Stefan Rocktäschel introduces a branch-and-bound algorithm that determines a cover of the efficient set of multiobjective mixed-integer convex optimization problems. He examines particular steps of this algorithm in detail and enhances the basic algorithm with additional modifications that ensure a more precise cover of the efficient set. Finally, he gives numerical results on some test instances.

Multiobjective mixed integer convex optimization refers to mathematical programming problems where more than one convex objective function needs to be optimized simultaneously and some of the variables are constrained to take integer values. We present a branch-and-bound method based on the use of properly defined lower bounds. We do not simply rely on convex relaxations, but we built linear outer approximations of the image set in an adaptive way. We are able to guarantee correctness in terms of detecting both the efficient and the nondominated set of multiobjective mixed integer convex problems according to a prescribed precision. As far as we know, the procedure we present is the first deterministic algorithm devised to handle this class of problems. Our numerical experiments show results on biobjective and triobjective mixed integer convex instances.

This book provides an insightful and comprehensive treatment of convexification and global optimization of continuous and mixed-integer nonlinear programs. Developed for students, researchers, and practitioners, the book covers theory, algorithms, software, and applications. This thought-provoking book: -develops a powerful and widely-applicable framework for constructing closed-form expressions of convex envelopes of nonlinear functions; -presents a systematic treatment of branch-and-bound, while providing acceleration mechanisms and enhancements; -unifies ideas at the interface between operations research and computer science, devising efficient algorithmic implementation for global optimization; offers students, modelers, and algorithm developers a rich collection of models, applications, and numerical examples; -elucidates through geometric interpretations the concepts discussed throughout the book; -shows how optimization theory can lead to breakthroughs in diverse application areas, including molecular design, process and product design, facility location, and supply chain design and operation; -demonstrates that the BARON software developed by the authors can solve global optimization problems heretofore considered intractable, in an entirely automated manner on a personal computer. Audience: This book will be of interest to researchers in operations research, management science, applied mathematics, computer science, computational chemistry, and all branches of engineering. In addition, the book can be used in graduate level courses in nonlinear optimization, integer programming, global optimization, convex analysis, applied mathematics, and engineering design.

Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.

Disjunctive Programming is a technique and a discipline initiated by the author in the early 1970's, which has become a central tool for solving nonconvex optimization problems like pure or mixed integer programs, through convexification (cutting plane) procedures combined with enumeration. It has played a major role in the revolution in the state of the art of Integer Programming that took place roughly during the period 1990-2010. The main benefit that the reader may acquire from reading this book is a deeper understanding of the theoretical underpinnings and of the applications potential of disjunctive programming, which range from more efficient problem formulation to enhanced modeling capability and improved solution methods for integer and combinatorial optimization. Egon Balas is University Professor and Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business.

This book discusses the P-graph framework for developing and understanding effective design tools for process systems engineering, and addresses the current state of its theory and applications. The book details the new philosophy of the axioms-based mathematical modelling of processing systems, the basic algorithms, areas of application, future directions, and the proofs of theorems and algorithms. Because of the rigorous foundation of the theory, the framework provides a firm basis for future research in mathematical modelling, optimization, and design of complex engineering systems. The various P-graph applications discussed include process network synthesis, reliability engineering, and systems resilience. The framework opens new avenues for research in complex systems including redundant operations for critical infrastructure, systems sustainability, and modelling tools for disaster engineering. Demonstration software is provided to facilitate the understanding of the theory. The book will be of interest to institutions, companies, and individuals performing research and R&D in process systems engineering.