Techniques based on Soft Computing are useful to model and solve real-world problems where decision makers use subjective knowledge or linguistic information when making decisions, measuring parameters, objectives, and constraints, and even when modeling the problem. In many problems in transport and logistics, it is necessary to take into account that the available knowledge about some data and parameters of the problem model is imprecise or uncertain. Truck and Trailer Routing Problem, TTRP, is one of most recent and interesting problems in transport routing planning. TTRP is a combinatorial optimization problem, and it is computationally more difficult to solve than the known Vehicle Routing Problem, VRP. Most of models used in the literature assume that the data available is accurate; but this consideration does not correspond with reality. For this reason, it is appropriate to focus research toward defining TTRP models for incorporating the uncertainty present in their data. The aims of the present chapter are: a) to provide a study on the Truck and Trailer Routing Problem that serves as help to researchers interested on this topic and b) to present an approach using techniques of Soft Computing to solve this problem.
A task based approach for a real-world commodity routing problem