This paper presents a three-objective distribution planner to tackle the tactical optimization of fresh food distribution networks considering operating cost, carbon footprint and delivery time ...goals. The developed expert system overcomes the widely adopted methodologies mainly focused on the cost minimization only. These three independent goals are jointly included in a unique tool, called Food Distribution Planner, to support the tactical planning of multi-modal distribution networks of perishable produces.
This expert system implements a three-objective linear programming model, considering the typical food distribution constraints, i.e. the food quality dependence on the delivery time, the geographically distributed market demand and the farmer production capacities.
This paper further applies the proposed system to a real case study dealing with the distribution of fresh fruits and vegetables from a set of Italian producers to several European retailers. The most effective distribution network is studied best balancing the economic, environmental and delivery time objective functions. Such a tactical network planning leads to 9.6% CO2 emission reduction with 2.7% cost increase compared to the correspondent single-objective configurations. Finally, the delivery time allows no produce waste due to the food quality preservation during shipment.
•Cost, emissions and time multi-objective optimization for fresh food distribution.•Multi-produce, multi-modal, multi-level distribution network planning.•Food perishability during transport evaluated through the quality loss function.•Case study of Italian consortium to distribute fresh produces to European retailers.•Cost, carbon footprint and delivery time Pareto frontier determination & evaluation.
Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature ...reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.
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•Model and closed-form expressions to predict divisional Seru productivity.•Properties stating the divisional Seru behaviour are presented and proofed.•Seru productivity depends on ...the sum of workers’ speed and product workload.•Proof of stationary system behaviour for any number of workers and product types.•Case study to exemplify the model use and to study the dynamic system behaviour.
Advanced production environments emerged as the good solution to address the modern market challenges asking for a wide product mix and low time to market. Within cellular systems, made of independent, modular and flexible working areas, tailored on families of similar products, Serus are of increasing adoption for both manufacturing and assembly tasks. Among them, the so-called divisional Serus are the first step to move from the traditional production lines to a production environment made of a set of identical working areas, parallelising activities and enabling potential productivity increase. Despite their adoption in industry, starting from the electronic sector and moving forward, reference analytic models to predict divisional Seru productivity are rare in the literature, while their formulation and application is a gap to fill. This paper addresses this gap in theory, supporting the transition toward Seru production environment by proposing and proofing the analytic closed-form expressions getting the expected productivity of a divisional Seru made of a generic number of workers and a) one (base case), b) two (extension) and c) a generic number (general case) of product types to produce. Together with the steps to get the productivity expressions for these three cases of immediate practical applicability and not yet proposed by the literature, a case study and sensitivity analysis on the divisional Seru dimension showcase the proposed model industrial use and impact on the expected productivity. Key results highlight a stationary behaviour of the working time for all workers making the Seru productivity dependent on the sum of the workers speed and the product type workloads.
The development of predictive approaches to estimate supplier delivery risks has become vital for companies that rely heavily on outsourcing practices and lean management strategies in the era of the ...shortage economy. However, the literature that presents studies proposing the development of such approaches is still in its infancy, and several gaps have been found. In particular, most of the current studies present approaches that can only estimate whether suppliers will be late or not. Moreover, even if autocorrelation in data has been widely considered in demand forecasting, it has been neglected in supplier delivery risk predictions. Finally, current approaches struggle to consider macroeconomic data as input and rely mostly on machine learning models, while deep learning ones have rarely been investigated. The main contribution of this study is thus to propose a new approach that for the first time simultaneously adopts a deep learning model able to capture autocorrelation in data and integrates several macroeconomic indicators as input. Furthermore, as a second contribution, the performance of the proposed approach has been investigated in a real automotive case study and compared with those studies resulting from approaches that adopt traditional statistical models and models that do not consider macroeconomic indicators as additional inputs. The results highlight the capabilities of the proposed approach to provide good forecasts and outperform benchmarks for most of the considered predictions. Furthermore, the results provide evidence of the importance of considering macroeconomic indicators as additional input.
Industry 4.0 emerged in the last decade as the fourth industrial revolution aiming at reaching greater productivity, digitalization and operational efficiency standard. In this new era, if compared ...to automated assembly systems, manual assembly systems (MASs) are still characterized by wide flexibility but poor productivity levels. To reach acceptable performances in terms of both productivity and flexibility, higher automation levels are required to increase the skills and capabilities of the human operators with the aim to design next-generation assembly systems having higher levels of adaptivity and collaboration between people and automation/information technology. In the current literature, such systems are called adaptive automation assembly systems (A3Ss). For A3Ss, few design approaches and industrial prototypes are available. This paper, extending a previous contribution by the Authors, expands the lacking research in the field and proposes a general framework guiding toward A3S effective design and validation. The framework is applied to a full-scale prototype, highlighting its features together with the technical- and human-oriented improvements arising from its adoption. Specifically, evidence from this study show a set of benefits from adopting innovative A3Ss in terms of reduction of the assembly cycle time (about 30%) with a consequent increase of the system productivity (about 45%) as well as relevant improvements of ergonomic posture indicators (about 15%). The definition of a general framework for A3S design and validation and the integration of the productivity and ergonomic analysis of such systems are missing in the current literature, representing an element of innovation. Globally, this research paper provides advanced knowledge to guide research, industrial companies and practitioners in switching from traditional to advanced assembly systems in the emerging Industry 4.0 era matching current industrial and market features.
The pervasive digital innovation of the last decades has led to a remarkable transformation of maintenance strategies. The data collected from machinery and the extraction of valuable information ...through machine learning (ML) have assumed a crucial role. As a result, data-driven predictive maintenance (PdM) has received significant attention from academics and industries. However, practical issues are limiting the implementation of PdM in manufacturing plants. These issues are related to the availability, quantity, and completeness of the collected data, which do not contain all machinery health conditions, are often unprovided with the contextual information needed by ML models, and are huge in terms of gigabytes per minute. As an extension of previous work by the authors, this paper aims to validate the methodology for streaming fault and novelty detection that reduces the quantity of data to transfer and store, allows the automatic collection of contextual information, and recognizes novel system behaviors. Five distinct datasets are collected from the field, and results show that streaming and incremental clustering-based approaches are effective tools for obtaining labeled datasets and real-time feedback on the machinery’s health condition.
Ergonomics is a key factor in the improvement of health and productivity in workplaces. Its use in improving the performance of a manufacturing process and its positive effects on productivity and ...human performance is drawing the attention of researchers and practitioners in the field of industrial engineering. This paper proposes an ergonomic design approach applied to an innovative prototype of an adaptive automation assembly system (A3S) equipped with Microsoft Kinect™ for real-time adjustment. The system acquires the anthropometric measurements of the operator by means of the 3-D sensing device and changes its layout, arranging the mobile elements accordingly. The aim of this study was to adapt the assembly workstation to the operator dimensions, improving the ergonomics of the workstation and reducing the risks of negative effects on workers’ health and safety. The case study of an assembly operation of a centrifugal electric pump is described to validate the proposed approach. The assembly operation was simulated at a traditional fixed workstation and at the A3S. The shoulder flexion angle during the assembly tasks at the A3S reduced between 18% and 47%. The ergonomic risk assessment confirmed the improvement of the ergonomic conditions and the ergonomic benefits of the A3S.
Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. ...The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time.
In the last decades, Reconfigurable Manufacturing Systems (RMSs) rose as an emerging manufacturing strategy matching the modern industrial and market requirements asking for a wide variety of ...products in flexible batches. A traditional reconfigurable manufacturing environment consists of dynamic cells, called Reconfigurable Machine Cells (RMCs), including a set of machines called Reconfigurable Machine Tools (RMTs). Such machines are characterized by fixed elements, i.e., basic modules, and dynamic elements, i.e., auxiliary modules, allowing them to perform different operations. Despite their automation level, these systems require the intervention of the human operators in performing specific tasks, e.g., handling of the auxiliary modules from the warehouse to the RMTs and their assembly/disassembly to/from the RMTs. This issue rises relevant ergonomic and safety questions due to the human–machine collaboration. Following this stream, this paper proposes and applies a bi-objective optimization model for the design and management of RMSs. The technical objective function minimizes the reconfiguration time, i.e., the time needed to equip the RMTs with the required auxiliary modules, and the part and auxiliary module travel time among the RMCs. The ergonomic objective function minimizes the repetitive movements performed by the human operators during the working activities according to the ISO 11228-3 standard. Results show the existence of a good trade-off between the two objective functions, proving the possibility to improve the ergonomic conditions of the human operators without excessively increasing the total time needed for RMTs reconfiguration and for part and auxiliary module travelling.
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•Extensive review of reconfigurable manufacturing systems (RMSs) from 1999 to 2017.•Proposal of a schematic outlining five research streams of major interest.•Discussion of major ...features of RMSs with models and methods to address them.•Integration of reconfigurability concept to Industry 4.0 principles.•Proposal of open questions to encourage future research.
The current manufacturing environment aims at getting an increasing variety of customised, high-quality products in flexible batches. The dynamic market demand, the short product lifecycle and the flexibility need mark the transition from the traditional manufacturing systems to the so-called Next Generation Manufacturing Systems (NGMSs). Reconfigurable Manufacturing Systems (RMSs) are within NGMSs and seem to match to these current market trends. RMSs allow rapid change in structure, hardware and software configuration to adjust, promptly, their production capacity and functionality.
This paper presents a structured and updated systematic review of the literature about RMSs, highlighting the application areas as well as the key methodologies and tools. The review further provides a schematic of RMS research, identifying five emerging and promising research streams ranging from conceptual models to empirical applications. Compared to previous reviews, focusing on specific aspects of the RMS design and management, this study covers multiple areas and topics and it links reconfigurable manufacturing to the upcoming Industry 4.0 fourth industrial revolution. Finally, important issues and new trends in the literature are outlined to stimulate researchers and practitioners in developing studies in this field strongly linked to the Industry 4.0 environment.
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