Hybrid Simulation Models Of Production Networks

Industrial production is one of the most basic human activities indispensable to the economic activity. Due to its complexity, production is not very well understood and modeled as opposed to traditional fields of inquiry such as physics. This book aims at enhancing rigorous understanding of a particular area of production, that of analysis and optimization of production lines and networks using discrete event models and simulation. To our knowledge, this is the first book treating this subject from the point of view mentioned above. We have arrived at the realization that discrete event models and simulation provide perhaps the best tools to model production lines and networks for a number of reasons. Analysis is precise but demands enormous computational resources, usually unavailable in practical situations. Brute force simulation is also precise but slow when quick decisions are to be made. Approximate analytical models are fast but often unreliable as far as accuracy is concerned. The approach of the book, on the other hand, combines speed and accuracy to an exceptional degree in most practical applications.

Industrial production is one of the most basic human activities indispensable to the economic activity. Due to its complexity, production is not very well understood and modeled as opposed to traditional fields of inquiry such as physics. This book aims at enhancing rigorous understanding of a particular area of production, that of analysis and optimization of production lines and networks using discrete event models and simulation. To our knowledge, this is the first book treating this subject from the point of view mentioned above. We have arrived at the realization that discrete event models and simulation provide perhaps the best tools to model production lines and networks for a number of reasons. Analysis is precise but demands enormous computational resources, usually unavailable in practical situations. Brute force simulation is also precise but slow when quick decisions are to be made. Approximate analytical models are fast but often unreliable as far as accuracy is concerned. The approach of the book, on the other hand, combines speed and accuracy to an exceptional degree in most practical applications.

Thomas Kirschstein provides an overview on methods and approaches for planning and optimizing large-scale chemical production networks. The focus is on an integrated modelling of chemical production processes, logistical processes as well as environmental effects. Therefore, a hybrid simulation framework is designed taking into account time series models for modelling chemical production processes, linear optimization models for describing logistical processes as well as stochastic processes for modelling environmental effects.

Doctoral Thesis / Dissertation from the year 2011 in the subject Business economics - Supply, Production, Logistics, grade: Doctor of Philosophy, University of Bremen, language: English, abstract: We study stability properties of interconnected hybrid systems with application to large-scale logistics networks. Hybrid systems are dynamical systems that combine two types of dynamics: continuous and discrete. Such behaviour occurs in wide range of applications. Logistics networks are one of such applications, where the continuous dynamics occurs in the production and processing of material and the discrete one in the picking up and delivering of material. Stability of logistics networks characterizes their robustness to the changes occurring in the network. However, the hybrid dynamics and the large size of the network lead to complexity of the stability analysis. In this thesis we show how the behaviour of a logistics networks can be described by interconnected hybrid systems. Then we recall the small gain conditions used in the stability analysis of continuous and discrete systems and extend them to establish input-to- state stability (ISS) of interconnected hybrid systems. We give the mixed small gain condition in a matrix form, where one matrix describes the interconnection structure of the system and the second diagonal matrix takes into account whether ISS condition for a subsystem is formulated in the maximization or the summation sense. The small gain condition is sufficient for ISS of an interconnected hybrid system and can be applied to an interconnection of an arbitrary finite number of ISS subsystems. We also show an application of this condition to particular subclasses of hybrid systems: impulsive systems, comparison systems and the systems with stability of only a part of the state. Furthermore, we introduce an approach for structure-preserving model reduction for large-scale logistics networks. This approach supposes to aggregate typical interconnection patterns (motifs) of the network graph. Such reduction allows to decrease the number of computations needed to verify the small gain condition.

No other book has been published giving a single-volume introduction and survey to production planning in distributed manufacturing networks. The published literature so far includes conference proceedings only.

This review volume is devoted to a discussion of analogies and differences of complex production systems ? natural, as in biological cells, or man-made, as in economic systems or industrial production. Taking this unified look at production is based on two observations: Cells and many biological networks are complex production units that have evolved to solve production problems in a reliable and optimal way in a highly stochastic environment. On the other hand, industrial production is becoming increasingly complex and often hard to predict. As a result, modeling and control of such production networks involve many different spatial and temporal scales and decision policies for many different structures. The common themes of industrial and biological production include evolution and optimization, synchronization and self-organization, robust operation despite high stochasticity, and hierarchical dynamics. The mathematical techniques used come from dynamical systems theory, transport equations, control theory, pattern formation, graph theory, discrete event simulations, stochastic processes, and others. The application areas range from semiconductor production to supply chains, protein networks, slime molds, social networks, and whole economies.