A note on approximate iterative solution of open tandem networks with blocking

An aggregate continuum model for production flows and supply chains with fi- nite buffers is proposed and analyzed. The model extends earlier partial differential equations that represent deterministic coarse grained models of stochastic production systems based on mass con- servation. The finite size buffers lead to a discontinuous clearing function describing the throughput as a function of the work in progress (WIP). Following previous work on stationary distribution of WIP along the production line, the clearing function becomes dependent on the production stage and decays linearly as a function of the distance from the end of the production line. A transient experi- ment representing the breakdown of the last machine in the production line and its subsequent repair is analyzed analytically and numerically. Shock waves and rarefaction waves generated by blocking and reopening of the production line are determined. It is shown that the time to shutdown of the complete flow line is much shorter than the time to recovery from a shutdown. The former evolves on a transportation time scale, whereas the latter evolves on a much longer time scale. Comparisons with discrete event simulations of the same experiment are made.

For tandem queueing networks with generally distributed service times, decomposition often is the only feasible solution method besides simulation. The network is partitioned into individual nodes which are analysed in isolation. In existing decomposition algorithms for continuous-time networks, the output of a queue is usually approximated as a renewal process, which serves as the arrival process to the next queue. In this paper, the internal traffic processes are described as semi-Markov processes (SMPs) and Markov modulated Poisson processes (MMPPs). Thus, correlations in the traffic streams, which are known to have a considerable impact on performance, are taken into account to some extent. A two-state MMPP, which arises frequently in communications modeling, serves as input to the first queue of the tandem network. For tandem networks with infinite or finite buffers, stationary mean queue lengths at arbitrary time computed quasi-promptly by the decomposition component of the tool TimeNET are compared to simulation.KeywordsQueue LengthRenewal ProcessArrival ProcessLoss ProbabilityBusy PeriodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

While most evolutionary scenarios for language see it as a communication system with consequences on the language-ready brain, there are major difficulties for such a view. First, language has a core combination of features—semanticity, discrete infinity, and decoupling—that makes it unique among communication systems and that raise deep problems for the view that it evolved for communication. Second, extant models of communication systems—the code model of communication (Millikan, 2005) and the ostensive model of communication (Scott-Phillips, 2015) cannot account for language evolution. I propose an alternative view, according to which language first evolved as a cognitive tool, following Fodor’s (1975, 2008) Language of Thought Hypothesis, and was then exapted (externalized) for communication. On this view, a language-ready brain is a brain profoundly reorganized in terms of connectivity, allowing the human conceptual system to emerge, triggering the emergence of syntax. Language as used in communication inherited its core combination of features from the Language of Thought.

Analysis of a general Markovian two-stage continuous-flow production system with a finite buffer

New bounds for expected cycle times in tandem queues with blocking

The paper emphasizes on the analysis on wideband channel model for high speed wireless communication systems. The research problem in this study are based on the following concepts such as (i) Firstly, it is necessary to change to the appropriate physical devices that can support 5G system, (ii) It is required to design a channel that will adapt to the medium that will be convenient for the changed physical devices, and (iii) Mobile terminals that currently use 4G cannot be used in 5G system. The objectives in this study are - to analyze the existing channel model for mobile communication, to analyze the mathematical and dynamical model for wireless propagation channel, to implement the wireless propagation channel with specific purposes, to implement the optimized channel model performance, and to evaluate the performance of the developed channel design. The numerical analyses in this study are conducted by using MATLAB language. The research direction in this study are based on the channel system functions, and tapped delay-line models. The simulation results are confirmed that the 12 taps in this study for the high speed wireless communication system design.

Most forest harvesting operations are performed using two or more forestry machines in combination; in the typical forest production system in steep terrain in Japan, there are four production processes: felling, yarding, processing, and forwarding. It is essential to evaluate the overall productivity of such combined production systems, but the calculation method that indicates the productivity has not been fully established. In this study, we built simulation models of serial, parallel, and sequential production systems by using system dynamics to evaluate their productivity regarding the combined machine productivity (CMP) and combined labor productivity (CLP) indices. Comparing the three types of forest harvesting systems in Japan, the parallel production system had the highest CMP, while the serial production system had the highest CLP; the sequential production system lay between the serial and parallel production systems in terms of CMP and CLP. The overall productivity of Japanese forest production systems was lower than that of Central Europe, where processor tower yarders (PTYs) are used. Thus, the CLP of Japanese forest production systems was improved by 12.8 to 26.5% by incorporating the concept of a PTY, and it can be further improved by eliminating the forwarding process.

Modeling and simulation are key performance analysis and control techniques to optimize decision-making as well as design and operate complex production systems. They are also indicated as one of the technological pillars of modern industry and IT solutions supporting the implementation of the roadmap toward Industry 4.0 in the areas of digital transformation and automation. In the context of the required rapid transformation of today’s enterprises, it becomes extremely important to look for solutions that allow the use of the existing infrastructure, information, and energy, so as to minimize the negative impact of new technologies and the transformation process itself on the environment. The article presents an approach to modeling large and complex production systems with the use of distributed Petri net models allowing the use of the possessed IT infrastructure as consistent with the idea of sustainable development in the activities of enterprises. This eliminates two major problems that render traditional models unusable. The first is related to the difficulties in analyzing and verifying models of enormous size and infinite space of states. The second is related to the required computing power, if such analyzes are to be performed on one computing unit, which would force the producers to replace the IT infrastructure. For this purpose, modular Petri nets are introduced. Other benefits of modularization, such as smaller components that can be independently analyzed, are also presented in the paper. The proposed modular Petri net has been implemented in the proprietary GPenSIM software. The paper is complemented by a practical example of industrial modeling of production systems with automated guided vehicles (AGVs) using the Modular Model with Intelligent Petri Modules.

IntroductionIn swine disease surveillance, obtaining labeled data for supervised learning models can be challenging because many farms lack standardized diagnostic routines and consistent health monitoring systems. Unsupervised learning is particularly suitable in such scenarios because it does not require labeled data, allowing for detecting anomalies without predefined labels. This study evaluates the effectiveness of unsupervised machine learning models in detecting anomalies in productivity indicators in swine breeding herds.MethodsAnomalies, defined as deviations from expected patterns, were identified in indicators such as abortions per 1000 sows, prenatal losses, preweaning mortality, total born, liveborn, culled sows per 1000 sows, and dead sows per 1000 sows. Three unsupervised models - Isolation Forest, Autoencoder, and K-Nearest Neighbors (KNN) - were applied to data from two swine production systems. The herd-week was used as the unit of analysis, and anomaly scores above the 75th percentile were used to flag anomalous weeks. A permutation test assessed differences between anomalous and non-anomalous weeks. Performance was evaluated using F1-score, precision, and recall, with true anomalous weeks defined as those coinciding with reported health challenges, including porcine reproductive and respiratory syndrome (PRRS) and Seneca Valley virus outbreaks. A total of 8,044 weeks were analyzed.ResultsThe models identified 336 anomalous weeks and 1,008 non-anomalous weeks in Production System 1, and 1,675 anomalous weeks and 5,025 non-anomalous weeks in Production System 2. The results from the permutation test revealed significant differences in productivity indicators between anomalous and non-anomalous weeks, especially during PRRS outbreaks, with more subtle changes observed during Seneca Valley virus outbreaks. The models performed well in detecting the PRRSV anomaly, achieving perfect precision (100%) across all models for both production systems. For anomalies like SVV the models showed lower performance compared to PRRSV.DiscussionThese findings suggest that unsupervised machine learning models are promising tools for early disease detection in swine herds, as they can identify anomalies in productivity data that may signal health challenges.

Modular production systems are an excellent possibility to meet the increasing complexity of production tasks, which result form a rising number of simultaneously produced product variants. These systems consist very often of standard modules, which can be connected randomly via a fixed linked transportation system; so different requirements can be accomplished. This flexibility results in a high degree of complexity for the material flow control that is increased additionally when the dynamic behaviour of the system is strongly raised by high product variety and fluctuating production mixes. Other factors having negative effects on the dynamic behaviour of the system are parasitic drag that are found in every production, like failures in work stations or in the transportation system, dead times or maintenances, in this paper different material flow strategies for the application in variant rich production environments are analysed: station oriented routing, task oriented routing, task oriented routing with load balancing and workpiece centred, agent oriented routing with consideration of priorities. These strategies were examined with two models of production systems in different scenarios on their characteristics by an event discrete simulator

This paper presents the results of research carried out in recent years in the area of modeling and simulation of production and assembly systems. The main goal of the article is to show the practical application of the multimodal approach in the simulation modeling of production and assembly systems and the benefits resulting from it. The article describes the issues of simulation modeling and presents a multi-level model of behaviors of the cyclic processes system, which is the backbone of the multimodal approach. The main part of the article is the concept of using a multimodal approach in simulation modeling and an example of its application. The main contribution of the paper is to define the methodology of simulation modeling based on cyclical processes - a multimodal approach and an approach to modeling from a high level where the basic object is a workstation.

By successful establishment of SCM(Supply Chain Management) System they should make the supply chains speedy and combine production information system with the outcome system and thus they reinforce the competition of the production system of local enterprises in accordance with the rapid decision making This study is to develop the most appropriate production system models through the reflection of JIT system in the SCM which is the necessity of the times and its importance. To achieve this goal the researcher diversifies the prerequisites for the success of the business which will be the strategical factors of the production systems. The objects of this study is to develop the most appropriate production system models and to provide the enterprises with the models which are based on the SCM.

To make a fundamental shift toward value orientation, manufacturing companies strategically move to integrate services into their portfolio. While manufacturing firms rely on production information systems as the backbone of their operations, these systems are based on product structure models (e.g., bill of materials). This poses a problem because services do not adhere to the goods-dominant perspective of product structures. To solve this divide, this paper proposes an integrative mathematical model for both production systems and service systems. The model draws upon concepts of service-dominant logic and is based on hypergraph theory. To illustrate that the production structure model includes both product structures and process structures, we further demonstrate that the production structure model can be transformed into either. Therefore, our theoretical contribution lies in introducing a structural model into production systems that is compatible with structures of a service system model. For practice, the model enables the development of production information systems that can plan and control products, services, and hybrids.

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

Satellite communication is one of the key focuses of the sixth generation (6G) mobile communication systems, and aerial hybrid reconfigurable intelligent surface (AHRIS) -assisted satellite communication has recently emerged as a promising candidate for practical deployment in the 6G wireless networks. System design and gain rely heavily on accurately modeling of the underlying channel characteristics, which is challenging due to the high mobility of AHRIS and the unique properties of satellite communication. In this paper, we propose a novel three-dimensional (3D) non-stationary wideband geometry-based stochastic model (GBSM) for AHRIS-assisted satellite communication systems, where AHRIS movements and satellite trajectories are specifically considered. Moreover, two AHRIS phase correction schemes are proposed based on the characteristics of AHRIS in satellite communication systems. Following this, channel correlation function, Doppler power spectral density (PSD) and channel capacity are derived according to the proposed model. Simulation results show that the proposed model can accurately capture the characteristics of the AHRIS-assisted satellite communication systems, and the proposed phase correction schemes can effectively improve the channel statistical characteristics and increase the system capacity.