Delivering automobiles, vans, and trucks to auto dealerships is a unique problem among the routing and scheduling problem class. A subproblem is that of loading the vehicles onto an auto-carrier (equipment specially designed for this purpose). Allocation of the vehicles to this equipment has typically been done by means of trial and error and heuristic methods. This paper shows how the loading aspect of the delivery problem can be solved using optimizing methods. The approach has the potential of replacing the empirical methods currently used in practice.

A novel interpolation algorithm, fuzzy interpolation, is presented and compared with other popular interpolation methods widely implemented in industrial robots calibrations and manufacturing applications. Different interpolation algorithms have been developed, reported, and implemented in many industrial robot calibrations and manufacturing processes in recent years. Most of them are based on looking for the optimal interpolation trajectories based on some known values on given points around a workspace. However, it is rare to build an optimal interpolation results based on some random noises, and this is one of the most popular topics in industrial testing and measurement applications. The fuzzy interpolation algorithm (FIA) reported in this paper provides a convenient and simple way to solve this problem and offers more accurate interpolation results based on given position or orientation errors that are randomly distributed in real time. This method can be implemented in many industrial applications, such as manipulators measurements and calibrations, industrial automations, and semiconductor manufacturing processes.