168 research outputs found

    Adaptive Multi-Priority Rule Approach To Control Agile Disassembly Systems In Remanufacturing

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    End-of-Life (EOL) products in remanufacturing are prone to a high degree of uncertainty in terms of product quantity and quality. Therefore, the industrial shift towards a circular economy emphasizes the need for agile and hybrid disassembly systems. These systems feature a dynamic material flow. Besides that, they combine the endurance of robots with the dexterity of human operators for an effective and economically reasonable EOL-product treatment. Moreover, being reconfigurable, agile disassembly systems allow an alignment of their functional and quantitative capacity to volatile production programs. However, changes in both the system configuration and the production program to be processed call for adaptive approaches to production control. This paper proposes a multi-priority rule heuristic combined with an optimization tool for adaptive re-parameterization. First, domain-specific priority rules are introduced and incorporated into a weighted priority function for disassembly task allocation. Besides that, a novel metaheuristic parameter optimizer is devised to facilitate the adaption of weights in response to evolving requirements in a reasonable timeframe. Different metaheuristics such as simulated annealing or particle swarm optimization are incorporated as black-box optimizers. Subsequently, the performance of these metaheuristics is meticulously evaluated across six distinct test cases, employing discrete event simulation for evaluation, with a primary focus on measuring both speed and solution quality. To gauge the efficacy of the approach, a robust set of weights is employed as a benchmark. Encouragingly, the results of the experimentation reveal that the metaheuristics exhibit a notable proficiency in rapidly identifying high-quality solutions. The results are promising in that the metaheuristics can quickly find reasonable solutions, thus illustrating the compelling potential in enhancing the efficiency of agile disassembly systems

    Management of information projects. SCRUM technology. Guidelines for the study of the subject "informatics project management"

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    Purpose of the Scrum Guide This is a methodical guide intended for advanced study of the lecture material. Scrum is a framework for developing, delivering, and sustaining complex products. This Guide contains the definition of Scrum. This definition consists of Scrum’s roles, events, artifacts, and the rules that bind them together

    Autonomous order dispatching in the semiconductor industry using reinforcement learning

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    Cyber Physical Production Systems (CPPS) provide a huge amount of data. Simultaneously, operational decisions are getting ever more complex due to smaller batch sizes, a larger product variety and complex processes in production systems. Production engineers struggle to utilize the recorded data to optimize production processes effectively because of a rising level of complexity. This paper shows the successful implementation of an autonomous order dispatching system that is based on a Reinforcement Learning (RL) algorithm. The real-world use case in the semiconductor industry is a highly suitable example of a cyber physical and digitized production system

    An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains

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    Choosing the right supplier has a significant impact on the efficiency and productivity of a supply chain (SC). Different supplier selection models and approaches have been developed for reverse SCs. The lean, agile, resilient, and green (LARG) strategy is an innovative paradigm for supply chain competitiveness and sustainability. This study proposes a fuzzy-based methodology that integrates the fuzzy group best-worst method (FG-BWM) and the fuzzy combined compromise solution (FCoCoSo) method for supplier selection in reverse SCs within a LARG strategic paradigm. The FG-BWM is used to measure the importance weights of the supplier selection criteria. Subsequently, FCoCoSo is coupled with the normalized weighted geometric Bonferroni mean functions to select the most suitable supplier. The BWM is used for its simplicity and efficacy. The CoCoSo model is used for its unique ability to produce a compromise solution. The Bonferroni functions are used to capture the interrelationships among the decision attributes and eliminate the influence of extreme data. The main proposed framework aims at providing an easy-to-implement but reliable method to help manufacturers active in recycling and concerned with sustainability issues to rank and select suppliers. We present a real-world case study whose results demonstrate the applicability of the proposed integrated framework to supplier selection in reverse SCs, focusing in particular on the wood and paper industry

    (Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies—the case of nanomaterials

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    The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system

    Review of selection criteria for sensor and actuator configurations suitable for internal combustion engines

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    This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture
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