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Optimization and Logistics Challenges in the Enterprise
In a world with highly competitive markets and economic instability due to capitalization, industrial competition has increasingly intensified. In order for many industries to survive and succeed, they need to develop highly effective coordination between supply chain partners, dynamic collaborative and strategic alliance relationships, and efficient logistics and supply chain network designs. Consequently, in the past decade, there has been an explosion of interest among academic researchers and industrial practitioners in innovative supply chain and logistics models, algorithms, and coordination policies. Mathematically distinct from classical supply chain management, this emerging research area has been proven to be useful and applicable to a wide variety of industries. This book brings together recent advances in supply chain and logistics research and computational optimization that apply to a collaborative environment in the enterprise.
Optimization Problems in Self-Organizing Networks
Modern computer networks or wireless ad-hoc networks offer a wide range of interesting optimization problems. Usual optimization goals are the minimization of the message delay in a Peer-to-Peer system or the minimization of the energy consumption of a wireless network. This thesis presents different kinds of algorithms to solve such optimization problems. Starting from the mathematical formulations for these problems, various global view optimization algorithms are presented. These algorithms are based on evolutionary algorithms and local search or similar heuristics. They can be used to quickly find near-optimal solutions, if a global view of the network is possible. As the participants in a computer network or a wireless ad-hoc network are autonomous nodes, distributed algorithms can be designed that enable these nodes to collectively solve the optimization problem. Four distributed algorithms are formulated and evaluated in this thesis, thus laying grounds for distributed optimization of networks. Using these algorithms, the network can be modelled as a self-optimizing network and the optimization problem can be approached without global view.
Managing Professional Service Delivery
Although the nature of service delivery varies significantly from profession to profession, the way the service is delivered tends to be fairly consistent among professions-or, at least, it should be. A step-by-step guide, Managing Professional Service Delivery-9 Rules for Success describes in detail how to achieve the internal discipline and contr
Laser and Photonic Systems
New, significant scientific discoveries in laser and photonic technologies, systems perspectives, and integrated design approaches can improve even further the impact in critical areas of challenge. Yet this knowledge is dispersed across several disciplines and research arenas. Laser and Photonic Systems: Design and Integration brings together a mu
Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond
Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a “hard nut to crack”. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions.
Proceedings of 20th International Conference on Industrial Engineering and Engineering Management
The International Conference on Industrial Engineering and Engineering Management is sponsored by the Chinese Industrial Engineering Institution, CMES, which is the only national-level academic society for Industrial Engineering. The conference is held annually as the major event in this arena. Being the largest and the most authoritative international academic conference held in China, it provides an academic platform for experts and entrepreneurs in the areas of international industrial engineering and management to exchange their research findings. Many experts in various fields from China and around the world gather together at the conference to review, exchange, summarize and promote th...
Real Options in Engineering Design, Operations, and Management
Given that engineering flexibility can potentially provide a competitive advantage, the question then becomes: Precisely how valuable is this flexibility? However, traditional methods often fail to accurately capture the economic value of investments in an environment of widespread uncertainty and rapid change. The real options method represents th
Ergonomics and Human Factors in Safety Management
Accident prevention is a common thread throughout every aspect of our society. However, even with the most current technological developments, keeping people safe and healthy, both at workplaces and at other daily activities, is still a continual challenge. When it comes to work environments, ergonomics and human factors knowledge can play an important role and, therefore, must be included in, or be a part of, the safety management as a cross-disciplinary area concerned with the understanding of actual work situations and potential variables. This multidisciplinary approach will ultimately ensure the safety, health, and well-being of all collaborators. The main goal of this book is to presen...
Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Stochastic Simulation Optimization
With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart ...
