Inhaltsangabe:Introduction: As the world population is growing continuously and emerging markets are expanding, natural recourses are being used even more intensively. Because of the scarcity of natural resources, industry faces a changing business environment. Due to government regulations, companies nowadays must handle not only in terms of efficiency, but also of sustainable development and new market opportunities. Thus, with the progression of the logistics sector in recent years, supply chain management and especially the concept of reverse logistics have become more important for both, industry and science. By utilizing reverse logistics, companies aim at maximizing their product revenue while reducing the costs of product returns. Accordingly, implementing an effective concept of reverse logistics, while manufacturing environmentally friendly products, has become a strategic issue. In order to meet the requirements, companies are confronted with the problem of reducing the uncertainties regarding the quality, quantity and timing of the product returns. In this context, a high level of uncertainty leads to a strong increase in complexity compared to the traditional forward supply chains. Using modern computer aided modelling techniques such as system dynamics, helps to counteract this complexity since they not only enable a better understanding of the dynamic behaviour of such complex systems but also allow an improved estimation of the impact of a changing environment and management decisions. This thesis contributes towards an improvement of the strategic decision making process in the field of reverse logistics by providing a generic simulation model which can be used to analyse the influence of different environmental and economical policies with respect to prevailing market conditions. To achieve this objective, the following approach is proposed: In Chapter 2, the theoretical foundation of reverse logistics is characterized forming the framework for the subsequent analytical approach concerning the appropriate model development. For this purpose, first, an overview of the state of the art concerning the processes and influencing factors within the field of reverse logistics is provided. This is achieved by describing the theoretical background of the topic, including a characterization of the impact of individual reverse logistic activities on each other and on their environment. Afterwards, current challenges and trends when [...]

As legislations have become stricter and the competition on markets is getting stronger, companies facing return flows strive for the implementation of efficient and cost-effective reverse logistic procedures. At the same time, when managing reverse logistics, they are not only confronted with a high degree of uncertainties concerning the quality, quantity and timing or the product returns, but also with a dynamically changing environment. Various aspects, such the increasing amount of return flows, shorter repair and lead times as well as increasing disposal costs, affect the reverse logistic system and need to be managed proficiently. Additionally, handling product returns requires supportive computer aided modelling tools that are capable of handling the dynamic and complex characteristics of the reverse logistic system and allow an improved estimation of the impact of a changing environment and management decisions. For the purpose of this study, the system dynamics modelling approach has been identified as particularly suitable for illustrating the system in question with a special focus on understanding the dynamic behaviour over time. A generic system dynamics model has been exemplarily created and simulated using the program iThink. The model comprises end-to-end processes of the main reverse logistic activities related to customer returns and has been used for studying the strategic design and optimization of the reverse logistic system. In order to consider relevant uncertainties as well as environmental concerns and economic efficiency, representative policies have been applied where, inter alia, with the help of the graphical illustration of the processes, effective strategies could be implemented. A general evaluation of the system dynamics methodology has revealed the significant advantages of using supportive modelling techniques for strategic decision making. Particularly for complex systems that change over time, such as reverse logistics, applying appropriate computer aided modelling tools in order to anticipate the overall effect on processes caused by varying surroundings has proven essential. An effective utilization of system dynamics may significantly reduce the forecasting and planning risks within individual frameworks, such as capacity planning. Moreover, the generic approach allows the application of the model to any other industry that is characterized by uncertain capacity utilization and varying technical, economical and legal conditions.

The 29th European Symposium on Computer Aided Process Engineering, contains the papers presented at the 29th European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Eindhoven, The Netherlands, from June 16-19, 2019. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. - Presents findings and discussions from the 29th European Symposium of Computer Aided Process Engineering (ESCAPE) event

1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities is now well established and has found its way into various textbooks including inter alia Llitkepohl (1991), Hamilton (1994), Enders (1995), Hendry (1995) and Greene (2002). The unrestricted VAR model provides a general and very flexible framework that proved to be useful to summarize the data characteristics of economic time series. Unfortunately, the flexibility of these models causes severe problems: In an unrestricted VAR model, each variable is expressed as a linear function of lagged values of itself and all other variables in the system.

Providing a comprehensive overview of various methods and applications in decision engineering, this book presents chapters written by a range experts in the field. It presents conceptual aspects of decision support applications in various areas including finance, vendor selection, construction, process management, water management and energy, agribusiness , production scheduling and control, and waste management. In addition to this, a special focus is given to methods of multi-criteria decision analysis. Decision making in organizations is a recurrent theme and is essential for business continuity. Managers from various fields including public, private, industrial, trading or service sectors are required to make decisions. Consequently managers need the support of these structured methods in order to engage in effective decision making. This book provides a valuable resource for graduate students, professors and researchers of decision analysis, multi-criteria decision analysis and group decision analysis. It is also intended for production engineers, civil engineers and engineering consultants.

Soft Computing Techniques in Solid Waste and Wastewater Management is a thorough guide to computational solutions for researchers working in solid waste and wastewater management operations. This book covers in-depth analysis of process variables, their effects on overall efficiencies, and optimal conditions and procedures to improve performance using soft computing techniques. These topics coupled with the systematic analyses described will help readers understand various techniques that can be effectively used to achieve the highest performance. In-depth case studies along with discussions on applications of various soft-computing techniques help readers control waste processes and come up with short-term, mid-term and long-term strategies. Waste management is an increasingly important field due to rapidly increasing levels of waste production around the world. Numerous potential solutions for reducing waste production are underway, including applications of machine learning and computational studies on waste management processes. This book details the diverse approaches and techniques in these fields, providing a single source of information researchers and industry practitioners. It is ideal for academics, researchers and engineers in waste management, environmental science, environmental engineering and computing, with relation to environmental science and waste management. - Provides a comprehensive reference on the implementation of soft computing techniques in waste management, drawing together current research and future implications - Includes detailed algorithms used, enabling authors to understand and appreciate potential applications - Presents relevant case studies in solid and wastewater management that show real-world applications of discussed technologies

This six-volume-set (CCIS 231, 232, 233, 234, 235, 236) constitutes the refereed proceedings of the International Conference on Computing, Information and Control, ICCIC 2011, held in Wuhan, China, in September 2011. The papers are organized in two volumes on Innovative Computing and Information (CCIS 231 and 232), two volumes on Computing and Intelligent Systems (CCIS 233 and 234), and in two volumes on Information and Management Engineering (CCIS 235 and 236).

Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

Industry and government are increasingly reliant on an intelligent – or ‘smart’ – and interconnected computer infrastructure, but the reality is that it is extremely difficult to provide full cyber defense and/or intrusion prevention for the smart networks that connect intelligent industrial and logistics modules, since the more intelligent the systems are, the more vulnerable they become. This book presents papers from the NATO Advanced Research Workshop (ARW) on Cyber Defence in Industry 4.0 Systems and Related Logistics and IT Infrastructures, held in Jyvaskyla, Finland, in October 2017. The main focus of the 11 papers included here is the creation and implementation of cyber systems and cyber platforms capable of providing enhanced cyber security and interoperability for smart IT infrastructure. Topics covered include: smart intrusion prevention; adaptive cyber defense; smart recovery of systems; and the smart monitoring, control and management of Industry 4.0 complexes and related logistics systems such as robotic equipment, logistics modules, units and technologic equipment, as well as their IT infrastructure.

This book provides an innovative, realistic and reliable solution to the common problem of Indian water and energy sector due to the onset of the Impact of Climate Change and Large-Scale Urbanization. Twelve Case Studies and One Review Paper that were included in this book depict the way soft computation techniques, simulation and decision-making framework can optimize the best solution from multiple solutions to the problems of water and energy management which corresponds to a novel symbiotic and synchronous nexus between water and the energy sector. All the studies included in this book are collected from all parts of India. The selected studies utilized the latest technologies like Multi-Criteria Decision Frame Work, Neural Networks and Nature-Based Optimization techniques to achieve diverse objectives from the prediction of climatic parameters to yield from ungauged watershed to performance optimization of Water Treatment Plant, Hydropower as well as futuristic alternative energy systems like Wave to Power Plants.