A Novel Hybrid Modelling for Aggregate Production Planning in a Reconfigurable Assembly Unit for Optoelectronics

Sisca, Francesco G.; Fiasché, Maurizio; Taisch, Marco

doi:10.1007/978-3-319-26535-3_65

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Various research has used synthetic data generated from discrete-event simulation models to develop hybrid models for aggregate production planning (Sisca et al, 2015) and solve flexible flow-shop scheduling problems (Apornak et al, 2021). The same technique has also helped develop decision support systems for plant-level maintenance (Guner et al, 2016).…”

Section: Physical Simulationmentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation. Current methods lack generalizable framework for researchers to follow and solve these issues. The development of synthetic data sets, however, can make up for missing samples and enable researchers to understand existing issues within the manufacturing process and create data-driven tools for reducing manufacturing costs. This paper systematically reviews both discrete and continuous manufacturing process data types with their applicable synthetic generation techniques. The proposed framework entails four main stages: Data collection, pre-processing, synthetic data generation, and evaluation. To validate the framework’s efficacy, a case study leveraging synthetic data enabled an exploration of complex defect classification challenges in the packaging process. The results show enhanced prediction accuracy and provide a detailed comparative analysis of various synthetic data strategies. This paper concludes by highlighting our framework’s transformative potential for researchers, educators, and practitioners and provides scalable guidance to solve the data challenges in the current manufacturing sector.

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Section: Physical Simulationmentioning

confidence: 99%

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Buggineni,

Chen,

Camelio

2024

Front. Manuf. Technol.

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“…Since APP problem always involves several criteria (objectives) and due to the vagueness of the available information, fuzzy multi-objective programming has been widely used in this area. Lee (1990), Gen, Tsujimura and Ida (1992), Wang and Fang (2001), Wang and Liang (2004), Ghasemy Yaghin, Torabi and Fatemi Ghomi (2012), Madadi and Wong (2014), Gholamian et al (2015), Gholamian, Mahdavi and Tavakkoli-Moghaddam (2016), Kalaf et al (2015), Sisca, Fiasché and Taisch (2015), Fiasché et al (2016) and Zaidan et al (2017) utilised various kinds of fuzzy multi-objective optimisation models to study APP under uncertainty. Gholamian et al (2015) and Gholamian, Mahdavi and Tavakkoli-Moghaddam (2016) developed a fuzzy multi-site multi-objective mixed integer nonlinear APP model in a supply chain under uncertainty with fuzzy demand, fuzzy cost parameters, etc.…”

Section: Fuzzy Multi-objective Optimisation Of App Under Uncertaintymentioning

confidence: 99%

“…A modified fuzzy multi-objective linear programming method to APP that minimises total production costs and total labour costs is proposed by Kalaf et al (2015), which involves fuzzy aspiration levels of the objectives and fuzzy tolerance levels. Sisca, Fiasché and Taisch (2015) constructed a fuzzy multi-objective linear programming model for APP in a reconfigurable assembly unit for optoelectronics where product price, inventory cost, etc. are supposed to be fuzzy variables.…”

Section: Fuzzy Multi-objective Optimisation Of App Under Uncertaintymentioning

confidence: 99%

Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry

Jamalnia

Yang

et al. 2019

Journal of Manufacturing Systems

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The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods. The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.The results of the simulation models are compared to those of multi-objective optimisation models.In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy.

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“…ODAY's global markets are increasingly being driven by demand for product customization whilst products life cycles are shrinking and customers awareness is increasing resulting in a greater uncertainty in market demand [1]. Moreover, in last three decades mass production has moved from old industrial countries to developing ones [2] and western manufacturing enterprises are undergoing a transformation from a pure product based value creation approach towards a more service oriented marketing model [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Additive manufacturing as a strategic tool for industrial competition

Sisca

Angioletti

Taisch

et al. 2016

2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI)

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Abstract-Additive Manufacturing (AM), often misleadingly referred to as 3D Printing (3DP), comprises of a group of technologies whose initial inception occurred over thirty years ago within the product design and development applications for the rapid prototyping of concepts, primarily using polymeric materials. Over the past few years AM development has increased exponentially and 3D Printing has expanded to include new areas of research such as 4D Printing, Nano AM, Contour Crafting and so on. However, a proper understanding of the technology's actual and potential benefits to industrial manufacturing has not been approached by practitioners and researchers in detail and industrial end-users risk missing the opportunity to make competitive choices due to the lack of an impartial and realistic overview. Real and tangible industrial benefits are often misunderstood due to the dissemination of information delivered with a mindset grown on a maker-side market, which misses to scope for AM in industrial applications. The aim of the paper is presenting a detailed overview of AM applications in the industrial world, focusing on the likely impacts on organizations and, moreover, to highlight and discuss the potential employments of the technology within the industrial value chain.

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A Novel Hybrid Modelling for Aggregate Production Planning in a Reconfigurable Assembly Unit for Optoelectronics

Cited by 7 publications

References 19 publications

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Enhancing manufacturing operations with synthetic data: a systematic framework for data generation, accuracy, and utility

Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry

Additive manufacturing as a strategic tool for industrial competition

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