1,721,068 research outputs found
A Markov chain-based approach to model the variance of times-to-failure and times-to-repair in manufacturing systems
Full text linkThe development of manufacturing systems with high level of automation is thrust by high-volume demand for heterogeneous engineered products. The paper focuses on the usage of Phase-type distributions in the description of reliability parameters, both times-to-failure (TTFs) and times-to-repair (TTRs), for a workstation with several failure modes. Differently from classical analytical models based on exponential distributions, the variance of reliability parameters can be exactly captured, allowing a sounder performance evaluation of the production system in which the workstation operates. While state-of-the-art research works adopt single-station models accounting for variance of TTR and/or TTF of a single failure mode, the presented model framework can capture the variance of TTRs and TTFs of all workstation failure modes, or only a portion of them. The formalized approach has been validated against a simulator replicating the workstation behavior, grounding on data acquired from the field. The application on an industrial case study showed the numerical impact of accounting for the actual variance on performance evaluation, exploiting an asynchronous continuous model of two machines-one buffer line, with finite buffer capacity and deterministic processing times. Further developments may concern the integration of the model in Markov chain-based analytical models of longer manufacturing lines
A Markov Chain model for the performance evaluation of manufacturing lines with general processing times
Full text linkThis paper presents a Markov Chain approximation to model stations in manufacturing lines with general distributed processing times. The proposed Markov Chain approximation enables the use of continuous flow models for the performance evaluation of serial lines with finite buffers and mixed manual - automated operations. Each station in the line can consist of a highly automated machine with deterministic processing times, or of a human operator performing manual operations with general distributed processing times. Stations with random processing times are modelled through a continuous time - discrete state Markov Chain characterized by an operational state with a deterministic processing time, and by an auxiliary down state used to stochastically dilate the overall completion time of a part on the station. The Markov Chain parameters are defined through moments fitting of the probability distribution of the processing time of the original station. The resulting Markov Chain represents the behavior of the station in isolation and is then used as input in the decomposition techniques, based on continuous flow models, for the performance evaluation of serial lines. The model has been applied in the analysis of the production performances of a real assembly line
Switching- and hedging- point policy for preventive maintenance with degrading machines: application to a two-machine line
Full text linkMaintenance and production are frequently managed as separate activities although they do interact. Disruptive events such as machine failures may find the company unready to repair the machine immediately leading to time waste. Preventive Maintenance may be carried out and maintenance time reduced to the effective task duration, in order to prevent time waste. Companies and researchers have been focusing on policies able to mitigate the impact of Preventive Maintenance on system availability, by exploiting the knowledge about degradation profiles in machines and the joint information from the machine state and the buffer level. In this work, the mathematical proof of the optimal threshold-based control policy for Preventive Maintenance with inventory cost, maintenance cost, backlog cost is provided. The control policy is defined in terms of buffer thresholds and dependency of the thresholds on the degradation condition. The optimal control policy is proved to include a combination of switching points and hedging points, where the first ones activate the Preventive Maintenance for a given condition and the latter ones control the production rate in order to minimize the surplus. An extensive experimental campaign analyzes the impact of system parameters such as the Maintenance duration on the cost function. The results show that there exists cases in which the optimal policy is dominated by the effect of the hedging points or the switching points, alternatively. Therefore, the proposed method is used to provide suggestions to the management for operative decisions, in order to choose the policy fitting best the system
Defining Logistics Parameters In The Drug Replenishment Of Hospital Systems By Means Of Simulation
No full textA review of different approaches to the FMS loading
Full text linkLoading in flexible manufacturing systems (FMSs) is affected by the characteristics of the FMS under analysis, by the type of plant where the FMS is introduced, and by the production planning hierarchy where the loading module operates. We propose an analysis of the various aspects that influence the prob- lem formulation, identifying the alternatives available in real systems and possible future evolutions. We then provide a survey of different approaches proposed in the literature to tackle the loading problem. Articles are classified according to the type of FMS analyzed, the objective function, and the constraints. Finally, based on our analysis, we suggest some problem issues which need to be addressed, and also directions for future research
A project scheduling approach to production and materialrequirement planning in Manufacturing-to-Order environments
No full textDigital supply chains for ecosystem resilience: a framework for the Italian case
Full text linkContingency where exogenous and dramatic factors (i.e. Covid) impact not only on political and social life but also on economy is changing the way business is managed. Grounded on recent works studying the relationship between digitalisation and resilience, this work aims to systematize the links between the two dimensions at Supply Chains (SC) and at ecosystem level. A conceptual framework for manufacturing companies and policy makers is proposed to cope with disruptions thanks to digital technology implementation. The work is based on the results of an explorative analysis held with the support of practitioners from the manufacturing sector, IT providers and policy makers in Italy to systematise results and to demonstrate that public–private partnership can help to face disruptions. This paper contributes to the theory of ecosystems to establish a systemic framework to go beyond the border of each SC proposing a cross-collaboration model
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