Analysis of a two-stage, flexible production system with unreliable machines, finite buffers and non-negligible setups

Senanayake, C.D.; Subramaniam, Velusamy

doi:10.1007/s10696-011-9115-2

Cited by 7 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNC machine tools as the fundamental and crucial equipment for most manufacturing systems have been chosen as the element of the workstation of FMS in the current paper. The storage system, such as an intermediate buffer, is not considered in this article since the trend of FMS is to reduce or even cancel the cache area [45]. An FMS is capable of manufacturing various kinds of product families through CNC machine tools, which are loaded and unloaded by robots [46].…”

Section: Multistate Model Of the Flexible Manufacturing Systemmentioning

confidence: 99%

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

et al. 2019

View full text Add to dashboard Cite

This article presents a performance evaluation of a flexible manufacturing system (FMS), in which the system and its components can have different performance levels ranging from perfect functioning to total failure. An original multistate transition process for the FMS’s main component, Computerized Numerical Control (CNC) machine tools, proposed in the current article and treated as an aging process because of its increasing failure rate is investigated. The Markov reward approach is utilized to analyze the availability of CNC machine tools and sensitivity analysis of each non-operational state to the machine’s downtime. To overcome the “state explosion problem” in the subsequent FMS performance evaluation process, an application of the Lz-transform method is proposed to determine the performance evaluation and availability of such aging multistate FMS. An illustrated case is presented to demonstrate the performance evaluation process. The results show that the suggested methods can be implemented in engineering decision making and construction of various aging multistate FMS related to demands, availability, and output performance.

show abstract

Section: Multistate Model Of the Flexible Manufacturing Systemmentioning

confidence: 99%

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Quantitative analysis of production lines includes the line balancing problem (Becker & Scholl, 2006), the buffer allocation problem (Charharsooghi & Nahavandi, 2003), queuing network performance and blocking (Govil & Fu, 1999;Li et al, 2009). Literature also discusses these concepts explored with the addition of flexible manufacturing systems with varying configurations (serial, sequential, work-cells) and product types (Senanayake & Subramaniam, 2013;So, 1989).…”

Section: Literature Reviewmentioning

confidence: 99%

Buffer clustering policy for sequential production lines with deterministic processing times

Schuler¹,

Darabi²

2016

10.5267/j.ijiec

View full text Add to dashboard Cite

A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1-buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.

show abstract

“…Chan et al proposed a new two-level hedging point policy for the optimal production control problem of a multiple product types and uncertain demands manufacturing system [7]. Senanayake et al analyzed a Markov model of a two-stage production system capable of producing two product types and described a solution method to evaluate the system performance [8]. Mok proposed a search-type algorithm to obtain the optimal hedging points for a multiple machines and multiple product types complex manufacturing system [9], which provided a new research direction for the production cost control of the complex manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Control Policy for a Multiple Machines and Multiple Product Types Manufacturing System Production Process with Uncertain Fault

You

Guo

et al. 2020

Processes

View full text Add to dashboard Cite

The optimization of production cost has always been a key issue in manufacturing systems; for the single product type manufacturing systems, lots of research studies have proved the validity of the hedging point control policy in production cost control. However, due to the complexity of the multiple machines and multiple product types manufacturing systems with uncertain fault, it is difficult to achieve a good control effect only by using the hedging point control policy. To optimize the total production cost under constantly changing demands, an integrated control policy that combines the prioritized hedging point (PHP) control policy with the production capacity planning during production is proposed, and the decision variables are obtained by a particle swarm optimization (PSO) algorithm. The simulation experiments show the effectiveness of the proposed integrated control policy in production cost control for the multiple machines and multiple product types manufacturing system.

show abstract

Analysis of a two-stage, flexible production system with unreliable machines, finite buffers and non-negligible setups

Cited by 7 publications

References 16 publications

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

Lz-Transform Method and Markov Reward Approach for Flexible Manufacturing System Performance Evaluation

Buffer clustering policy for sequential production lines with deterministic processing times

Integrated Control Policy for a Multiple Machines and Multiple Product Types Manufacturing System Production Process with Uncertain Fault

Contact Info

Product

Resources

About