1,064 research outputs found

    Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

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    From MDPI via Jisc Publications RouterHistory: accepted 2021-07-08, pub-electronic 2021-07-15Publication status: PublishedFunder: National Natural Science Foundation of China; Grant(s): 51875420, 51875421In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness

    Decentralized Scheduling Using The Multi-Agent System Approach For Smart Manufacturing Systems: Investigation And Design

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    The advent of industry 4.0 has resulted in increased availability, velocity, and volume of data as well as increased data processing capabilities. There is a need to determine how best to incorporate these advancements to improve the performance of manufacturing systems. The purpose of this research is to present a solution for incorporating industry 4.0 into manufacturing systems. It will focus on how such a system would operate, how to select resources for the system, and how to configure the system. Our proposed solution is a smart manufacturing system that operates as a self-coordinating system. It utilizes a multi-agent system (MAS) approach, where individual entities within the system have autonomy to make dynamic scheduling decisions in real-time. This solution was shown to outperform alternative scheduling strategies (right shifting and dispatching priority rule) in manufacturing environments subject to uncertainty in our simulation experiments. The second phase of our research focused on system design. This phase involved developing models for two problems: (1) resource selection, and (2) layout configuration. Both models developed use simulation-based optimization. We first present a model for determining machine resources using a genetic algorithm (GA). This model yielded results comparable to an exhaustive search whilst significantly reducing the number of required experiments to find the solution. To address layout configuration, we developed a model that combines hierarchical clustering and GA. Our numerical experiments demonstrated that the hybrid layouts derived from the model result in shorter and less variable order completion times compared to alternative layout configurations. Overall, our research showed that MAS-based scheduling can outperform alternative dynamic scheduling approaches in manufacturing environments subject to uncertainty. We also show that this performance can further be improved through optimal resource selection and layout design

    St. Cloud State University Employee Scholarship Bibliography [July 2019 - February 2022]

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    College Senate Minutes May 18, 2019

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    Minutes for the meeting of the College Senate on May 18, 2017

    Study applying simulation to improve a real production process in the context of Industry 4.0

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    During the thesis development, simulation theories and techniques will be applied to a part of the production process of an Italian manufacturing company. A simulation model of the steaming and washing phases will be developed to outline the automated and manual procedures that are performed in the AS-IS state. Several what-if scenarios will be then envisioned and simulated to analyze how the production activities could be re-engineered in the light of the new technological advancements, such as the introduction of full traceability

    Information Security Management Accounting

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    Self-organisation of mobile robots in large structure assembly using multi-agent systems

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    Competition between manufacturers in large structure assembly (LSA) is driven by the need to improve the adaptability and versatility of their manufacturing systems. The lack of these qualities in the currently used systems is caused by the dedicated nature of their fixtures and jigs. This has led to their underutilisation and costly changeover procedures. In addition to that, modern automation systems tend to be dedicated to very specific tasks. This means that such systems are highly specialised and can reach obsolescence once there is a substantial change in production requirements. In this doctoral thesis, a dynamic system consisting of mobile robots is proposed to overcome those limitations. As a first knowledge contribution in this doctoral thesis, it is investigated under which conditions using mobile robots instead of the traditional, fixed automation systems in LSA can be advantageous. In this context, dynamic systems are expected to be more versatile and adaptive than fixed systems. Unlike traditional, dedicated automation systems, they are not constrained to gantry rails or fixed to the floor. This results in an expanded working envelope and consequently the ability to reach more workstations. Furthermore, if a product is large enough, the manufacturer can choose how many mobile robots to deploy around it. Accordingly, it was shown that the ability to balance work rates on products and consequently meet their due times is improved. For the second knowledge contribution, two fundamentally different decision-making models for controlling mobile agents in the complex scheduling problem are investigated. This is done to investigate ways of taking full advantage from the potential benefits of applying mobile robots. It is found that existing models from related academic literature are not suited for the given problem. Therefore, two new models had to be proposed for this purpose. It was plausible to use an agent-based approach for self-organisation. This is because similarly to agents, mobile robots can perform independently of one-another; and have limited perception and communication abilities. Finally, through a comparison study, scenarios are identified where either model is better to use. In agreement with much of the established literature in the field, the models are shown to exhibit the common advantages and disadvantages of their respective architecture types. Considering that the enabling technologies are nearing sufficient maturity for deploying mobile robots in LSA, it is concluded that this approach can have several advantages. Firstly, the granularity and freedom of movement enables much more control over product completion times. Secondly, the increased working envelope enables higher utilisation of manufacturing resources. In the context of LSA, this is a considerable challenge because products take a very long time to get loaded and unloaded from workstations. However, if the product flow is steady, there are rare disruptions and rare production changes, fixed automation systems have an advantage due to requiring much less time (if any) for moving and localising. Therefore, mobile systems become more preferred to fixed systems in environments where there is an increasing frequency of disruptions and changes in production requirements. The validation of agent-based self-organisation models for mobile robots in LSA confirms the expectations based on existing literature. Also, it reveals that with relatively low amounts of spare capacity (5%) in the manufacturing systems, there is little need for sophisticated models. The value of optimised models becomes apparent when spare capacity approaches 0% (or even negative values) and there is less room for inefficiencies in scheduling

    2023 Annual Estimate of the Strategic Security Environment

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    The Annual Estimate of the Strategic Security Environment serves as a guide for academics and practitioners in the defense community on the current challenges and opportunities in the strategic environment. This year’s publication outlines key strategic issues across the four broad themes of Regional Challenges and Opportunities, Domestic Challenges, Institutional Challenges, and Domains Impacting US Strategic Advantage. These themes represent a wide range of topics affecting national security and provide a global assessment of the strategic environment to help focus the defense community on research and publication. Strategic competition with the People’s Republic of China and the implications of Russia’s invasion of Ukraine remain dominant challenges to US national security interests across the globe. However, the evolving security environment also presents new and unconventional threats, such as cyberattacks, terrorism, transnational crime, and the implications of rapid technological advancements in fields such as artificial intelligence. At the same time, the US faces domestic and institutional challenges in the form of recruiting and retention shortfalls in the all-volunteer force, the prospect of contested logistics in large-scale combat operations, and the health of the US Defense Industrial Base. Furthermore, rapidly evolving security landscapes in the Arctic region and the space domain pose unique potential challenges to the Army’s strategic advantage.https://press.armywarcollege.edu/monographs/1958/thumbnail.jp

    Asian Development Outlook 2015 Update: Enabling Women, Energizing Asia

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    [Excerpt] Over the past several decades, rapid growth has transformed the region into a “New Asia” that is more prosperous, urban, and globalized than ever before. Yet much work remains to ensure that the benefits of growth reach everyone in developing Asia. ADO 2015 Update draws attention to the unfinished agenda for gender equality, as Asian women are still disadvantaged in terms of education and workforce participation. Bringing more women into paid work can be a catalyst for broader social change. Equality between men and women is a goal in its own right, but women are not the only ones who will benefit. Economies in developing Asia will enjoy greater prosperity if they resolve to use all of their human resources to the fullest
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