1,838 research outputs found
An optimized fuzzy logic model for proactive maintenance
Fuzzy logic has been proposed in previous studies for machine diagnosis, to
overcome different drawbacks of the traditional diagnostic approaches used.
Among these approaches Failure Mode and Effect Critical Analysis method(FMECA)
attempts to identify potential modes and treat failures before they occur based
on subjective expert judgments. Although several versions of fuzzy logic are
used to improve FMECA or to replace it, since it is an extremely cost-intensive
approach in terms of failure modes because it evaluates each one of them
separately, these propositions have not explicitly focused on the combinatorial
complexity nor justified the choice of membership functions in Fuzzy logic
modeling. Within this context, we develop an optimization-based approach
referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly
generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan
data collected in real-time from a plant machine. In the experiment, three
types of membership functions (Triangular, Trapezoidal, and Gaussian) were
used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this
model based on the Trapezoidal membership functions identifies the failure
states with high accuracy, and its capability of dealing with large numbers of
rules and thus meets the real-time constraints that usually impact user
experience.Comment: 16 pages in single column format, 11 figures, 12th International
Conference on Artificial Intelligence, Soft Computing and Applications (AIAA
2022) December 22 ~ 24, 2022, Sydney, Australi
On the role of Prognostics and Health Management in advanced maintenance systems
The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-201
A survey of AI in operations management from 2005 to 2009
Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.
Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.
Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an
increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.
Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified.
Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.
Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research
A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization
Textile manufacturing is a typical traditional industry involving high
complexity in interconnected processes with limited capacity on the application
of modern technologies. Decision-making in this domain generally takes multiple
criteria into consideration, which usually arouses more complexity. To address
this issue, the present paper proposes a decision support system that combines
the intelligent data-based random forest (RF) models and a human knowledge
based analytical hierarchical process (AHP) multi-criteria structure in
accordance to the objective and the subjective factors of the textile
manufacturing process. More importantly, the textile manufacturing process is
described as the Markov decision process (MDP) paradigm, and a deep
reinforcement learning scheme, the Deep Q-networks (DQN), is employed to
optimize it. The effectiveness of this system has been validated in a case
study of optimizing a textile ozonation process, showing that it can better
master the challenging decision-making tasks in textile manufacturing
processes.Comment: arXiv admin note: text overlap with arXiv:2012.0110
A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. View Full-Tex
Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research
Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research
Quantitative modelling approaches for lean manufacturing under uncertainty
[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812
The Application of the EDAS Method in the Parametric Selection Scheme for Maintenance Plans in the Nigerian Food Industry
Nowadays, maintenance performance in organizations has become compelling due to competitiveness in the global market and the inclusion of more legislation issues (such as safety and health regulations) in assessments. In this article, the purpose is to formulate in maintenance problem for a food processing unit as a multicriteria problem and solve it using the evaluation based on distance from the average solution (EDAS) method. To attain this purpose, the authors defined a set of weighted criteria and a set of alternatives, and the solution is the alternative that scores the best in those criteria. Consequently, analysis was done based on the EDAS method and the calculated results from the literature data. Consequently, the parameters considered include the frequency of failure, MTBF, MTTF and MTTR while availability is the response. The EDAS method was used to select the best alternative (MTTR, 0.8802) and this score of 0.8802 is for an alternative. The chief novelty of this article is the unique introduction of an innovative EDAS method, which requires only two measures of the desirability of alternative (positive and negative distances from the average solution) but excluded the evaluation of the idea and nadir solutions for the key performance indicators of maintenance. Consequently, this study initiates a maintenance plan for the food industry referring to the key performance indicators as a cause for poor availability of equipment in the Nigerian food industry
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