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The Truck and Trailer Routing Problem consists of a heterogeneous fleet composed of trucks and trailers to serve a set of customers. This problem has been solved previously considering accurate data available. But in a real-world the available knowledge about some data and parameters involves a vague nature. In this study, we propose the application of the Soft Computing to model and solve the TTRP when capacity constraints are imprecise. Numerical examples are presented to illustrate the proposed method.

The Truck and Trailer Routing Problem, TTRP, is an extension of the well-known Vehicle Routing Problem. This problem consist of a heterogeneous fleet composed of trucks and trailers to serve a set of customers. Most of models used in the literature assume that the data available are accurate, but in many practical problems the available knowledge about some data and parameters of the model involving uncertainty. In this study, we propose the application of a Soft Computing-based method to model and solve TTRP when the set of constrains is imprecise.

Techniques based on Soft Computing are useful to solve real-world problems where decision makers use subjective knowledge when making decisions. In many problems in transport and logistics it is necessary to take into account that the available knowledge about the problem is imprecise or uncertain. Truck and Trailer Routing Problem (TTRP) is one of most recent and interesting problems in transport routing planning. Most of models used in the literature assume that the data available are accurate; for this reason it would be appropriate to focus research toward defining TTRP models for incorporating the uncertainty present in their data.

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