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This paper addresses the wholesale hub location problem in food supply chains. The paper aims to design an optimal hub location network to serve food consumption markets through efficient connections with production sites. These optimal locations can be compared with the current locations of hubs to determine whether changes could lead to greater efficiencies. The model is mathematically formulated as a mixed-integer programming problem. The model minimizes the total network costs, which include the transportation of goods and the construction of hubs. The mathematical program considers several constraints on travel distance, hub capital cost and capacity, road condition, and transportation cost. Several experiments are conducted to test the sensitivity of the model to changes in parameters such as the food's average travel distance, the maximum hub capacity, and the transportation cost. Then, a real-world application is made to the Northeast United States livestock industry. Finally, the results show the effect of the changes in model parameters on the optimal hub network design (i.e., the number of hubs and the selection of hub locations).

The problem of dynamic origin–destination (O-D) demand estimation aims at estimating the unknown demand values for all O-D pairs and departure times with the use of available time-varying link flow observations. This paper presents a distributed algorithm for estimating the dynamic O-D tables for urban transportation networks. The new algorithm supports the deployment of systems for real-time traffic network management that adopt dynamic traffic assignment methodology for network state estimation and prediction. It encapsulates available link information and reduces the data size required by conventional algorithms for O-D demand estimation. The algorithm adopts a two-stage approach. In the first stage, the study area under consideration is divided into a number of subareas, and an O-D demand table is estimated independently for each subarea. These local O-D tables are then integrated to construct an O-D table for the entire study area. An application of the new algorithm for a typical freeway network is presented as an example.