Switching- and hedging- point policy for preventive maintenance with degrading machines: application to a two-machine line

Maintenance 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

Catalytic Tri-reforming of Biomass-Derived Syngas to Produce Desired H2:CO Ratios for Fuel Applications

This study focuses on upgrading biomass derived syngas for the synthesis of liquid fuels using Fischer-Tropsch synthesis (FTS). The process includes novel gasification of biomass via a tri-reforming process which involves a synergetic combination of CO2 reforming, steam reforming, and partial oxidation of methane. Typical biomass-derived syngas H2:CO is 1:1 and contains tars that deactivate FT catalyst. This innovation allows for cost-effective one-step production of syngas in the required H2:CO of 2:1 with reduction of tars for use in the FTS. To maximize the performance of the tri-reforming catalyst, an attempt to control oxygen mobility, thermal stability, dispersion of metal, resistance to coke formation, and strength of metal interaction with support is investigated by varying catalyst synthesis parameters. These synthesis variables include Ce and Zr mixed oxide support ratios, amount Mg and Ni loading, and the preparation of the catalyst. Reaction conditions were also varied to determine the influences reaction temperature, gas composition, and GHSV have on the catalyst performance. Testing under controlled reaction conditions and the use of several catalyst characterization techniques (BET, XRD, TPR, XAFS, SEM-EDS, XPS) were employed to better explain the effects of the synthesis parameters. Applications of the resulting data were used to design proof of concept solar powered BTL plant. This paper highlights the performance of the tri-reforming catalyst under various reaction conditions and explains results using catalyst characterization

Evaluation of Material Shortage Effect on Assembly Systems Considering Flexibility Levels

The global pandemic caused delays in global supply chains, and numerous manufacturing companies are experiencing a lack of materials and components. This material shortage affects assembly systems at various levels: process level (decreasing of the resource efficiency), system level (blocking or s tarvation of production entities), and company level (breaking the deadlines for the supplying of the products to customers or retailers). Flexible assembly systems allow dynamic reactions in such uncertain environments. However, online scheduling algorithms of current research are not considering reactions to material shortages. In the present research, we aim to evaluate the influence of material shortage on the assembly system performance. The paper presents a discrete event simulation of an assembly system. The system architecture, its behavior, the resources, their capacities, and product specific operations are included. The material shortage effect on the assembly system is compensated utilizing different system flexibility levels, characterized by operational and routing flexibility. An online control algorithm determines optimal production operation under material shortage uncertain conditions. With industrial data, different simulation scenarios evaluate the benefits of assembly systems with varying flexibility levels. Consideration of flexibility levels might facilitate exploration of the optimal flexibility level with the lowest production makespan that influence further supply chain, as makespan minimization cause reducing of delays for following supply chain entities

Supporting Interoperability of Virtual Factories

The manufacturing industry is entering a new era. This emerging era starts with the integration of new ICT technologies and collaboration applications into traditional manufacturing practices and processes, such as manufacturing 2.0. Manufacturing 2.0 has been conceptualised as a system that goes beyond the factory floor, and paradigms of “manufacturing as an ecosystem” have emerged. The virtual factory is one of the important concepts and foundations central to the realization of future manufacturing. In this paper, we take a look into the current research on virtual factories and propose a new approach to improve interoperability through the integration of different proprietary, legacy and existing solutions

Sustainable engineering master module - Insights from three cohorts of European engineering team

Mobility and transnational migration are current social developments among the population of the European Union. These developments in both society-at-large and companies, linked to the challenges of sustainability, lead to new requirements for working in the European Union. Teaching and learning in higher education needs to adapt to these requirements. As a result, new and innovative teaching and learning practices in higher education should provide competencies for transnational teamwork in the curriculum of tomorrow's engineers in order to ensure their competitiveness in the job market. A transnational project-oriented teaching and learning framework, which provides the future key competencies for young engineers was implemented in the course European Engineering Team (EET). Engineering students from four countries participated in a new project-based course that focused on the development of innovative and sustainable products and opportunities. The goal of this paper is to present results and lessons learnt from three cohorts of EET

A Markov Chain model for the performance evaluation of manufacturing lines with general processing times

This 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

Performance evaluation of two-machines line with multiple up and down states and finite buffer capacity

An analytical model for evaluating the performance of two-machine continuous flow system with finite capacity buffer, multiple up and down states is proposed in this paper. The expression multiple up and down states means that each machine can assume various working states and non working states. The idea underlying the proposed model is the definition of the state of the system as the product of the states of the two machines. The model provides a way to analyze a wide range of two-machine systems, including for example systems with phase-type failure and repair time distributions, series/parallel machines and quality control machines. Moreover, the proposed two-machine line can be used as a building block for the analysis of larger systems

Robust optimization of manufacturing systems flexibility

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order to be robust to changes in the production scenario. Therefore, manufacturing companies face a relevant risk when taking strategic decisions about which system resources should be acquired. This risk can be mitigated by exploiting performance evaluation models, such as analytical models and Discrete Event Simulation, that are effectively adopted to estimate the performance of possible system configurations. However, current decision-support tools for optimizing system configurations can be only loosely coupled with performance evaluation models, hence undermining the actual optimization of the system itself, even more if production requirements may evolve in the future. This work presents an analytical methodology to support the optimization of manufacturing systems configuration and reconfiguration subject to evolving production requirements. The methodology integrates a stochastic analytical model for performance evaluation of manufacturing lines into a mixed integer programming problem, by means of performance linearization. The advantage of using the proposed methodology is shown on a line configuration problem, where buffer capacities and machine capabilities have to be jointly optimized, in order to minimize costs and satisfy the target performance