Gang Yu – författare

This text deals with decision-making in environments of significant data uncertainty, with particular emphasis on operations and production management applications. For such environments, this text suggests the use of the robustness approach to decision making, which assumes inadequate knowledge of the decision-maker about the random state of nature and develops a decision that hedges against the worst contingency that may arize. The book provides a comprehensive mathematical programming framework for robust decision making. It also presents decision support tools and solution methods for generating robust decisions in a variety of interesting application environments such as: linear programming; assignment problems; shortest paths; minimum spanning trees; knapsack problems; resource allocation; scheduling; production planning; location; inventory; layout planning; network design; and international sourcing.

This text demonstrates industrial applications of combinatorial optimization. A range of applications is described including: manpower planning; production planning; job sequencing and scheduling; manufacturing layout design; facility Planning; vehicle scheduling and routing; retail seasonal planning, space shuttle scheduling, and telecommunication network design. A representative set of industry sectors is covered, including electronics, airlines, manufacturing, tobacco, retail, telecommunication, defense, and livestock. These examples illustrate the importance and practicality of optimization which is beginning to be realized by management of various organizations, as well as some of the pioneering developments in this field now beginning to bear fruit. Audience: Researchers and teachers in the fields of operations research/management, applied mathematics, management science, and system and industrial engineering; also managers, analysts, and system developers responsible for planning, scheduling, management, control, manpower deployment, distribution, procurement, and so forth.

The field of operations research has had a tremendous impact on the management of today's air transportation. Driven by enormous demand from management to gain a competitive advantage in the market, airlines are turning to advanced optimization techniques to develop mission-critical decision support systems for management and control of airline operations. The purpose of this text is to show some recent advances in optimization techniques and decision support systems applications in air transportation. The book covers a wide variety of operations research topics in the air transportation industry including: demand forecasting; network design; revenue management; route planning; airline schedule planning; irregular operations aircraft routing; integrated scheduling; real-time crew management; crew pairing optimization; air traffic flow management; crew rostering; airport traffic simulation and control; and coping with the FAA's traffic control programme.

This book deals with decision making in environments of significant data uncertainty, with particular emphasis on operations and production management applications. For such environments, we suggest the use of the robustness approach to decision making, which assumes inadequate knowledge of the decision maker about the random state of nature and develops a decision that hedges against the worst contingency that may arise. Robust Discrete Optimization is a comprehensive mathematical programming framework for robust decision making. This book takes a giant first step in presenting decision support tools and solution methods for generating robust decisions in a variety of interesting application environments such as: linear programming, assignment problems, shortest paths, minimum spanning trees, knapsack problems, resource allocation, scheduling, production planning, location, inventory, layout planning, network design, and international sourcing. Beyond theoretical results, the book provides many suggestions and useful advice to the practitioners of the robustness approach.Emphasis is placed upon the assessment of the decision environment for applicability of the approach, structuring of data uncertainty and the scenario generation process, choice of appropriate robustness criteria, and formulation and solution of robust decision problems. Audience: The book will be of interest to researchers, practitioners and graduate students working in the fields of operations research, management science, industrial and systems engineering, computer science, decision analysis and applied mathematics.

This book demonstrates industrial applications of combinatorial optimization - optimization that involves a discrete but large number of alternatives. A wide range of applications is described including: Manpower planning, Production planning, Job sequencing and scheduling, Manufacturing layout design, Facility planning, Vehicle scheduling and routing, Retail seasonal planning, Space shuttle scheduling, and Telecommunication network design. A representative set of industry sectors is covered, including electronics, airlines, manufacturing, tobacco, retail, telecommunication, defense, and livestock. These examples illustrate the importance and practicality of optimization which is beginning to be realized by management of various organizations, as well as some of the pioneering developments in this field now beginning to bear fruit. Audience: Researchers and teachers in the fields of operations research/management, applied mathematics, management science, and system and industrial engineering; also managers, analysts, and system developers responsible for planning, scheduling, management, control, manpower deployment, distribution, procurement, and so forth.

260 2 Crew Legalities and Crew Pairing Repair 264 3 Model and Mathematical Formulation 266 4 Solution Methodology 271 5 Computational Experiences 277 6 Conclusion 285 REFERENCES 286 10 THE USE OF OPTIMIZATION TO PERFORM AIR TRAFFIC FLOW MANAGEMENT Kenneth Lindsay, E. Andrew Boyd, George Booth, and Charles Harvey 287 1 Introduction 288 2 The Traffic Flow Management (TFM) Problem 289 3 Recent TFM Optimization Models 292 4 The Time Assignment Model (TAM) 302 5 Summary and Conclusions 307 REFERENCES 309 11 THE PROCESSES OF AIRLINE SYSTEM OPERATIONS CONTROL Seth C. Grandeau, Michael D. Clarke, and Dennis F.X. Mathaisel 312 1 Introduction 313 2 The Four Phases of Airline Schedule Development 315 The Airline Operations Control Center (OCC) 3 320 4 Analysis of Operational Problems 331 5 Areas For Improvement 352 6 Case Study: PT Garuda Indonesia Airlines 357 REFERENCES 368 12 THE COMPLEX CONFIGURATION MODEL Bruce W. Patty and Jim Diamond 370 1 Introduction 370 Problem Description 2 371 Problem Formulation 3 375 4 Model Implementation 379 ix Contents 383 5 Summary REFERENCES 383 13 INTEGRATED AIRLINE SCHEDULE PLANNING Cynthia Barnhart, Fang Lu, and Rajesh Shenoi 384 1 Introduction 385 2 Fleet Assignment and Crew Pairing Problems: Existing M- els and Algorithms 388 3 An Integrated Approximate Fleet Assignment and Crew Pa- ing Model 393 4 An Advanced Integrated Solution Approach 395 5 Case Study 396 6 Conclusions and Future Research Directions 399 REFERENCES 401 14 AIRLINE SCHEDULE PERTURBATION PROBLEM: LANDING AND TAKEOFF WITH

ovatus, including feed and feeding, environmental manipulation, hatchery management, and fingerlings in transportation. Chapter eight and onwards decode the possible molecular mechanisms underlying fish development and response to environmental changes and discuss the transcription and expressions of growth and development-related genes in T.

ovatus, including feed and feeding, environmental manipulation, hatchery management, and fingerlings in transportation. Chapter eight and onwards decode the possible molecular mechanisms underlying fish development and response to environmental changes and discuss the transcription and expressions of growth and development-related genes in T.

This pioneering book addresses the latest research findings and application results on disruption management, which is the study of how to dynamically recover a predetermined operational plan when various disruptions prevent the original plan from being executed smoothly. A disruption management system will help decision-makers respond to disruptive events in real time so that the cost incurred by the disruption is reduced to a minimum. The impact of such systems is significant. For example, each year the disruption management system for US airlines generates savings of tens of millions of dollars.