| 1. |
- Mahmoodi, Ehsan, et al.
(author)
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A framework for throughput bottleneck analysis using cloud-based cyber-physical systems in Industry 4.0 and smart manufacturing
- 2024
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In: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 232, s. 3121-3130
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Journal article (peer-reviewed)abstract
- The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement.
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| 2. |
- Mahmoodi, Ehsan, et al.
(author)
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Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing
- 2024
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In: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 72, s. 287-307
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Journal article (peer-reviewed)abstract
- Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.
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| 3. |
- Mahmoodi, Ehsan, et al.
(author)
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The impact of Industry 4.0 on bottleneck analysis in production and manufacturing : Current trends and future perspectives
- 2022
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In: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 174
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Research review (peer-reviewed)abstract
- Bottleneck analysis, known as one of the essential lean manufacturing concepts, has been extensively researched in the literature. Recently, there has been a move towards using new Industry 4.0-based concepts and technologies in the development of bottleneck analysis. However, the interrelations between bottleneck analysis and Industry 4.0 have not been studied thoroughly. The present study addresses this gap and performs a systematic literature review on articles available in major scientific databases (i.e., Web of Science and Scopus) to investigate the impact of Industry 4.0 on the advancement of bottleneck analysis in production and manufacturing. Bibliometric analysis and content review were performed to extract the quantitative and qualitative data. Results revealed that only five out of 15 design principles and five out of eleven technologies of Industry 4.0 were addressed previously in developing bottleneck analysis methods. In addition to highlighting the existing gaps in the literature and proposing topics for future research, several potential development streams are proposed by studying the design principles and technologies of Industry 4.0, which have not been considered in bottleneck analysis before.
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| 4. |
- Nourmohammadi, Amir, et al.
(author)
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A genetic algorithm for heterogenous human-robot collaboration assembly line balancing problems
- 2022
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In: Procedia CIRP. - : Elsevier. - 2212-8271. ; 107, s. 1444-1448
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Journal article (peer-reviewed)abstract
- Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.
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