Optimization Essentials
Description
This book explores recent developments and exciting challenges in operations research and mathematical optimization. It provides the following in a unified and carefully developed presentation: (a) novel problems that have arisen in the real-life optimization domain, highlighting the challenges in each problem; (b) significant methodological advances for solving existing optimization problems, with a special emphasis on large scale problems.
The book assumes a decent understanding of matrix algebra, linear and integer programming, non-linear programming, computational complexity, and graph theory. Each chapter in this book starts with an introduction to the underlying optimization technique. It then explores a real-life case study to which the technique will be applied. The objective is to demonstrate how the underlying technique can be utilized to solve a challenging problem. The chapters offer details on how to formulate a research problem into a formal optimization model, reformulate or transform it (if required) to improve computational tractability, and apply necessary customizations to the optimization technique specific to the underlying problem to derive an optimal or near-optimal solution. The book covers various state-of-the-art methods (both exact and heuristics) and modelling approaches in sync with the current research trends, which are still not discussed in typical graduate-level textbooks. Applications covered in the book span the realms of resource planning, telecommunications, scheduling, logistics, education, environmental conservation, and many others. It is thus a valuable resource for post-graduate students of operations research and mathematical optimization. It also serves as a valuable reference for researchers who wish to explore various optimization techniques as part of their research methodologies. The learning from the book should enable the professionals to apply optimization theory and algorithms to their particular field of interest.More details
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Person
Faiz Hamid is an Associate Professor in the Department of Industrial and Management Engineering at the Indian Institute of Technology (IIT) Kanpur, where he joined as an Assistant Professor in 2014. After completing his PhD in 2012 at the Indian Institute of Management Lucknow with a specialization in Operations Research, he worked on optimization problems for 4G wireless networks as a postdoctoral research fellow at Telecom SudParis, France. Prior to joining IIT Kanpur, he served as a Functional Architect at JDA Software (now Blue Yonder), Hyderabad, for a project on revenue management for passenger railways.
Faiz's research expertise lies primarily in integer programming, combinatorial optimization, data science, and their applications to transportation planning, railways, communications network design, and sports. His research has been published in several international journals of repute. At IIT Kanpur, Faiz has been actively involved with teaching activities at various levels, from undergraduates to Masters and PhD students. He teaches in the broad areas of operations research, management science, and data science, with a strong emphasis on modeling and applying theoretical concepts to real-world problems.
Content
The Many Guises of Linear Programming.- Mixed-Integer Programming Modeling Strategies for Scheduling-Based Optimization Problems.- Converting Weak to Strong MIP Formulations: A Practitioner's Guide.- Valid Inequalities for the Telecommunications Network Design Problem.- Modeling and Solution Approaches for Resource Assignment with Deployment Restrictions.- Modeling and Exact Solution Approaches for the Distance-based Critical Node and Edge Detection Problems.- Benders decomposition: a humanitarian operation with stochastic programming framework.- Delayed Column Generation: Solving large-scale optimization models from the airline industry.- Column Generation and Lagrangian Relaxation: Solving real-world crew (re)scheduling problems.- Dantzig-Wolfe Reformulation and Column Generation: Optimization in the Chemical Tanker Industry.- Volume Algorithm: Training Neural Networks and Solving Difficult Linear Programs.- Exact And Approximation Methods for Mixed Integer Non-LinearProgramming Problem.- Lagrangian Methods and Dynamic Programming-Based MIP Formulations for the Unit Commitment Problem.- Bilevel Optimization: Applications, Models and Solution Approaches.- Modeling and Solving Robust Chance-Constrained Binary Programs Using Sample Average Approximations.- Production Planning And Scheduling on Parallel Machines with Sequence Dependent Setup Times.- Facility Location Problem: A Case Study of School Consolidation.- Quantitative Models in Railway Operations Management.- Rail Yard Optimization.- Solving a Large-Scale Multi-Depot Vehicle Routing Problem Heuristically.- Effective Metaheuristic Algorithms for Platelet Collection Routing Problem.- NSGA-II and TOPSIS for a Multi-Objective Vehicle Routing Problem with Ecological Considerations.- Modelling and Solving Difficult-to-Represent Optimization Problems.- Coordinating a Behavioral Supply Chain under Revenue Sharing Contract.