selected. The study suggests that significant reductions in operational costs are expected as the result of using the proposed decision making procedure.