DATA COLLECTION METHODOLOGIES IN URBAN FREIGHT TRANSPORTATION

The well-known approaches to the process of modeling the demand in urban freight transportation in conditions of limited availability of adequate data are considered. The main task is to select a model for creating a reliable system for analyzing urban freight traffic. Demand assessment models were chosen as input data because they are the most representative for assessing urban freight transport performance.

Approximation Algorithms for a New Truck Loading Problem in Urban Freight Transportation

Motivated by urban freight transportation practices in China, we study an optimal truck loading problem in which a fixed cost and an additional cost that depends on the number of unloading points are associated with each truck used. The truck loading problem is modeled as a one-dimensional bin packing problem, where the cost of each bin is a convex fixed-plus-linear function of the number of items in the bin. The objective is to minimize the total cost of bins used. We develop an asymptotic polynomial time approximation scheme and an efficient approximation algorithm with an asymptotic worst-case performance ratio of 1.5 to tackle the problem. Our computational experiments indicate that the approximation algorithm performs well for certain order patterns, and also reveal several important insights on using the freight charge scheme.