Production Planning and WIP Assignment for Wafer Fabrication Tools With Availability Constraints

Perlman‬‏, ‪Yael; Crispil, Elad; Kalir, Adar A.

doi:10.1109/tsm.2015.2439951

Cited by 6 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hajba and Horvá th (2014) constructed mixed integer linear programming (MILP) models to optimize the real production systems without considering the product departure halfway. Perlman et al (2015) proposed a cost-based optimization model that seeks to minimize the cost. However, most of the previous mathematical models consider that the production systems have infinite buffer capacities so that products' queue time and queue length are not explicitly addressed (Dhouib et al, 2009).…”

mentioning

confidence: 99%

Performance analysis and optimization of queueing network production systems considering non-conforming products rework and departure

Zhang

2018

JAMDSM

View full text Add to dashboard Cite

During the past decades, the production performance analysis and optimization have played a significant role in queueing network system production planning and operation design for the manufacturing plants, but how to model the optimized production system performance and solve it is facing great challenges. Since the more and more wide use of queueing network, the non-conforming product scenarios such as rework and departure, cause mass random interference. These problems when modelled in the conventional analysis and optimization models are extremely difficult to solve as they are coupled and NP-hard problems. This paper considers a modified M/M/n/m-first come first service (M/M/n/m-FCFS) queueing network approximation model that is developed to maximize utilization and profit by analysing the queue time and queue length of the products under the constraint conditions. A hybrid meta-heuristics is applied to solve the approximation model, both the service rate and buffer allocation of the queueing network system can be determined. Finally, to assess the effectiveness of the proposed method, extensive numerical experimental results from the approximation model are compared to the ARENA simulation, and sensitivity and validation analysis are also conducted, which prove it accurate, believable, and effective.Keywords : Production planning, Operation design, Analysis and optimization, Queueing network, Non-conforming product, Hybrid meta-heuristics, Approximation model IntroductionMost manufacturing companies are facing the challenge of quickly implementing flexible and complex production systems (Walid and Ali, 2002). But the production systems often experience worse performance for the unavoidable variability exists in reality. This phenomenon appears widely in queueing network and multi-product mixed production system. Performance analysis and optimization problems in queueing systems have received considerable attention in recent years, due to their wide applicability in the performance evaluation and optimal design of manufacturing systems and computer networks (Song et al., 1998). Now, the long serial lines analysis and optimization have been well studied using decomposition (Shi and Gershwin, 2014;Damodaran and Hulett, 2012;Cauffriez and Willaeys, 2006) or aggregation (Veeger et al., 2011;Belmansour and Nourelfath, 2008) methods for a long time, and extensive results are obtained. Although many studies have focused on solving the line optimization problems, the studies in which a large number of approaches are currently applied to research the performance of queueing network systems considering the non-conforming products are still extremely limited.Research in the field of performance optimization tends to focus on issue models, such as cycle time (CT) optimization (Gilenson et al., 2015), throughput (TH) optimization (Law et al., 2008), work-in-process (WIP) optimization (Reyes et al., 2013;Qiu, 2005), and buffer allocation optimization (Shihabudin et al., 2015;Nahas, 2014). The published literature...

show abstract

mentioning

confidence: 99%