Production and inventory control with chaotic demands (original) (raw)

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.

Enhancing supply chain solutions with the application of chaos theory

Supply Chain Management: An International Journal, 2006

PurposeThe purpose of this article is to expand the base of supply chain knowledge by applying chaos theory principles to selected supply chain functions.Design/methodology/approachResearchers borrow chaos theory from the natural sciences, provide a basic explanation, and then examine how it may be applied to enhance supply chain management techniques.FindingsChaos theory principles are used to assist in the examination of forecasting, product design, and inventory management challenges currently facing supply chain practitioners.Research limitations/implicationsApplication of chaos theory to various supply chain issues and key functional areas may produce an increase in the level of understanding of supply chain ambiguity and how chaos theory may provide valuable insight into the effective management of supply chain networks.Practical implicationsWhen applied correctly, chaos theory shows potential to be a tool that can be instrumental in helping explain why unpredictability occurs...

Observations of Chaotic Behaviour in Nonlinear Inventory Models

International Journal of Applied Industrial Engineering, 2019

This article describes the use of simulation to investigate incipient chaotic behaviour in inventory models. Model structures investigated were either capacity limited or of variable delay time, implemented in discrete and continuous transform algebras. Results indicate the absence of chaos for a continuous time model but gave limited evidence for chaos in both unrestricted discrete models and those with a positive orders only limit. The responses where interaction with the capacity limit occurred did not confirm chaotic behaviour at odds with published results. Using the Liapunov exponent as a measure of chaotic behaviour, the results indicated, where the delay varies in proportion to order rate, a larger fixed delay reduced the Liapunov exponent as did increasing the dependence of delay on order rate. The effect of the model structures showed that the IOBPCS model, produced the largest Liapunov exponent. Reducing the discrete model update time reduced the Liapunov exponent.

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.

Supply chain optimization using chaotic differential evolution method

Systems, Man and Cybernetics, …

This paper describes the application of differential evolution approaches to the optimization of a supply chain. Although simplified, this supply chain included stocks, production, transportation and distribution, in an integrated production-inventory-distribution system. The supply chain problem model is presented as well as a short introduction to each evolutionary algorithm. Differential evolution (DE) is an emergent evolutionary algorithm that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. Inspired by the chaos theory, this work presents a new global optimization algorithm based on different DE approaches combined with chaotic sequences (DEC), called chaotic differential evolution algorithm. The performance of three evolutionary algorithm approaches (genetic algorithm, DE and DEC) and branch and bound method were evaluated with numerical simulations. Results were also compared with other similar approach in the literature. DEC was the algorithm that led to better results, outperforming previously published solutions. The simplicity and robustness of evolutionary algorithms in general, and the efficiency of DEC, in particular, suggest their great utility for the supply chain optimization problem, as well as other logistics-related problems.

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.

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.

Analysis of decision-making in economic chaos control

Nonlinear Analysis: Real World Applications, 2009

In some economic chaotic systems, players are concerned about whether their performance is improved besides taking some methods to control chaos. In the face of chaos occurring in competition, whether one player takes controlling measures or not affects not only their own earning but also other opponents' income. An output duopoly competing evolution model with bounded rationality is introduced in this paper. Using modern game theory, decision-making analyses about chaos control of the model are taken by taking aggregate profits as players' payoff. It is found that the speed of players' response to the market and whether the decisive parameters are in the stable region of the Nash equilibrium or not have a distinct influence on the results of the game. The impact of cost function' type on results of the game is also found. The mechanism of influences is discovered by using numerical simulation.