Nowadays, the integration of smart systems within the modern industrial scenario is a continuously growing paradigm. Computer Numerical Control (CNC) machinery can heavily benefit from the ...introduction of Artificial Intelligence (AI) based monitoring applications. In this paper, we present an industrial condition and fault prevention monitoring system for CNC tools. The developed system is the result of an industrial project aimed at realizing a multi-purpose machine which is currently in pre-commercial stage. The results of this work represent the base platform for the further commercial development, which will be carried on from the industrial partners in accordance with clients feedbacks and specifications. This work presents the hardware architecture of the system, the web-based monitoring platform for remote management, and the AI framework used for fault monitoring. The multi-purpose machine is equipped with accelerometer units to monitor the vibration in multiple points of the structure. The control unit of the machine is connected to the sensing nodes and is used to communicate the actual machine state to a remote web platform. The accelerometric data are analyzed through an AI algorithm to perform fault detection. The fault detection algorithm was trained with the measurements performed on the machine under controlled environment faulty operation. The Internet of Things (IoT) based architecture has proven to be effective to facilitate the supervision of the machining processes, and the AI-based classification shows good classification performances for the fault detection tests.
We present a probe vehicles approach to evaluate traffic congestion in urban areas. The method is illustrated by a real case study in Bari, Italy. Global Positioning System (GPS) data generated by ...buses are transmitted via General Packet Radio Service (GPRS) to a control station, where they are used to analyze traffic conditions. Traffic indices are evaluated to provide an exhaustive view of roads congestion and implement suitable control strategies. Thus, traffic administrators can efficiently manage sustainable mobility in cities and people can acquire awareness on traffic and public transport performance.
Multi-Criteria Decision Making (MCDM) techniques are mathematical tools that help decision makers evaluating and ranking in an automatic way many possible alternatives over multiple conflicting ...criteria in highly complex situations. Several MCDM approaches exist, and their application fields are numerous, including the Supplier Selection Problem (SSP), which is an important problem in the management field. The aim of this paper is to perform a comparative analysis among some selected well-known MCDM techniques to show how they can properly support the specific decision making process of Public Procurement (PP) tenders, which is a particular type of the SSP, characterized by very stringent rules, thus requiring a specific assessment. Indeed, PP is a field characterized by the need for transparency, objectivity, and non-discrimination, which requires tendering organizations to explicitly state the adopted awarding method, the chosen decision criteria, and their relative importance in the call for proposals. However, this field has been seldomly investigated in the pertinent literature and thus the aim of this paper is to overcome such a limitation. In particular, this work focuses on the most commonly adopted methods in the field of supplier selection, namely the Analytic Hierarchy Process (AHP), the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE), the Multi Attribute Utility Theory (MAUT), and the Data Envelopment Analysis (DEA). First, we adapt these techniques to the PP problem and its requirements. Then, by means of some real tenders at a European Institution, the selected techniques are compared with each other and with the currently adopted methodology in their classical deterministic setting, to identify which method best suits the specific requirements of PP tenders. Hence, since nowadays uncertainty is inherent in data from real applications, and can be modelled by expert evaluations through fuzzy logic, the comparison is extended to the fuzzy counterparts of two of the most promising selected approaches, i.e., the Fuzzy AHP and the Fuzzy DEA, showing that these methods can be effectively applied to the PP sector also in the presence of uncertainty on the tenders data.
•The paper proposes a comparison of Multi-Criteria Decision Making approaches.•We focus on the Public Procurement awarding process.•This sector is characterized by strict requirements.•Several real public tenders from a European Institution are evaluated.•Models are compared both under deterministic and fuzzy settings.
Transportation is an indispensable activity for the socio-economic development of countries. However, it is one of the most energy consuming and polluting activities and its environmental impact is ...expected to increase in the future. As a conse- quence, there is a growing need for managing in a more efficient and sustainable manner this sector. This paper investigates the main issues in developing sustain- able transportation systems with a focus on the European Union, highlighting recent trends to identify the foremost research areas whose advancement will reduce the environmental impact of this sector. Following the European Commission guidelines, three sub-themes are focused: Supply Chain Networks, intermodal transportation of freights, and passenger transportation in urban area. Based on a literature review, each sub-theme is analyzed in terms of needs, barriers, and recent research trends. Some contributions to accomplish the identified needs and overcome the detected barriers are reported as baselines to address the future research agenda. The main findings fit with most of the industrialized countries, that are expected to make more significant reductions in their environmental impact. At the same time, by highlighting examples of good practices and identifying limits to adaptation, this work can also support developing countries in tackling this issue.
The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In ...multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity.
Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.
The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.
•The paper presents a novel cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique.•The technique evaluates the performance of Decision Making Units (DMUs) under uncertainty.•A fuzzy ...efficiency is assigned each DMU by a cross evaluation as a compromise between objectives.•Results are defuzzified to rank the DMUs.•The method is applied to the performance evaluation of healthcare systems in an Italian region.
The paper presents a novel cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique for evaluating different elements (Decision Making Units or DMUs) under uncertainty. In order to evaluate the performance of several DMUs while dealing with uncertain input and output data, the presented technique employs triangular fuzzy numbers. A fuzzy triangular efficiency is associated to each DMU through a cross evaluation obtained by a compromise between suitably chosen objectives. Results are then defuzzified to provide a ranking of the DMUs. The proposed method is applied to the performance evaluation of healthcare systems in a region of Southern Italy. The DMU data uncertainty derives from ongoing reforms and the reported assessment is conducted firstly in order to evaluate and rank the efficiency of the considered healthcare systems, and subsequently to assess the evolution of the performance of one of the most affected among these DMUs by the reform plans. The case study demonstrates the model ease of application, its discriminative power among DMUs when compared to a more classical fuzzy DEA approach, and the usefulness in planning and validating targeted reforms in the case of healthcare systems.
We present a novel method for supply chain network (SCN) design under uncertainty that jointly solves the candidate selection, the order allocation, and the transportation mode selection problems. In ...the proposed method, four steps are executed in cascade. First, a cross-efficiency fuzzy data envelopment analysis technique ranks the candidates of each SCN stage in a multiobjective perspective and under uncertain data. Second, a fuzzy linear integer programming model determines the supplies required from each actor by those belonging to the subsequent SCN stage. This step determines the best compromise between candidates' efficiencies, estimated costs, and delivery time, considering stock levels and uncertain capacity of actors, while satisfying customers' uncertain demand. The third step evaluates the efficiency of the transportation alternatives under uncertain data to optimally plan the transport chain. Finally, the fourth step measures the performance of the designed SCN. The method provides as a result an integrated, agile, and resource-efficient design of the SCN under uncertainty. Its application to a case study shows it is effective in selecting the SCN partners, assigning the corresponding order quantities, and delivering them to customers. Validation is obtained by comparison with well-known approaches and statistical analysis.
The slime mould algorithm (SMA) has recently been introduced to solve continuous engineering problems, which has been employed to solve a wide range of various problems due to its good performance. ...This paper presents an enhanced binary SMA for solving the 0–1 knapsack problem at different scales. In the presented binary SMA, eight different transfer functions have been used and evaluated. The transfer function, which has performed better than others, has been proposed for the subsequent experiments. The Bitwise and Gaussian mutation operators are used to enhance the performance of the proposed binary SMA. Furthermore, a penalty function and a repair algorithm are used to handle infeasible solutions. The proposed method’s performance was evaluated statistically on 63 standard datasets with different scales. The obtained results from the proposed method were compared with ten state-of-the-art methods. The results indicated the superiority of the proposed methods.
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 article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products ...are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners' selection and allocation of the received orders among them. The partners' selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers' demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. Note to Practitioners -We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members' selection and the orders' allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers.
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