A comprehensive review of flowshop group scheduling literature

Neufeld, Janis S.; Gupta, Jatinder N. D.; Buscher, Udo

doi:10.1016/j.cor.2015.12.006

Cited by 106 publications

(37 citation statements)

References 158 publications

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“…Both Neufeld, Gupta, and Buscher (2016) and Janiak et al (2015) provide very good and recent reviews of this classic Resource Constrained Project Scheduling Problems with detailed comparison of the different problems and used algorithms. Early descriptions of these problems can for example be found in Ignall and Schrage (1965); Graham et al (1979); Adams, Balas, and Zawack (1988); and Demeulemeester and Herroelen (1992).…”

Section: Branch and Bound Concept Of Apollomentioning

confidence: 99%

Production planning optimisation for composite aerospace manufacturing

Hueber

Fischer

Schwingshandl

et al. 2018

International Journal of Production Research

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Composite materials proved highly successful for aerospace applications in the last decades, but increased cost pressure forces the composite industry to become more efficient. This requires new manufacturing technologies and optimised processes as raw material costs and labour costs are basically fixed when wanting to keep production on site. Probably the most defining process for aerospace composite production is manual layup of prepreg material with subsequent curing in an autoclave. From production planning view, this combination poses the challenge of transition from discrete layup manufacturing to batch curing processing with the restriction of limited allowed storage time of the prepreg material prior to cure. In this paper, a new approach for production order optimisation at the conjunction of discrete and batch processing is presented. The APOLLO named tool is designed to decrease throughput times, streamline production and increase autoclave utilisation in the composite aerospace industry.

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Section: Branch and Bound Concept Of Apollomentioning

confidence: 99%

Production planning optimisation for composite aerospace manufacturing

Hueber

Fischer

Schwingshandl

et al. 2018

International Journal of Production Research

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“…The study of such systems is quite limited ([18]- [21]) and setups are often ignored in these studies. In another direction, setups are typically studied from scheduling perspective in job shop manufacturing (e.g., review [22]). Although bottlenecks have been studied extensively (see monograph [23] and papers [24]- [28]), how to identify the setup bottleneck, i.e., the product type or machine whose setup time reduction leads to the largest improvement in system production rate, is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Flexible Serial Lines With Setups: Analysis, Improvement, and Application

Zhao

Huang

et al. 2017

IEEE Robot. Autom. Lett.

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Setup times commonly exist in many flexible manufacturing lines to adjust tools and carry out safety and quality check during product changeovers. In this paper, a Markov chain model is introduced to study flexible manufacturing lines with setups. An aggregation-based recursive procedure is introduced to evaluate system performance. The convergence of the procedure is proved analytically and the accuracy is justified numerically. Using such a method, the impact of setups is analyzed and the effectiveness of downtime and setup time reduction is investigated. Indicators are derived to identify the setup bottlenecks with respect to machine and product type, whose setup time reduction can lead to the largest improvement in line production rate. Finally, an application study at a furniture manufacturing plant is introduced.Index Terms-Factory automation, intelligent and flexible manufacturing, markov chain, setups.

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“…Relevance of minimising flow times is related to, among others, improvement of customer responsiveness, maintaining flexibility, improvement of product quality, less need for relying on forecasts, reducing costs associated with work in process, and making better forecasts (Hopp and Spearman 2008;Pinedo 2015). See Mosier, Elvers, and Kelly (1984), Mahmoodi and Dooley (1991), Ponnambalam, Aravindan, and Reddy (1999), Pickardt and Branke (2012) and Neufeld, Gupta, and Buscher (2016) for heuristics addressing due date related criterions.…”

Section: Literature Reviewmentioning

confidence: 99%

Coordinating batching decisions in manufacturing networks

Zee

2017

International Journal of Production Research

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Family-based dispatching heuristics aim for improving job flow times by reducing time spent on set-ups. They realise set-up efficiencies by batching similar types of jobs. By their intuitiveness and the simplicity of their decision logic, they may contribute to an easy to implement and viable strategy in many practical settings. Similar to common dispatching rules most existing family-based dispatching heuristics are myopic, i.e. their decision scope is restricted to a single manufacturing stage. Hence, they neglect opportunities for improving shop performance by coordinating batching decisions with other manufacturing stages. Case examples from industry underpin the need for exploring these opportunities. We do so by studying a simple two-stage flow shop, entailing a serial and a batch stage. To facilitate shop coordination we propose extensions to existing family-based dispatching heuristics. Extended heuristics seek to further increase set-up efficiencies by allowing for upstream job re-sequencing, and pro-active set-ups, i.e. set-ups that may be initiated prior to the arrival of a job. Outcomes of an extensive simulation study indicate significant performance gains for extended heuristics vs. existing heuristics. Performance gains are largest for moderate and high set-up to run-time ratios.

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A comprehensive review of flowshop group scheduling literature

Cited by 106 publications

References 158 publications

Production planning optimisation for composite aerospace manufacturing

Production planning optimisation for composite aerospace manufacturing

Flexible Serial Lines With Setups: Analysis, Improvement, and Application

Coordinating batching decisions in manufacturing networks

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