The aim of this paper is to find an optimal solution to operational planning of freight transportation in an industrial district. We propose a system architecture that drives agents – the industrial district firms - to cooperate in logistic field, to minimize transport and environmental costs. The idea is to achieve logistics optimization setting up a community made of district enterprises, preserving a satisfactory level of system efficiency and fairness. We address the situation in which a virtual coordinator helps the agents to reach an agreement. The objectives are: maximizing customers satisfaction, and minimizing the number of trucks needed. A fuzzy clustering (FCM), two Fuzzy Inference System (FIS) combined with a Genetic Algorithm (GA), and a greedy algorithm are thus proposed to achieve these objectives, and eventually an algorithm to solve the Travelling Salesman Problem is also used. The proposed framework can be used to provide real time solutions to logistics management problems, and negative environmental impacts.

The potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy sets.