Stochastic Maintenance Opportunity Windows for Serial Production Line

2015 ◽  
Author(s):  
Jing Zou ◽  
Qing Chang ◽  
Yong Lei ◽  
Guoxian Xiao ◽  
Jorge Arinez
Keyword(s):  
Stochastic Model ◽  
Energy Saving ◽  
Manufacturing Systems ◽  
Production Control ◽  
Production Systems ◽  
Critical Element ◽  
Serial Production ◽  
Manufacturing Plants ◽  
Future Prediction ◽  
Saving Mode

Timely performance of preventive maintenance (PM) tasks is a critical element of manufacturing systems. The current PM at most manufacturing plants is to conduct maintenance tasks during non-production shifts, breaks, or other scheduled downtime. This practice may introduce unnecessary extra labor and overhead costs. Therefore, there is need to look for hidden maintenance opportunities to perform PM tasks during normal production time without impacting throughput. Additional benefit of the hidden opportunity window is that it can be translated to hidden energy saving opportunity during which machines can be strategically shut down or turned to energy saving mode while PM can be performed. Since production schedules are always made beforehand, the development of opportunity window and the downtime schedule need future prediction about the production system. In this paper, a stochastic model of the downtime opportunity on serial production systems is developed. Based on the stochastic model, revised opportunity window and recovery time is defined, which can help on production control on deciding when and where to insert the downtime events and the duration of the downtime events for the incoming process.

Stochastic Energy Opportunity Windows in Advanced Manufacturing Systems

2015 ◽  
Author(s):  
Yang Li ◽  
Qing Chang ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Energy Saving ◽  
Manufacturing Systems ◽  
Production Control ◽  
Environmental Concern ◽  
Markov Chain Model ◽  
Cost Effective ◽  
Chain Model ◽  
Global Competitiveness ◽  
Improve Energy Efficiency ◽  
Multistage Manufacturing

To improve energy efficiency is becoming more and more critical for manufacturing enterprises because of the rising energy costs, increased global competitiveness, environmental concern and more government regulations. Production control has been considered as one of the most cost-effective methods to achieve the goal. This paper discusses the energy saving opportunities in a multistage manufacturing system through strategically shut down machines to reduce their running time. We start from the investigation on what is the longest time that machines can be shut down or turn to energy saving mode without affecting system production. Then, energy opportunity windows (EOWs) of machines are defined. A Markov chain model is developed to estimate the EOWs. A case study is conducted to demonstrate the proposed model and its potential on energy saving in multistage manufacturing systems.

Event-Based Production Control for Energy Efficiency Improvement in Sustainable Multistage Manufacturing Systems

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Yang Li ◽  
Jun-Qiang Wang ◽  
Qing Chang
Keyword(s):  
Energy Efficiency ◽  
Energy Saving ◽  
Energy Cost ◽  
Manufacturing Systems ◽  
Production Control ◽  
Control Method ◽  
Sustainable Manufacturing ◽  
Environmental Crisis ◽  
System Level ◽  
Event Based

There has been an increasing trend for manufacturers to shift toward sustainable manufacturing strategies in response to an ever-growing pressure from fluctuating energy price and environmental crisis. Reducing energy consumption is considered as an important step to achieve the sustainability of a production system. This paper proposes an event-based control methodology to improve the production energy efficiency through strategically switching appropriate stations to energy saving mode. Based on an event-based analysis of production dynamics, an analytical approach is developed to quantitatively predict the system level production loss resulted from an energy saving control event (ESCE). A genetic-based control algorithm is proposed to balance the trade-off between the gain from energy saving and the expense of throughput loss. The energy improvement analysis results in a fundamental understanding of production energy dynamics and a significant decrease of energy cost for a manufacturing facility. Numerical case studies are performed to validate the effectiveness of the proposed method. It is found that the control method can effectively reduce energy cost, while only slightly impacting production.

scholarly journals Worker Movement Diagram Based Stochastic Model of Open Paced Conveyors

2018 ◽  
Vol 46 (2) ◽  
pp. 55-62 ◽  
Author(s):  
Tamás Ruppert ◽  
János Abonyi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Resources ◽  
Stochastic Model ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Benchmark Problem ◽  
Stochastic Nature ◽  
Moving Patterns ◽  
Open Nature

Abstract Human resources are still utilized in many manufacturing systems, so the development of these processes should also focus on the performance of the operators. The optimization of production systems requires accurate and reliable models. Due to the complexity and uncertainty of the human behavior, the modeling of the operators is a challenging task. Our goal is to develop a worker movement diagram based model that considers the stochastic nature of paced open conveyors. The problem is challenging as the simulator has to handle the open nature of the workstations, which means that the operators can work ahead or try to work off their backlog, and due to the increased flexibility of the moving patterns the possible crossings which could lead to the stopping of the conveyor should also be modeled. The risk of such micro-stoppings is calculated by Monte-Carlo simulation. The applicability of the simulator is demonstrated by a well-documented benchmark problem of a wire-harness production process.

scholarly journals A novel analysis model for optimization performance of Bernoulli serial production system considering rework processes

2019 ◽  
Vol 234 (1-2) ◽  
pp. 295-309
Author(s):  
Binghai Zhou ◽  
Song Lin
Keyword(s):  
Quality Management ◽  
Production System ◽  
Manufacturing Systems ◽  
Production Systems ◽  
System Modeling ◽  
System Level ◽  
Recent Analysis ◽  
Analysis Model ◽  
Serial Production ◽  
Traditional Approaches

Production system modeling aims to investigate the principles of production procedures and to reveal the relationship between components and systems. Tremendous efforts have been devoted to production system modeling for the serial production system. However, most of the research focuses on the analysis of the systems at the steady state. Due to the emphasis of the quality management, production systems with rework loops are widely used in today’s manufacturing industrials, which the traditional approaches are not applicable to. Since the recent analysis of transients shows significant value and great potential in manufacturing systems, in this article, a new mechanism for rework is introduced based on the principles of quality management and lean production. A novel “Instant-Checking” method is developed to model Bernoulli serial production system considering rework loops. This method overcomes conventional restrictions and limited assumptions, and it extends the problem to systems with complex structures. Meanwhile, the analysis for transients is conducted to demonstrate relationships between component- and system-level characteristics. Finally, numerical experiments are performed to verify the effectiveness of the model.

ENERGY-SAVING MODE OF OPERATION OF THE ELECTRIC DRIVE OF THE OIL CROPS CHOPPER

2019 ◽  
Vol 5 (1) ◽  
pp. 8-12
Author(s):  
Matkarim Ibragimov ◽  
◽  
Gulnoz Ergasheva
Keyword(s):  
Energy Saving ◽  
Electric Drive ◽  
Oil Crops ◽  
Mode Of Operation ◽  
Saving Mode

scholarly journals Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 3-8
Author(s):  
Marvin Carl May ◽  
Lars Kiefer ◽  
Andreas Kuhnle ◽  
Nicole Stricker ◽  
Gisela Lanza
Keyword(s):  
Economic Model ◽  
Production Control ◽  
Production Systems ◽  
Multi Agent ◽  
Matrix Production

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Queue Length Forecasting in Complex Manufacturing Job Shops

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 322-338
Author(s):  
Marvin Carl May ◽  
Alexander Albers ◽  
Marc David Fischer ◽  
Florian Mayerhofer ◽  
Louis Schäfer ◽  
...  
Keyword(s):  
Operations Management ◽  
Queue Length ◽  
Manufacturing Systems ◽  
Production Control ◽  
Data Sets ◽  
Job Shops ◽  
Static Data ◽  
Queue Lengths ◽  
Operational Excellence

Currently, manufacturing is characterized by increasing complexity both on the technical and organizational levels. Thus, more complex and intelligent production control methods are developed in order to remain competitive and achieve operational excellence. Operations management described early on the influence among target metrics, such as queuing times, queue length, and production speed. However, accurate predictions of queue lengths have long been overlooked as a means to better understanding manufacturing systems. In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based queue forecasting models. Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. The main findings concluded that accurate queue length prediction, even with minimal available data, is feasible by applying a variety of techniques, which can enable further research and predictions.

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