Brad Stone enjoyed unprecedented access to current and former Amazon employees and Bezos family members, and his book is the first in-depth, fly-on-the-wall account of life at Amazon. The Everything Store is the book that the business world can't stop talking about, the revealing, definitive biography of the company that placed one of the first and largest bets on the Internet and forever changed the way we shop and read.
With many detailed examples from companies that have put time-based strategies in place, such as Federal Express, Ford, Milliken, Honda, Deere, Toyota, Sun Microsystems, Wal-Mart, Citicorp, Harley-Davidson, and Mitsubishi, the authors describe exactly how reducing elapsed time can make the critical difference between success and failure. Give customers what they want when they want it, or the competition will. Time-based companies are offering greater varieties of products and services, at lower costs, and with quicker delivery times than their more pedestrian competitors. Moreover, the authors show that by refocusing their organizations on responsiveness, companies are discovering that long-held assumptions about the behavior of costs and customers are not true: Costs do not increase when lead times are reduced; they decline. Costs do not increase with greater investment in quality; they decrease. Costs do not go up when product variety is increased and response time is decreased; they go down. And contrary to a commonly held belief that customer demand would be only marginally improved by expanded product choice and better responsiveness, the authors show that the actual results have been an explosion in the demand for the product or service of a time-sensitive competitor, in most cases catapulting it into the most profitable segments of its markets.
With persuasive evidence, Stalk and Hout document that time consumption, like cost, is quantifiable and therefore manageable. Today's new-generation companies recognize time as the fourth dimension of competitiveness and, as a result, operate with flexible manufacturing and rapid-response systems, and place extraordinary emphasis on R&D and innovation. Factories are close to the customers they serve. Organizations are structured to produce fast responses rather than low costs and control. Companies concentrate on reducing if not eliminating delays and using their response advantage to attract the most profitable customers.
Stalk and Hout conclude that virtually all businesses can use time as a competitive weapon. In industry after industry, they illustrate the processes involved in becoming a time-based competitor and the ways managers can open and sustain a significant advantage over the competition.
This book will help you:Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.
Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
“Artfully envisions a breathtakingly better world.” —Los Angeles Times
“Elaborate, smart and persuasive.” —The Boston Globe
“A pleasure to read.” —The Wall Street Journal
One of CBS News’s Best Fall Books of 2005 • Among St Louis Post-Dispatch’s Best Nonfiction Books of 2005 • One of Amazon.com’s Best Science Books of 2005
A radical and optimistic view of the future course of human development from the bestselling author of How to Create a Mind and The Age of Spiritual Machines who Bill Gates calls “the best person I know at predicting the future of artificial intelligence”
For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.
From the Trade Paperback edition.
A Huffington Post Definitive Tech Book of 2013
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Students taking MBA, MSc and MBM classes on operations management, advanced operations management, and strategic operations management will find this textbook fulfills all their requirements whilst advanced undergraduate classes in these areas will also find the book an essential read.
The Goal: A Process of Ongoing Improvement by Eliyahu Goldratt and Jeff Cox describes a process by which an unprofitable manufacturing operation can be made profitable. It conveys proven factory turnaround principles through a fictional story…
PLEASE NOTE: This is key takeaways and analysis of the book and NOT the original book.
Inside this Instaread of The Goal:Overview of the bookImportant PeopleKey TakeawaysAnalysis of Key Takeaways
Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications of vastly increasing the powers of our intelligence in addressing the world’s problems. He thoughtfully examines emotional and moral intelligence and the origins of consciousness and envisions the radical possibilities of our merging with the intelligent technology we are creating.
Certain to be one of the most widely discussed and debated science books of the year, How to Create a Mind is sure to take its place alongside Kurzweil’s previous classics which include Fantastic Voyage: Live Long Enough to Live Forever and The Age of Spiritual Machines.
From the Hardcover edition.
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States.
Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 – 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.
Meyer argues that fast cycle time is achieved not by working faster, but by aligning the organization's purpose, strategy and structure. He demonstrates how the product development cycle must become a learning laboratory in which the four continuous elements "Design, Fabricate, Assemble, and Test" are analyzed with the intent to improve strategy in the next business cycle. Analyzing strategy and core processes enables management to detect and correct problems earlier, and leverage knowledge for improved innovation and increased value for customers.
Employing an ongoing case study, Core Products, Inc., throughout the text, Meyer shows how to redesign the organization for manufacturability and assembly, how to implement multifunctional teams that work, how to analyze and map critical cycle time interdependencies such as "co-location," and how to measure the impact of cycle time on business performance. Meyer's practical approach provides a simple methodology for organizations to deliver products to customers rapidly, accurately, and reliably.
"Chris Meyer interrelates many pieces that we have all read about in different places into a coherent guide to making it happen. Ironically, as Meyer shows, implementing fast cycle time means almost the opposite of what most American managers are inclined to do...Many years of practical experience have shown Meyer and his colleagues the wisdom of a paradox—that to speed up you often have to slow down."
—From the Foreword by Peter M. Senge
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.
But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.
Each chapter will cover a different technique in a spreadsheet so you can follow along:Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
Statistical process control is a tool, which enables both manufacturers and suppliers to achieve control of product quality by means of the application of statistical methods in the controlling process. This book gives the foundations of good quality management and process control, including an explanation of what quality is, and control of conformance and consistency during production. The text offers clear guidance and help to those unfamiliar with either quality control or statistical applications and coves all the necessary theory and techniques in a practical and non-mathematical manner. This book will be essential reading for anyone wishing to understand or implement modern statistical process control techniques.
The 21 self-contained chapters in this volume are devoted to the examination of modern trends and open problems in the field of optimization. This book will be a valuable tool not only to specialists interested in the technical detail and various applications presented, but also to researchers interested in building upon the book’s theoretical results.
In the editors' previous work on traditional wired networks, we have observed that designing low cost, survivable telecommunication networks involves extremely complicated processes. Commercial products available to help with this task typically have been based on simulation and/or proprietary heuristics. As demonstrated in this book, however, mathematical programming deserves a prominent place in the designer's toolkit. Convenient modeling languages and powerful optimization solvers have greatly facilitated the implementation of mathematical programming theory into the practice of commercial network design.
These points are equally relevant and applicable in today’s world of wireless network technology and design. But there are new issues as well: many wireless network design decisions, such as routing and facility/element location, must be dealt with in innovative ways that are unique and distinct from wired (fiber optic) networks. The book specifically treats the recent research and the use of modeling languages and network optimization techniques that are playing particularly important and distinctive roles in the wireless domain.
See what’s in the Second Edition:
New chapters include Order Statistics, Traffic Flow and Delay, and Heuristic Search Methods New sections include Distance Norms, Hyper-Exponential and Hypo-Exponential Distributions Newly derived formulas and an expanded reference list
Like its predecessor, the new edition of this handbook presents the analytical results and formulas needed in the scientific applications of operations research and management. It continues to provide quick calculations and insight into system performance. Presenting practical results and formulas without derivations, the material is organized by topic and offered in a concise format that allows ready-access to a wide range of results in a single volume.
The field of operations research encompasses a growing number of technical areas, and uses analyses and techniques from a variety of branches of mathematics, statistics, and other scientific disciplines. And as the field continues to grow, there is an even greater need for key results to be summarized and easily accessible in one reference volume. Yet many of the important results and formulas are widely scattered among different textbooks and journals and are often hard to find in the midst of mathematical derivations. This book provides a one-stop resource for many important results and formulas needed in operations research and management science applications.
This book is the first comprehensive reference book to be published in the field of RM.
It unifies the field, drawing from industry sources as well as relevant research from disparate disciplines, as well as documenting industry practices and implementation details.
Successful hardcover version published in April 2004.
For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.
There is a selected solutions manual for instructors for the new edition.
Tom DeMarco, a leading management consultant to both Fortune 500 and up-and-coming companies, has discovered a counterintuitive principle that explains why efficiency improvement can sometimes make a company slow. If your real organizational goal is to become fast (responsive and agile), then he proposes that what you need is not more efficiency, but more slack.
What is “slack”? Slack is the degree of freedom in a company that allows it to change. It could be something as simple as adding an assistant to a department, letting high-priced talent spend less time at the photo copier and more time making key decisions. Slack could also appear in the way a company treats employees: instead of loading them up with overwork, a company designed with slack allows its people room to breathe, increase effectiveness, and reinvent themselves.
In thirty—three short chapters filled with creative learning tools and charts, you and your company can learn how to:
∑make sense of the Efficiency/Flexibility quandary
∑run directly toward risk instead of away from it
∑strengthen the creative role of middle management
∑make change and growth work together for even greater profits
A innovative approach that works for new- and old-economy companies alike, this revolutionary handbook will debunk commonly held assumptions about real-world management, and give you and your company a brand-new model for achieving and maintaining true effectiveness—and a healthier bottom line.
From the Hardcover edition.
From the Trade Paperback edition.
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches. It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material.
Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves.
The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Knowledge flow — A mobile learning platform provides Apps and Books.
Knowledge flow provides learning book of Operations Research. This book brings essential reference with detailed illustrations for operation research, whether students, teachers or professionals across the world. This book of operations research based on management and engineering operations research courses and this operation research book covers basic concepts such as optimization, game theory, networks, and transport operations.
2. Game Theory
3. Queuing Systems
4. Discrete Event Simulation
5. Computer and Telecommunication Networks
6. Financial Engineering
7. Supply Chain Manage
8. Dynamic Programming and Control Problems
9. Transport Operations and Logistics
10. Service Operations Management
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This new edition contains computational exercises in the form of case studies which help understanding optimization methods beyond their theoretical, description, when coming to actual implementation. Besides, the nonsmooth optimization part has been substantially reorganized and expanded.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
New to this edition is a chapter devoted to Conic Linear Programming, a powerful generalization of Linear Programming. Indeed, many conic structures are possible and useful in a variety of applications. It must be recognized, however, that conic linear programming is an advanced topic, requiring special study. Another important topic is an accelerated steepest descent method that exhibits superior convergence properties, and for this reason, has become quite popular. The proof of the convergence property for both standard and accelerated steepest descent methods are presented in Chapter 8. As in previous editions, end-of-chapter exercises appear for all chapters.
From the reviews of the Third Edition:
“... this very well-written book is a classic textbook in Optimization. It should be present in the bookcase of each student, researcher, and specialist from the host of disciplines from which practical optimization applications are drawn.” (Jean-Jacques Strodiot, Zentralblatt MATH, Vol. 1207, 2011)
Focusing on the underlying structure of a system, Optimal Design of Queueing Systems explores how to set the parameters of a queueing system, such as arrival and service rates, before putting it into operation. It considers various objectives, comparing individually optimal (Nash equilibrium), socially optimal, class optimal, and facility optimal flow allocations.
After an introduction to basic design models, the book covers the optimal arrival rate model for a single-facility, single-class queue as well as dynamic algorithms for finding individually or socially optimal arrival rates and prices. It then examines several special cases of multiclass queues, presents models in which the service rate is a decision variable, and extends models and techniques to multifacility queueing systems. Focusing on networks of queues, the final chapters emphasize the qualitative properties of optimal solutions.
Written by a long-time, recognized researcher on models for the optimal design and control of queues and networks of queues, this book frames the issues in the general setting of a queueing system. It shows how design models can control flow to achieve a variety of objectives.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
The framework presented in the book enables the effective interplay of business, analytics, and information technology (business intelligence) both to leverage analytics for competitive advantage and to embed the use of business analytics into the business culture. It lays out an approach for analytics, describes the processes used, and provides guidance on how to scale analytics and how to develop analytics teams. It provides tools to improve analytics in a broad range of business situations, regardless of the level of maturity and the degree of executive sponsorship provided.
As a guide for practitioners and managers, the book will benefit people who work in analytics teams, the managers and leaders who manage, use and sponsor analytics, and those who work with and support business analytics teams.
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.
The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.
The text concentrates on the interpretation, strengths, and weaknesses of analytical techniques, along with challenges encountered by analysts in their daily work. The author shares various lessons learned from applying analytics in the real world. He supplements the technical material with coverage of professional skills traditionally learned through experience, such as project management, analytic communication, and using analysis to inform decisions. Example data sets used in the text are available for download online so that readers can test their own analytic routines.
Suitable for beginning analysts in the sciences, business, engineering, and government, this book provides an accessible, example-driven introduction to the emerging field of analytics. It shows how to interpret data and identify trends across a range of fields.
Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach.
Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field.
This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students.
The authors have made the material accessible to a broad readership, using simplified notation and revealing unifying concepts. The results unique to flow shop research should provide the seed for research in other areas of scheduling and in optimization in general.
Several POM challenges are answered through a comprehensive analysis of concepts and models that assist the selection of outsourcing strategies and dynamic pricing policies. Supply Chain Engineering presents inventory management techniques in supply chains, radio frequency identification (RFID) technologies, and methods for the design of flexible and re-configurable manufacturing systems, as well as real-time assignment and scheduling methods. A significant part of the book is also devoted to:
• lean manufacturing,
• line balancing (assembly lines, U-lines, and bucket brigades),
• dynamic facilities layout approaches, and
• new warehousing techniques.
Explanations are given using basic examples and detailed algorithms, while discarding complex and unnecessary theoretical minutiae. In addition, all the examples have been carefully selected with a view to eventual industrial application.
Supply Chain Engineering is written for students and professors in industrial and systems engineering, management science, operations management, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.
The Operations Research Calculations Handbook meets that need. It contains more than 300 results in a single, concise volume. Organized by topic and listed in a convenient summary format, it allows readers to have frequently used results at their fingertips. Although based on the author's experience in the manufacturing industry, many of the results are basic to system modeling. They carry over easily to applications in other areas of operations research and management science.
While modern software packages are useful for obtaining numerical results, formulas continue to play a significant role in systems modeling. They allow one to draw general conclusions about system behavior, reveal the underlying system model, and help provide an understanding of system performance. Whether you are a student, professor, or seasoned professional, the Operations Research Calculations Handbook offers not only a handy reference that will save time, but also a tool that will help build the intuitive understanding you need to apply systems models with confidence.
The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.
This book will prove useful to researchers, students, and practitioners in transportation and will stimulate further research in this rich and fascinating area.Volume 14 examines transport and its relationship with operations and management science11 chapters cover the most recent research developments in transportationFocuses on main transportation modes-air travel, automobile, public transit, maritime transport, and more
A lean strategy is about gaining a competitive edge by offering better quality products at competitive prices and making a sustainable profit by eliminating waste through engaging employees in discovering deeper ways to think about their own jobs and smarter ways of working together. In its current form, lean has been radically effective, but its true powers have yet to be harnessed.
Lean Strategy harnesses that power and delivers a new way of creating value from lean. Leading lean experts address popular misconceptions about the basics of lean/TPS, showing the true purpose of tools, methods, and attitudes that leverage the intelligence of every employee doing the work. You’ll learn how to think—and then act—differently, tapping the power of every person in your organization in a disciplined manner that generates unparalleled, sustainable success that is responsive to today’s most pressing challenges
Featuring a mix of international authors, Operations Research and Management Science Handbook combines OR/MS models, methods, and applications into one comprehensive, yet concise volume. The first resource to reach for when confronting OR/MS difficulties, this text – Provides a single source guide in OR/MS Bridges theory and practice Covers all topics relevant to OR/MS Offers a quick reference guide for students, researchers and practitioners Contains unified and up-to-date coverage designed and edited with non-experts in mind Discusses software availability for all OR/MS techniques Includes contributions from a mix of domestic and international experts
The 26 chapters in the handbook are divided into two parts. Part I contains 14 chapters that cover the fundamental OR/MS models and methods. Each chapter gives an overview of a particular OR/MS model, its solution methods and illustrates successful applications. Part II of the handbook contains 11 chapters discussing the OR/MS applications in specific areas. They include airlines, e-commerce, energy systems, finance, military, production systems, project management, quality control, reliability, supply chain management and water resources. Part
II ends with a chapter on the future of OR/MS applications.