The paper addresses feedback control of actuated prostheses based on the Stewart platform parallel mechanism. In such a problem it is essential to apply a feasible numerical method to determine in real time the solution of the forward kinematics, which is highly nonlinear and characterized by analytical indetermination. In this paper, the forward kinematics problem for a human elbow hydraulic prosthesis developed by the research group of Polytechnic of Bari is solved using artificial neural networks as an effective and simple method to obtain in real time the solution of the problem while limiting the computational effort. We show the effectiveness of the technique by designing a PID controller that governs the arm motion thanks to the provided neural computation of the forward kinematics. © 2014 Elsevier B.V.

The paper addresses closed loop control of a hydraulic prosthesis for human elbow. In such a problem it is essential to obtain quick results of simulation in order to appreciate the dynamic behavior of the entire system. In this paper, the forward kinematics problem for a hydraulic prosthesis for human arm developed by the research group of Polytechnic of Bari is solved using artificial neural networks as an effective and simple method to obtain in real time the solution of the problem without an excessive computational effort. We show the effectiveness of the method by designing a PID closed loop control that effectively controls the arm thanks to the provided neural computation of the forward kinematics.

The paper addresses the optimal design of the last supply chain branch, i.e., the Distribution Network (DN), starting from manufacturers till the retailers. It considers a distributed system composed of different stages connected by material links labeled with suitable performance indices. A hierarchical procedure employing direct graph (digraph) modeling, mixed integer linear programming, and the Analytic Hierarchy Process (AHP) is presented to select the optimal DN configuration. More in detail, a first-level DN optimization problem taking into account the definition and evaluation of the distribution chain performance provides a set of Pareto optimal solutions defined by digraph modeling. A second level DN optimization using the AHP method selects, on the basis of further criteria, the DN configuration from the Pareto face alternatives. To show the method effectiveness, the optimization model is applied to a case study describing an Italian regional healthcare drug DN. The problem solution by the proposed design method allows improving the DN flexibility and performance.

This paper addresses a crucial objective of the strategic purchasing function in supply chains, i.e. optimal supplier selection. We present a hierarchical extension of the data envelopment analysis (DEA), the most widespread method for supplier rating in the literature, for application in a multiple sourcing strategy context. The proposed hierarchical technique is based on three levels. First, a modified DEA approach is used to evaluate the efficiency of each supplier according to some criteria proposed by the buyer. Second, the well known technique for order preference by similarities to ideal solution (TOPSIS) is applied to rank the maximally efficient suppliers given by the previous step. Third and finally, a linear programming problem is stated and solved to find the quantities to order from each maximally efficient supplier in the multiple sourcing context. The presented approach is able to straightforwardly discern between efficient and inefficient partners, avoid the confusion between efficient and effective suppliers and split the supply in a multiple sourcing context. The hierarchical model is applied to the supply of a C class component to show its robustness and effectiveness, while comparing it with the DEA and TOPSIS approaches

he efficient management of hospital departments (HDs) has recently become an important issue. Indeed, the in- creased demand and design for hospital services have saturated the capacity of HD that requires suitable tools for the efficient use of resources and flow of patients, staff, and drugs. This pa- per proposes a model based on a three-level strategy to design at the tactical level in a concise and effective way the structure, the resources, and the dynamics of a critically congested HD. The design strategy is composed of three basic elements: the modeling module, the optimization module, and the simulation and decision module. The first module employs a Unified Modeling Language tool and a timed Petri net (PN) model to effectively capture the detailed flow and dynamics of patients, starting from their arrival to the HD until their discharge. The optimization module employs the fluid relaxation to concisely approximate in a continuous PN framework the HD model and optimize suitable performance indices. The simulation module verifies that the opti- mized parameters allow an effective workflow organization while maximizing the patient flow. In case of inconsistencies due to the fluid approximation between the continuous model used in the design phase by the optimization module and the discrete one used in the subsequent verification phase by the simulation module, the latter module revises the values of some HD model parameters. A real case study on the Emergency Cardiology Department of the General Hospital of Bari (Italy) shows the efficiency and accuracy of the proposed method.

We present a Decision Support System (DSS) for real-time management of railway networks. The DSS employs a mathematical programming approach addressing traffic rescheduling under unexpected disturbances in a mixed-(single- and double-) tracked network. The DSS simulates the network behavior with the mathematical programming model based on the railway topology and constraints, rescheduling the timetable in real time, detecting and solving conflicts in the network. The DSS is applied to a real data set related to a large portion of a regional network in Southern Italy.

We present a simulation model based on the Nash equilibrium for the analysis of the auction based day ahead electricity generation market. Starting from the empirical data distributions of the market clearing price and the energy demand registered by the supervisory authority, the model allows evaluating the market competitiveness and preventing anticompetitive actions by participants. It also represents a basis for a decision support tool for producers to define their optimal bidding strategy. With respect to other existing models, it allows considering differences in the generation capacities of producers, in the utilized energy sources, and in the zonal market. The model is tested in the Italian energy market by means of two different scenarios and by varying the number of bidders and their production capacities.

In this paper we present a decision support scheme to help managing and optimizing two critical activities in intermodal terminals, namely the containers allocation in the terminal yard and the freight trains composition. In particular, the focus of this paper is on the first problem and the goal is that of maximizing the utilization of the available space while keeping into account several constraints. The approach was successfully tested on a real case study, the rail-road terminal of a leading intermodal logistics company.

This paper focuses on modelling and performance evaluation of an Intermodal Freight Transport Terminal (IFTT), the rail-road inland terminal of a leading Italian intermodal logistics company. The IFTT is regarded as a discrete event system and is modelled in a timed Petri net framework. By means of suitable performance indices, we simulate the Petri net model and evaluate the operational performance of the transport system. This allows assessing the efficiency level of the terminal and identifying its criticalities and bottlenecks. Further, the model allows evaluating different solutions to the recognized criticalities under alternative scenarios (e.g., when inflow traffic increases and congestions may occur).

The paper specifies an Integrated System (IS) devoted to the management of Intermodal Transportation Networks (ITNs) to take both tactical decisions, i.e., in an offline mode, and opera- tional decisions, i.e., in real-time. Both the resulting IS structures rely on a closed-loop approach that is able to tune the choices with the current system conditions. In either case, the core of the pre- sented IS are a reference model and a simulation module. In par- ticular, the reference model uses information from the real system, obtained by modern Information and Communication Technolo- gies (ICTs) and the simulation module evaluates the impact of the management decisions. In order to obtain a systematic model suit- able to describe a generic ITN, the paper proposes a metamodeling approach that describes in a thorough and detailed way the struc- ture and the behavior of ITNs. Moreover, the metamodeling pro- cedure is a top-down technique based on the well-known Unified Modeling Language (UML), a graphic and textual formalism able to describe systems from structural and behavioral viewpoints. In order to show the IS application at the tactical decision level, the paper specifies the IS for an ITN case study that is constituted by the port of Trieste (Italy) and the inland terminal of Gorizia (Italy). The results show how the IS can improve the performance of the ITN by applying ICT tools and information-based services.