Tag Archives: multiobjective

“Advanced Topics in Applied Operations Management” ed. by Yair Holtzman

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"Advanced Topics in Applied Operations Management" ed. by Yair Holtzman
InTeOpP | 2012 | ISBN: 9535103458 | 209 pages | PDF | 11 MB
This volume creatively demonstrates a valuable connection among operations strategy, operations management, operations research, and various departments, systems, and practices throughout an organization. The authors show how mathematical tools and process improvements can be applied effectively in unique measures to other functions.

“Advanced Topics in Applied Operations Management” ed. by Yair Holtzman
The book provides examples that illustrate the challenges confronting firms competing in today's demanding environment bridging the gap between theory and practice by analyzing real situations.

Part 1: Competing with Operations
1 Lean Six Sigma in the Service Industry
2 Economic Impact of the Adoption of Enterprise Resource Planning Systems: A Theoretical Framework
3 Utilizing Innovation and Strategic Research and Development to Catalyze Efficient and Effective New Product Development
Part 2: Process Strategy and Process Analysis
4 A Value Structured Approach to Conflicts in Environmental Management
5 Game Theoretic Analysis of Standby Systems
6 Harnessing Efficiency and Building Effectiveness in the Tax Department
Part 3 Mathematical Methods for Evaluating Operations
7 Some Remarks About Negative Efficiencies in DEA ModelsE
8 Quality of Life Modelling on the Basis of Qualitative and Quantitative Data
9 A Ranking for the Vancouver 2010 Winter Olympic Games Based on a Hierarchical Copeland Method
10 Benchmarking Distance Learning Centers with a Multiobjective Data Envelopment Analysis Model
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Introduction to Evolutionary Algorithms (Decision Engineering)

Xinjie Yu, Mitsuo Gen, "Introduction to Evolutionary Algorithms (Decision Engineering)"
English | 2010 | ISBN: 184996128X | 418 pages | PDF | 4,6 MB
Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc.

This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm optimization, artificial immune systems, artificial life, genetic programming, etc.

It emphasises the initiative ideas of the algorithm, contains discussions in the contexts, and suggests further readings and possible research projects. All the methods form a pedagogical way to make EAs easy and interesting.

This textbook also introduces the applications of EAs as many as possible. At least one real-life application is introduced by the end of almost every chapter. The authors focus on the kernel part of applications, such as how to model real-life problems, how to encode and decode the individuals, how to design effective search operators according to the chromosome structures, etc.

This textbook adopts pedagogical ways of making EAs easy and interesting. Its methods include an introduction at the beginning of each chapter, emphasising the initiative, discussions in the contexts, summaries at the end of every chapter, suggested further reading, exercises, and possible research projects.

Introduction to Evolutionary Algorithms will enable students to:

• establish a strong background on evolutionary algorithms;

• appreciate the cutting edge of EAs;

• perform their own research projects by simulating the application introduced in the book; and

• apply their intuitive ideas to academic search.

This book is aimed at senior undergraduate students or first-year graduate students as a textbook or self-study material.

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