Bernoulli serial lines with batching machines: Performance analysis and system-theoretic properties

The batching machine can process several jobs simultaneously as a batch. Aiming at the Bernoulli serial production lines with finite buffers and batching machines, we establish an analytical model for the two-machine one-buffer Bernoulli serial lines and an approximate method for the longer lines to evaluate the performance measures. Our numerical experiments verify the high accuracy of such methods.

Effective control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems. This paper investigates energy consumption reduction in Bernoulli serial lines with buffer stripping, i.e., with the buffers that must be depleted at the end of each shift. This buffer depletion activity is widely used in automotive paint shops and other production systems with perishable products to avoid quality deterioration. Clearly, to reduce energy consumption in such systems, the startup schedule of the machines downstream of the depleted buffers should be controlled properly at the beginning of each shift. In this paper, using transient analysis of the systems at hand, performance evaluation formulas are derived and system-theoretic properties are analyzed. In addition, effective controllers for machine startup schedule are proposed. The efficacy and practical implications of these controllers are demonstrated using numerical investigation. Finally, insights for obtaining the optimal controllers are provided.

Manufacturing systems with perishable products are widely seen in practice (e.g. food, metal processing, etc.). In such systems, the quality of a part is highly dependent on its residence time within the system. However, the behaviour and properties of these systems have not been studied systematically and, therefore, it is carried out in this paper. Specifically, we assume that the probability that each unfinished part is of good quality is a decreasing function of its residence time in the preceding buffer. Then, in the framework of serial production lines with machines having Bernoulli reliability model, we derive closed-form formulas for performance evaluation in the two-machine line case, and develop an aggregation-based procedure to approximate the performance measures in -machine lines. In addition, we study monotonicity properties of these production lines using numerical experiments. A case study in an automotive stamping plant is described to illustrate the theoretical results obtained.

Transient behavior of production systems has significant practical and theoretical implications. However, analytical methods for analysis and control of production systems during transients remain largely unexplored. In the framework of serial production lines with Bernoulli machines and finite buffers, this article develops a mathematical model for transient analysis and derives closed-form expressions for evaluating the production rate, consumption rate, work-in-process, and probabilities of machine starvation and blockage during transients. In addition, a computationally efficient procedure based on recursive aggregation is developed to approximate the transient performance measures with high accuracy. Finally, based on the mathematical model derived, system-theoretic properties of several important system transient characteristics are studied.

Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of noisy and ambiguous continuous variables, while we typically seek simplified dynamics rules that capture essential variables. In this work, we propose a method to extract a small number of essential hidden variables from high-dimensional time-series data and learn state transition rules between hidden variables. Our approach is based on the Restricted Boltzmann Machine (RBM), which models observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, consist of both discrete and continuous variables with temporal relationships. To address this, we introduce the Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables and the Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependencies among discrete hidden variables. Additionally, we propose a rule-based method to extract essential information as hidden variables and represent state transition rules in an interpretable form. We evaluate our proposed method on the Bouncing Ball, Moving MNIST, and dSprite datasets. Experimental results demonstrate that our approach effectively learns the dynamics of these physical systems by extracting state transition rules between hidden variables. Moreover, our method can predict unobserved future states based on observed state transitions.

Artificial genetic circuits have been recently proposed to realize unprecedented biological systems which do not exist in nature by combining several genes. For example, recombinant Escherichia coli provides novel artificial functions such as blinking periodically or invading cancer cells. In protein engineering, new functional proteins have been created by site-directed mutagenesis methods. The main purpose of our research is to implement the Cellular Automata (CA) in vivo employing these methods. Computational capacity of CA is equivalent to the universal Turing Machine. CA are multi-cellular systems in which uniform cells are allocated on lattice grid. Each cell has a finite number of states. The state transition is determined by the current state and the states of neighbor cells. Three mechanisms are required to implement CA in bacteria: (1) Sending and receiving signals between cells, (2) Sustaining the state, (3) Sensing input signals and changing the state following the state transition rules. To encode signals for the mechanism (1), we use small molecules inducing transcription. For the mechanism (2), the toggle switch circuit was employed to represent a finite number of states using gene expression. If one gene does express and the other does not, the state is 1, and vice versa, 0. For the mechanism (3), the state transition functions were implemented as logic gates using transcriptional regulatory proteins which bind to specific signal molecules (chemicals). In this paper, we applied this method to in vivo implementation of one-dimensional CA and corroborated whether it behaves correctly. In principle, this method can be applied to any dimensional CA by employing more signal molecules Permission to make digital or hard copies of all or part of this work for to construct more complex logic gate. Furthermore, we succeeded to execute one step calculation of one-dimensional CA in vivo in a laboratory experiment.

For telecommunication software design, SDL[1,2] (Functional Specification Description Language) is conventionally used for specification descriptions. This article first clarifies difficulties of SDL description for supplementary services[3] which allow a conversation among an infinite number of customers, and where there is considerable service interaction behavior.Then, this article proposes a new telecommunication service description method, STR (State Transition Rule) method. In the proposed method, the service among an arbitrary number of customers can be defined easily, and declarative description is allowed for service interaction behavior in the early stage of software design. Also, this article clarifies a feature of the proposed descriptions, that monotonic increase of transition rules realizes easy service addition. Lastly, the analyzability of the proposed descriptions is discussed.

It is thought that the reveal of generalized mechanism of hierarchical morphosis phenomena of living system is the fundamental of various field, such as mass-production of molecular machines of nanotechnology, artificial synthetic of biology, and so on. This study developed the model to simulate the morphogenetic mechanism of cells under the condition of two-dimensional cellular automata. We considered not only the state transition rules but also the concentration diffusion of the field. Thus our model was able to simulate morphogenetic formation in few state transition rules.

Modeling and analysis of Bernoulli serial lines with batching machines under a partial-batch policy

Recently, the research of the internet of vessels system is a hotspot in the marine engineering area. In this article, the Internet of Vessels based on Beidou satellite navigation system (IoV-BDS) architecture is proposed for marine cold chain transportation monitoring and scheduling. An improved communication method using Beidou short-message communication (SMC) is designed to ensure the reliability of ship-shore data transmission. Additionally, based on the IoV-BDS, we proposed a novel route scheduling optimization model which considers the key costs for marine cold chain transportation, such as fuel, catches damage, and refrigeration energy consumption, and designed a speed selection mechanism. Furthermore, an improved ant colony optimization (IACO) algorithm was utilized to optimize the whole fleet transportation routes, which takes advantage of the A-star algorithm to speed up the convergence of ant colony algorithm (ACO), a novel state transition rule was used to prevent falling into a locally optimal solution. The applicability of IoV-BDS was verified by the shipboard data acquisition and Beidou SMC experiments. The performance evaluation and analysis of route optimization schemes were presented by simulations and comparison with other schemes. The results demonstrated the benefit of our proposed schemes, in terms of solution quality and convergence of the algorithm.

Feature selection is an important task which can significantly affect the performance of a pattern recognition system. In this paper, we present a feature selection algorithm based on Ant Colony Optimization (ACO) for human activity recognition. The algorithm incorporates the classification performance into the state transition rule, and uses the pheromone of the ant colony to find the optimal feature set, then selects feature set with small size and high classification accuracy. We perform experiments on human activity dataset using four classifiers, Naive Bayes, Random Forest, K-Nearest Neighbor and Support Vector Machine(NB, RF, KNN and SVM). The results show that the proposed method can improve the recognition accuracy, and it is comparable to other feature selection methods.

This paper develops a behavioral model of AC solid state power controller (SSPC) based on Mixed Signal Finite State Machine (FSM). State transition and impedance variation rules are summarized by analyzing the working modes of AC SSPC. The model defines 6 working states and 8 transitions functions to imitate the impedance variation of AC SSPCs during ON/OFF State, Zero voltage turn-on, Zero current turn-off operations. The model is implemented by Mixed Signal Finite State Machine modeling tool in Saber and configured by test results for accuracy verification. Speed test results show that the proposed behavioral model significantly improves computation efficiency.

When the ant colony algorithm is used to solve the global path planning of the unmanned boat, it is prone to problems such as local optimization and too slow convergence speed. This paper combines the problems encountered in the actual application of the unmanned boat and proposes an improved ant colony algorithm. Based on the traditional ant colony algorithm, the state transition rules are optimized and the pheromone volatilization coefficient is dynamically adjusted. The simulation results show that the improved ant colony algorithm compared with the traditional ant colony algorithm, the shortest path and path finding time of the USV to the destination are optimized, and the smoothness of the USV's search path is also improved.

There are often many kinds of rumors spreading in new media after emergencies; however, the existing studies have focused on the rumors with competition, and little attention has been paid to those with mutual promotion. Therefore the mutual promotion between two rumors and the state transition rules between different groups are first analyzed. Then a 3SI3R model, extending the classical 2SI2R model, and mean-field equations of the proposed model are given. Further, the stability and influence of the mutual promotion on the final size of two rumors in the proposed model are derived. Finally, numerical simulations are carried out to investigate the influence of factors, that is, the mutual promotion, occurrence time differences, and the forgetting rate, on the propagation of two rumors. The results show that the enhancement of the mutual promotion can accelerate propagation of two rumors and enlarge their final size; the increase of forgetting rate helps to decrease the final size of two rumors; the decrease in occurrence time differences can result in a small increase in the final size of the later rumor, and the overall situation of two rumors becomes more difficult to control.

This paper proposes a methodology for developing multi-agent systems. Several design considerations for robotic agents are discussed. A script specifies a multi-agent system by defining the behavior of each agent. An agent is modeled by an extended finite state automaton. By using the state transition rules as the building blocks, an agent as well as a multi-agent system can be developed efficiently. This methodology is further applied to multi-agent robotic systems in which reactivity and interaction are important issues. A script-based architecture is developed to implement the agents in multi-agent robotic systems. This architecture considers not only the control architecture of a single agent, but also the interactions among multiple agents and their environments. A script for the object-sorting task problem is utilized for demonstration.