Chaotic Behavior in a Flexible Assembly Line of a Manufacturing System (original) (raw)

Chaotic behavior in manufacturing systems

International Journal of Production Economics, 2006

In this article, we present a methodology derived from non-linear dynamic systems (NLDS) theory for analyzing the dynamic behavior of manufacturing systems. Some simple production systems are simulated, for which a chaotic behavior can be observed under certain dispatching rules and utilization levels. The dynamic behavior of a reactive system is studied; i.e., a system in which there is no previous schedule but jobs and operations are assigned to machines according to the state of the system. A discrete event model is used to represent the manufacturing system.

Deterministic chaos in a model of discrete manufacturing

2009

A natural extension of the bucket brigade model of manufacturing is capable of chaotic behavior in which the product intercompletion times are, in effect, random, even though the model is completely deterministic. This is, we believe, the first proven instance of chaos in discrete manufacturing. Chaotic behavior represents a new challenge to the traditional tools of engineering management to reduce variability in production lines. Fortunately, if configured correctly, a bucket brigade assembly line can avoid such pathologies.

The nature and origin of chaos in manufacturing systems

Proceedings of 1994 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (ASMC)

In an informal manner, the word chaos frequently comes to the lips of engineers trying to operate manufacturing facilities. From a formal perspective, the discovery and application of the theory of deterministic chaos to natural systems has revolutionized work in many branches of physics, chemistry, and biology. This paper presents initial efforts to demonstrate chaotic behavior in manufacturing systems, and to explore its origins. We characterize chaotic behavior operationally as small changes bringing about large effects.

Chaos detection and control in production systems

Global Production Management, 1999

The growing structural and dynamical complexity oftoday's production systems in combination with nonlinearities causes problems for production control. For analysing the complex production systems a production model is simulated with a production simulator and the dynamic system properties are examined with different methods of the linear and nonlinear time series analysis. The dependence of the stock fluctuations of different parameters and initial conditions are analysed. At the appearance of irregular stock fluctuations, suitable control methods should intrude into sensitive production sectors and lead the system back to a stable range.

Analyzing the Manual and Automated Assembly Line Using System Dynamics (SD) Approach

Journal of Systems Thinking in Practice, Volume & Issue: Volume 2, Issue 1, 2023

Improving the responsiveness to customers’ orders is the goal of this research. Balancing the manual and automated production lines using proper equipment is crucial for the case study to reduce production costs and increase the quantity. The main problem in this research is responding to orders and demand using the proper equipment and machines in the production line. Using automated equipment can improve productivity and responses, but using advanced technology is not necessarily effective and should be organized in proportion to demands. As in this case study, there are complex cause-and-effects relations; using System Dynamics is very effective. Different productivity factors, delays, orders, etc., are considered to determine the proper production method and equipment in the long term. After that, behavioral reproduction and extreme condition testing validate the model and compare the automated production line equipment percentage usage. According to the volume of orders, the optimum case is suggested. Then, different scenarios related to customer volume are analysed, and finally, the policies are examined. As the simulation results in current conditions and market forecasting indicate, 67.2% of production line equipment should be automated. Any customer order changes can increase or decrease the results, which should be calculated accurately. Considering all the influential factors, the presented model helps the production managers have the best policy for choosing the production method. Using this method, considering some changes in the variables can be used in different industries as optimizing the production line equipment is the main key finding of this research.

Chaos synchronization for a class of uncertain chaotic supply chain and its control by ANFIS

International journal of production management and engineering, 2023

In this paper, modeling of a three-level chaotic supply chain network. This model has the uncertainty of the retailer in the manufacturer. An adaptive neural fuzzy method has been proposed to synchronize the two chaotic supply chain networks. First, a nonlinear feedback control method is designed to train an adaptive neural fuzzy controller. Then, using Lyapunov theory, it is proved that the nonlinear feedback controller can reduce the synchronization error to zero in a finite time. The simulation results show that the proposed neural fuzzy controller architecture well controls the synchronization of the two chaotic supply chain networks. In the other part of the simulation, a comparison is made between the performance of the nonlinear controller and the adaptive neural fuzzy. Also, in the simulation results, the controller signal is depicted. This signal indicates that the cost of implementation in the real world is not high and is easily implemented.

Chaotic Dynamics of Cutting Processes Applied to Reconfigurable Manufacturing Systems Control

2000

There are already known and enounced into dedicated literature the limits of classic theory concerning cutting processes stability. Starting from this aspect and also from the need of designing an intelligent system to control cutting stability, to enable full use of RMS technological system productivity resources, this paper is a first step to a new approach of cutting process dynamics,

Transient behavior in a flexible assembly system

International Journal of Flexible Manufacturing Systems, 1991

We consider an assembly line with m stations in series having finite-capacity buffers. Blocking occurs when buffers are full. There are M different types of products 1o be assembled, each with its own processing requirements. There is a production target set for each type. We give tight bounds on how long it takes such a system to reach steady state for a given cyclic schedule.

Robust controlling of chaotic behavior in supply chain networks

Journal of the Operational Research Society, 2016

The supply chain network is a complex nonlinear system that may have a chaotic behavior. This network involves multiple entities that cooperate to meet customers demand and control network inventory. Although there is a large body of research on measurement of chaos in the supply chain, no proper method has been proposed to control its chaotic behavior. Moreover, the dynamic equations used in the supply chain ignore many factors that affect this chaotic behavior. This paper offers a more comprehensive modeling, analysis, and control of chaotic behavior in the supply chain. A supply chain network with a centralized decision-making structure is modeled. This model has a control center that determines the order of entities and controls their inventories based on customer demand. There is a time-varying delay in the supply chain network, which is equal to the maximum delay between entities. Robust control method with linear matrix inequality technique is used to control the chaotic behavior. Using this technique, decision parameters are determined in such a way as to stabilize network behavior.

Production and inventory control with chaotic demands

Omega, 2005

This study explores an e cient approach for identifying chaotic phenomena in demands and develops a production lot-sizing method for chaotic demands. Owing to the butter y e ect of chaotic demands, precise prediction of long-term demands is di cult. The experiments conducted in this study reveal that the maximal Lyapunov exponent is very e ective in classifying chaotic and non-chaotic demands. A computational procedure of the Lyapunov exponent for production systems has been developed and some real world chaotic demands have been identiÿed using the proposed chaos-probing index. This study proposes a modiÿed Wagner-Whitin method that uses a forward focused perspective to make production lot-sizing decision under chaos demands for a single echelon system. The proposed method has been empirically demonstrated to achieve lower total production costs than three commonly used lot-sizing models, namely: lot-for-lot method, periodic ordering quantity, and Silver-Meal discrete lot-size heuristic under a ÿxed production horizon, and the conventional Wagner-Whitin algorithm under chaotic demands. Sensitivity analysis is conducted to compare changes in total cost with variations in look-ahead period, initial demand, setup cost and holding costs.