Simulation in production system life cycle

Košturiak, Ján; Gregor, Milan

doi:10.1016/s0166-3615(98)00116-x

Cited by 43 publications

(21 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of running simulation models are useful to plan for longterm demand and demand variations to determine plant capacity requirements, and inventory levels. The main goal identified by Košturiak et al [80] is that in a production system design, the planned and actual requirements and resources are correlated as much as possible. To facilitate this undertaking, Chomiakow [81] used system dynamics to develop a generic pattern for modeling manufacturing plants that includes factors such as production capacity and inventory, demand and sales, costs, and sales price.…”

Section: Computer Simulationmentioning

confidence: 99%

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Kibira

Morris

Kumaraguru

2016

J. RES. NATL. INST. STAN.

View full text Add to dashboard Cite

The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data to guide decision-making is, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital. The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. It then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

show abstract

Section: Computer Simulationmentioning

confidence: 99%

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Kibira

Morris

Kumaraguru

2016

J. RES. NATL. INST. STAN.

View full text Add to dashboard Cite

show abstract

“…The results of running simulation models are useful to plan for long-term demand and demand variations to determine plant capacity requirements, and inventory levels. The main goal identified by Košturiak et al (1999) is that in a production system design, the planned and actual requirements and resources are correlated as much as possible. To facilitate this undertaking, Chomiakow (2007) used system dynamics to develop a generic pattern for modeling manufacturing plants that includes factors such as production capacity and inventory, demand and sales, costs, and sales price.…”

Section: Computer Simulationmentioning

confidence: 99%

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Kibira

Morris

Kumaraguru

2015

View full text Add to dashboard Cite

The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data and subsequent distribution of extracted information to guide decisionmaking throughout the enterprise necessitate, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital.The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. This paper then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

show abstract

“…As far back as 1999, Kosturiak and Gregor [5] argued for a new kind of decision support system for rapidly changing environments. They summarized how discrete-event simulation can be used in the design, operation, and continuous improvement of complex manufacturing and logistical systems.…”

Section: Introductionmentioning

confidence: 99%

Scheduling rules for a small dynamic job-shop: a simulation approach

Dileepan¹,

Ahmadi²

2010

Int. j. simul. model.

View full text Add to dashboard Cite

The objective of this paper is to study a selected set of scheduling criteria commonly used in scheduling research and develop a set of simple rules that can be easily implemented in relatively small dynamic job-shops. Simulation models were developed for two dynamic production shop environments, namely, a job-shop with jumbled flow and a job-shop with limited jumbled flow. Five scheduling criteria were used with each of the three models. They are, (i) Slack per Remaining Operations (SPRO), (ii) Priority Ratio (PR), (iii) Earliest Due Date (EDD), (iv) Total Work Content Remaining (TWKR), and (v) Smallest Processing Time (SPT). In each of these cases, performance statistics relating to work-in-process (WIP) inventory and job lateness were gathered. The results show that in most cases, simple rules such as SPT and EDD outperform the other rules. SPT rule minimizes the performance metrics that measure WIP inventory levels and the EDD rule minimizes the performance metrics that measure deviation of job completion time from the promised due date.

show abstract

Simulation in production system life cycle

Cited by 43 publications

References 0 publications

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Methods and Tools for Performance Assurance of Smart Manufacturing Systems

Scheduling rules for a small dynamic job-shop: a simulation approach

Contact Info

Product

Resources

About