Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

Cortes, Edwin A.; Rabelo, Luis; Sarmiento, Alfonso T.; Gutiérrez, Edgar

doi:10.3390/info11100467

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Discrete Event Simulation (DES), in particular, is one of the preferred research topics nowadays (Bara, Gautier, and Giard 2020) for its ability to simulate production system and supply chain behaviors. DES is suitable for leading the analysis of the dynamics of discrete processes such as manufacturing systems (Ingemansson and Bolmsjö 2004;Cortes et al 2020) and other environments, such as manufacturing plants, queuing systems, distribution systems, inventory and delivery, transportation networks, and communication networks (Huynh, Akhtar, and Li 2020;Zupan and Herakovic 2015). Jeon and Kim (2016) note that DES is a frequently used tool for Production Planning and Control problems that represents more than 45% of the simulation models in the studied sample.…”

Section: Distributed Discrete Event Simulation In Manufacturing Industriesmentioning

confidence: 99%

Lean techniques impact evaluation methodology based on a co-simulation framework for manufacturing systems

Possik

Zouggar-Amrani

Vallespir

et al. 2021

International Journal of Computer Integrated Manufacturing

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Lean implementation plays a major role in optimizing productivity and reducing waste. Applying the adequate integration of Lean Techniques (LT) can ensure a higher profitable benefit. Many companies face difficulties in choosing the LT that best suit their situations to reach their objectives. In this study, we propose the simulation of specific modeled industrial contexts and check the impact of implementing LT simultaneously. Market fluctuation, demand diversification, and uncertainty of resources contexts are studied to perceive how LT behaves accordingly. Four KPIs (Key Performance Indicators) are retained for the analysis: Work in Progress, Lead-time, Production Throughput, and Defect Rate. An aeronautical company is modeled and experiments are performed to demonstrate the usefulness of a developed co-simulation framework to perceive the sensitivity of LT to some industrial contexts. The results showed that Poka Yoke and 5S are context-free LT valid in any industrial context. Pull, SMED, and Cross training are contextual and deserve careful applicability regarding the simulated context. Cross training, suitable for uncertainty of resources, does not show any significant improvements when the company was exposed to market fluctuations and demand diversification contexts.

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Section: Distributed Discrete Event Simulation In Manufacturing Industriesmentioning

confidence: 99%

Lean techniques impact evaluation methodology based on a co-simulation framework for manufacturing systems

Possik

Zouggar-Amrani

Vallespir

et al. 2021

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

show abstract

“…The deep belief network (DBN) is composed of a multi-layer restricted Boltzmann machine (RBM) network and a one-layer backpropagation (BP) network (Cortes et al, 2020). Each node of the model is subject to Bernoulli distribution.…”

Section: Model Introductionmentioning

confidence: 99%

Big Data Analysis and Modeling Method of College Student Employment Management Based on Deep Learning Model

Yin

2023

International Journal of Web-Based Learning and Teaching Technologies

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In order to study the big data of college students' employment, this paper takes the big data of college students' employment as the premise, analyzes the current employment data by establishing a DBN model, and puts forward relevant management measures, aiming to provide scientific basis for the management of graduates' employment data. The results are as follows: By comparing the application evaluation of linear regression method, BP neural network and DBN model, this paper finds that DBN model has better accuracy and lower error and has better advantages in the application of college students' employment data management characteristics. In addition, the development of social economy and the number of college graduates are the key factors for the employment rate of college students. Therefore, this paper suggests that through the use of big data technology, college will build a data platform for college students' employment management and provide a carrier for college students to obtain professional information and employment management information.

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“…With this methodological architecture, public transportation policy makers and transportation managers can benefit from previously acquired knowledge to make decisions. Both industry and academic works have taken advantage of previous knowledge to guide the execution of operations with the support of analytical simulation models, also called digital twins [38][39][40]. Urban environments are dynamic and complex due to a broad set of interactions stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations

2021

Self Cite

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Last-mile operations in forward and reverse logistics are responsible for a large part of the costs, emissions, and times in supply chains. These operations have increased due to the growth of electronic commerce and direct-to-consumer strategies. We propose a novel data- and model-driven framework to support decision making for urban distribution. The methodology is composed of diverse, hybrid, and complementary techniques integrated by a decision support system. This approach focuses on key elements of megacities such as socio-demographic diversity, portfolio mix, logistics fragmentation, high congestion factors, and dense commercial areas. The methodological framework will allow decision makers to create early warning systems and, with the implementation of optimization, machine learning, and simulation models together, make the best utilization of resources. The advantages of the system include flexibility in decision making, social welfare, increased productivity, and reductions in cost and environmental impacts. A real-world illustrative example is presented under conditions in one of the most congested cities: the megacity of Bogota, Colombia. Data come from a retail organization operating in the city. A network of stakeholders is analyzed to understand the complex urban distribution. The execution of the methodology was capable of solving a complex problem reducing the number of vehicles utilized, increasing the resource capacity utilization, and reducing the cost of operations of the fleet, meeting all constraints. These constraints included the window of operations and accomplishing the total number of deliveries. Furthermore, the methodology could accomplish the learning function using deep reinforcement learning in reasonable computational times. This preliminary analysis shows the potential benefits, especially in understudied metropolitan areas from emerging markets, supporting a more effective delivery process, and encouraging proactive, dynamic decision making during the execution stage.

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Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

Cited by 5 publications

References 39 publications

Lean techniques impact evaluation methodology based on a co-simulation framework for manufacturing systems

Lean techniques impact evaluation methodology based on a co-simulation framework for manufacturing systems

Big Data Analysis and Modeling Method of College Student Employment Management Based on Deep Learning Model

Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations

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