| In this work, a Carbon Footprint (CF) calculator developed by authors to the tourism sector in Portugal, was validated. The CF calculator self-validation was based on the comparison of the results obtained with two tools available online (Carbon Footprint Ltd-CFL and Climate Care-CC) and with a Life Cycle Assessment (LCA) performed using the SimaPro PhD software. The calculator is based in the CO2e (carbon dioxide equivalent) emissions of 6 components: electricity, water, laundry, fuels, waste and food. These tools were applied to a 3 stars Hotel, located in Viseu region (Portugal) with 50 guestsrooms and an occupied area of 1500m2. CF results attained with calculator developed were slightly higher than CFL and lower than CC results, as it has a different emission from electricity componentFor LCA two scenarios were considered, Scenario 1 (same assumptions as CF calculator) and Scenario 2 (assumptions based on LCA methodology) a higher emission was obtained of more than 23975.7 kgCO2e/year and 72680 kgCO2e/year, respectively. For both scenarios the difference was caused by electricity consumption component and additional by fuel consumption in Scenario 2. The emission factors chosen used for each component were the main responsible for these differences. Self-validation process demonstrated that the CO2e emissions from the different tools were very similar when the same assumptions were considered, so the calculator is consistent. 

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Purpose - This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plug&Produce (P&P) systems in the presence of continuously changing production orders. Design/methodology/approach - The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for P&P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative. Findings - The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system. Originality/value - This paper addresses a less explored domain within the P&P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the P&P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.

This work introduces a phenomenographic analysis of the concept of flexibility in the domain of production science. Flexibility is a cornerstone in the education of industrial and production engineers; however, it still appears as a broadly and even inconsistently defined construct. In order to clarify what is or should be learnt, this work analyses first the established literature to extract a working characterisation of the flexibility concept. The resulting understanding is then used to represent the experts' perception of the topic, which in turn is used as the ideal level of understanding that a student should achieve herself/himself when studying such a concept. The second phase of the work aims at disclosing and classifying the multifaceted perceptions of flexibility that two classes of industrial engineering students have after two courses in which the focal concept of manufacturing flexibility has been presented using two different approaches. The research is based on a survey completed by students. The data collected have consequently been structured into a finite set of clusters according to: a) the level of understanding of the key concept; and b) the nature of the shown knowledge. The classification is, then, the basis for defining an epistemologically sound approach to develop suitable teaching and learning activities to ensure optimal acquisition of the concept of flexibility.

Sustainability is currently one of the biggest challenges and drivers of manufacturing industry. With traditional automation approaches becoming evermore inadequate to support sustainable mass customized production, the research focus is moving towards agile systems that enact companies with the ability to quickly reconfigure their shop-floors by seamlessly deploying or removing modules. Such systems are envisioned as key for attaining a profitable and sustainable industrial development. In this sense, this paper attempts to characterize an innovative approach that relies on bio-inspired concepts as the main control mechanism, in order to foster sustainability by attaining the necessary shop-floor agility. Furthermore an experimental setup is presented and the results are analysed, in order to understand the influence and impact of the main properties of the approach towards the system performance.

The word “flexibility” is often abused and not univocally understood within the manufacturing science domain and in particular in the context of industrial automation. Since the raise of industrial robots in the 1960’, different researchers and practitioners have been using such a common word with different meanings. This has generated a very articulated concept, spanning from capability of a system to increase the production volumes to ability to handle product mix variation. Several authors have tried to count the current meanings of such a word in manufacturing and someone arrived to more than 50!. In spite of this fuzziness in both the definition and scope, the concept of flexibility remain one of the cornerstones in the curriculum of industrial and production engineers, and it appears in many courses along the bachelor and master studies. The apparent paradox that higher education institutions have to teach things that are not even well-defined and agreed in the scientific world is, in fact, quite a usual practice. In order to clarify what is, or should be, learnt this work analyzes first the established literature to extract a “working” characterization of the flexibility concept. The resulting understanding is then used to represent the experts’ perception of the topic which in turn is used as ideal level of understanding that a student should achieve her/himself when studying such a concept.   

The second phase of the work aims at disclosing and classifying the multifaceted perceptions of flexibility that two different classes of industrial engineering students have after two courses in which the focal concept of manufacturing flexibility has been presented using two different approaches. The research is based on a phenomenographic analysis of a series of well-designed interviews to the students. The collected data have consequently been structured in a finite set of clusters according of: (1) the level of understanding of the key concept (as expressed in the Bloom’s taxonomy) and (2) the nature of the shown knowledge (as presented in the SOLO taxonomy). The classification is then the basis for defining an epistemological sound approach to develop suitable teaching and learning activities to ensure optimal acquisition of the concept of flexibility.

The word "flexibility" is often abused and not univocally understood within the manufacturing science domain and in particular in the context of industrial automation. Since the raise of industrial robots in the 1960', different researchers and practitioners have been using such a common word with different meanings. This has generated a very articulated concept, spanning from capability of a system to increase the production volumes to ability to handle product mix variation. Several authors have tried to count the current meanings of such a word in manufacturing and someone arrived to more than 50 [1]!. In spite of this fuzziness in both the definition and scope, the concept of flexibility remain one of the cornerstones in the curriculum of industrial and production engineers, and it appears in many courses along the bachelor and master studies. The apparent paradox that higher education institutions have to teach things that are not even well-defined and agreed in the scientific world is, in fact, quite a usual practice. In order to clarify what is, or should be, learnt this work analyzes first the established literature to extract a "working" characterization of the flexibility concept. The resulting understanding is then used to represent the experts' perception of the topic which in turn is used as ideal level of understanding that a student should achieve her/himself when studying such a concept. The second phase of the work aims at disclosing and classifying the multifaceted perceptions of flexibility that two different classes of industrial engineering students have after two courses in which the focal concept of manufacturing flexibility has been presented using two different approaches. The research is based on a phenomenographic analysis of a series of well-designed interviews to the students [2]. The collected data have consequently been structured in a finite set of clusters according of: (1) the level of understanding of the key concept (as expressed in the Bloom's taxonomy [3]) and (2) the nature of the shown knowledge (as presented in the SOLO taxonomy [4]). The classification is then the basis for defining an epistemological sound approach to develop suitable teaching and learning activities to ensure optimal acquisition of the concept of flexibility.

The diversity of requirements and the frequency of change in the market can only be competed with dynamicity and responsiveness in both production and planning systems. In this sense, working principles of a novel workload control method, called continuous precise workload control are presented in this paper. The implementation of the method is based on a multi-agent based architecture. The presented approach generates dynamic non periodic release decisions exploiting real time shop floor information. The performance of the system and correlation of norm value against the assessment range are investigated through an experimented test case.

Computational and communication capabilities are increasingly being used in all devices. In the production context this leads to the generation of massive amounts of data that are rarely proficuously used. More particularly the application of data-mining techniques to infer knowledge from systems’ operation to improve its design decisions remains fairly unexplored. This article presents an approach to extract system design and configuration rules from Evolvable Production Systems. Furthermore it provides the empirical results from two test-cases that support the hypothesis that a simulation-data-mining approach can help reducing the complexity of the work carried by system designers and production managers. 

Simulation has played an important role along the years to predict systems' behaviour before their deployment. In the case of self-organising mechatronic systems simulation tools can help researchers and practitioners understanding the full potential of the solution as well as its underlying limitations. Self-organising mechatronic systems have passed a feasibility study and presented promising results. However they are rarely explored in industry in part due to the lack of methods to support their design and configuration and the difficulty to predict the systems' behaviour before their deployment. Given the cost and development time associated with building self-organising mechatronic systems this research problem has been left quite unattended. In this article we present a tool that enables the creation and simulation of Evolvable Production Systems and their self-organising behaviour. The generated operational results can posteriorly be used to analyse the suitability of different design and configuration alternatives for different product types and volumes.