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In this paper we provide an overview of recent theoretical approaches and technologies that respond to the fundamental challenges of modern factory automation. We classify these major methods and t...

This paper proposes a hierarchical technique for Supply Chain Network (SCN) efficiency maximisation under uncertainty composed of three steps. The first step extends a previous fuzzy cross-efficiency Data Envelopment Analysis approach, originally intended for suppliers’ selection, in order to evaluate and rank all the actors in each SCN stage under conflicting nondeterministic criteria. Afterwards, a fuzzy linear integer programming model is stated and solved for each pair of subsequent SCN stages to determine the quantities required from each stakeholder to maximise the overall SCN efficiency while satisfying the estimated demand and respecting the nodes capacity. Finally, a heuristics is applied to limit the exchange of small quantities in the SCN, in which the trade is not economically convenient according to quantity discounts. An illustrative example from the literature shows the technique effectiveness.

This paper provides a survey of the main advanced control techniques currently adopted in factory automation. In particular, it focuses on five classes of control approaches, namely: model-based control, control based on computational intelligence, adaptive control, discrete event systems-based control and finally event-triggered and self-triggered control. A particular focus is put on the most significant and recent contributions in these areas with attention to their application in the factory automation domain. Finally, open issues, challenges and the requirements of further research efforts for each class are pointed out.

A novel hierarchical and iterative technique is presented for the analysis and management of the internal logistics of manufacturing systems. The method effectively integrates the value stream mapping VSM tool, the analytic hierarchy process AHP approach, and discrete event simulation. Starting from a concise description of the manufacturing system obtained by the VSM graphical approach, so as to identify nonvalue-adding activities, a detailed and standardised description is obtained by the unified modelling language UML. Then the AHP technique is used to rank the system anomalies, singling out the major ones. Further application of the VSM tool produces an overall picture of the desired manufacturing system internal flow, and the UML description details the to-be system activities. Finally, the use of discrete event simulation allows the quantitative verification of the effects of the changes in the production system. The proposed technique is a tool to systematically improve the internal logistics of complex production systems while assessing the system dynamics and corresponding performance improvements. An application of the method to a real case study enlightens its effectiveness.

Worldwide competition originated the development of integrated e-supply chains (IESC) that are distributed manufacturing systems integrating international logistics and information technologies with production. This work builds upon an IESC network design methodology previously proposed to select partners in the different IESC stages and the links connecting them. In order to rank the Pareto optimal solutions obtained by such a method, the paper proposes a second level IESC optimization performed using fuzzy logic. Indeed, fuzzy multi-criteria optimization is particularly suitable for choosing, on the basis of the subjective and qualitative knowledge provided by the decision makers, the IESC configuration from the set of Pareto optimal alternatives. Two fuzzification techniques and two different multi-criteria methods are considered. Moreover, a discussion on the different advantages and limitations of the proposed techniques is provided. Finally, the effectiveness of the methodology is illustrated by way of a case study.