An adapted version of the water wave optimization algorithm for the capacitated vehicle routing problem with time windows with application to a real case using probe data

Customers' habits, as far as shipping requests are concerned, have changed in the last decade, due to the fast spread of e-commerce and business to consumer (B2C) systems, thus generating more and more vehicles on the road, traffic congestion and, consequently, more pollution. Trying to partially solve this problem, the operational research field dedicates part of its research to possible ways to optimize transports in terms of costs, travel times, full loads etc., with the aim of reducing inefficiencies and impacts on profit, planet and people, i.e., the well-known triple bottom line approach to sustainability, also thanks to new technologies able to instantly provide probe data, which can detail information as far as the vehicle behavior. In line with this, an adapted version of the metaheuristic water wave optimization algorithm is here presented and applied to the context of the capacitated vehicle routing problem with time windows. This latter one is a particular case of the vehicle routing problem, whose aim is to define the best route in terms of travel time for visiting a set of customers, given the vehicles capacity and time constraints in which some customers need to be visited. The algorithm is then tested on a real case study of an express courier operating in the South of Italy. A nearest neighbor heuristic is applied, as well, to the same set of data, to test the effectiveness and accuracy of the algorithm. Results show a better performance of the proposed metaheuristic, which could improve the journeys by reducing the travel time by up to 23.64%

The evolution of man–machine interaction: the role of human in Industry 4.0 paradigm

ndustry 4.0 is a new paradigm in the manufacturing world and it has deeply changed the Human–machine interaction. This paper focus is on the nature of this interaction, which is made possible thanks to the Internet of Things (IoT), and Cyber-Physical System (CPS). These Industry 4.0 key technologies are studied related to the standard Deming cycle, in order to underline the importance of Human–machine interaction. The Fourth Industrial Revolution involves several changes in the workforce’s key features. In this paper, a new perspective based on the centrality of humans is given in the new Industry era. The importance of the human factor will be deeply studied through the implementation of the ‘Sand Cone Model’. A new framework is proposed in order to explain the quality measures addiction on the workforce quality skills, and how it engraves on improving efficiency and effectiveness of an industrial process

The evolution of man–machine interaction: the role of human in Industry 4.0 paradigm

Industry 4.0 is a new paradigm in the manufacturing world and it has deeply changed the Human–machine interaction. This paper focus is on the nature of this interaction, which is made possible thanks to the Internet of Things (IoT), and Cyber-Physical System (CPS). These Industry 4.0 key technologies are studied related to the standard Deming cycle, in order to underline the importance of Human–machine interaction. The Fourth Industrial Revolution involves several changes in the workforce's key features. In this paper, a new perspective based on the centrality of humans is given in the new Industry era. The importance of the human factor will be deeply studied through the implementation of the 'Sand Cone Model'. A new framework is proposed in order to explain the quality measures addiction on the workforce quality skills, and how it engraves on improving efficiency and effectiveness of an industrial process

Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0

Capacitated vehicle routing problem with time windows: A linear model and a case study of express courier

Given the importance gained by the e-commerce field in the recent years, this study investigates the issue of minimizing the delivery travel time of a real company located in the South of Italy and operating as a courier, express and parcel (CEP) service provider. The scenario under examination consists of a depot, three vehicles and several customers served by the CEP company. A Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) model is formulated to optimize the deliveries to the customers for the targeted company and solved under the commercial software IBM ILOG CPLEX Optimization Studio. As outcomes, the model returns a simulated path covered by the vehicles and computes the corresponding travel time. Results show that with the proposed formulation, the time windows (TWs) of all customers are respected. Because the analysis is grounded on a real company, the results are expected to provide practical indications to logistics and supply chain managers, to maximize the performance of their delivery system

Framework for selecting manufacturing simulation software in industry 4.0 environment

Even though the use of simulation software packages is widespread in industrial and manufacturing companies, the criteria and methods proposed in the scientific literature to evaluate them do not adequately help companies in identifying a package able to enhance the efficiency of their production system. Hence, the main objective of this paper is to develop a framework to guide companies in choosing the most suitable manufacturing simulation software package. The evaluation framework developed in this study is based on two different multi-criteria methods: analytic hierarchy process (AHP) integrated with benefits, opportunities, costs, risks (BOCR) analysis and the best-worst method (BWM). The framework was developed on the basis of the suggestions from the literature and from a panel of experts, both from academia and industry, trying to capture all the facets of the software selection problem. For testing purposes, the proposed approach was applied to a mid-sized enterprise located in the south of Italy, which was facing the problem of buying an effective simulation software for Participatory Design. From a practical perspective, the application showed that the framework is effective in identifying the most suitable simulation software package according to the needs of the company. From a theoretical point of view, the multi-criteria methods suggested in the framework have never been applied to the problem of selecting simulation software; their usage in this context could bring some advantages compared to other decision-making tools

2D Shape Recognition Using Information Theoretic Kernels

In this paper, a novel approach for contour-based 2D shape recognition is proposed, using a recently intro-duced class of information theoretic kernels. This kind of kernels, based on a non-extensive generalization of the classical Shannon information theory, are defined on probability measures. In the proposed approach, chain code representations are first extracted from the contours; then n-gram statistics are computed and used as input to the information theoretic kernels. We tested different versions of such kernels, using support vector machine and nearest neighbor classifiers. An experi-mental evaluation on the chicken pieces dataset shows that the proposed approach outperforms the current state-of-the-art methods. 1

Hemostatic powder TC-325 treatment of malignancy-related upper gastrointestinal bleeds: International registry outcomes

BACKGROUND AND AIM: Upper gastrointestinal tumors account for 5% of upper gastrointestinal bleeds. These patients are challenging to treat due to the diffuse nature of the neoplastic bleeding lesions, high rebleeding rates, and significant transfusion requirements. TC-325 (Cook Medical, North Carolina, USA) is a hemostatic powder for gastrointestinal bleeding. The aim of this study was to examine the outcomes of upper gastrointestinal bleeds secondary to tumors treated with Hemospray therapy. METHODS: Data were prospectively collected on the use of Hemospray from 17 centers. Hemospray was used during emergency endoscopy for upper gastrointestinal bleeds secondary to tumors at the discretion of the endoscopist as a monotherapy, dual therapy with standard hemostatic techniques, or rescue therapy. RESULTS: One hundred and five patients with upper gastrointestinal bleeds secondary to tumors were recruited. The median Blatchford score at baseline was 10 (interquartile range [IQR], 7-12). The median Rockall score was 8 (IQR, 7-9). Immediate hemostasis was achieved in 102/105 (97%) patients, 15% of patients had a 30-day rebleed, 20% of patients died within 30 days (all-cause mortality). There was a significant improvement in transfusion requirements following treatment (P < 0.001) when comparing the number of units transfused 3 weeks before and after treatment. The mean reduction was one unit per patient. CONCLUSIONS: Hemospray achieved high rates of immediate hemostasis, with comparable rebleed rates following treatment of tumor-related upper gastrointestinal bleeds. Hemospray helped in improving transfusion requirements in these patients. This allows for patient stabilization and bridges towards definitive surgery or radiotherapy to treat the underlying tumor

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management