As the field of Supply Chain Management has matured, maintaining the precise flow of goods to maintain schedules (hence, minimizing inventories) on a just-in-time basis still remains as a major challenge. This problem or challenge has resulted in a fair amount of quantitative research in the area, producing an array of models and algorithms to help ensure the precise flow of components and final products into inventories to meet just-in-time requirements.

Just-in-Time Scheduling: Models and Algorithms for Real Time Operating Systems is the first expository treatment surveying the theoretical work on computer systems models and algorithms utilized in just-in-time scheduling. With the impact of globalization and supply chains on manufacturing, there are immense amounts of material flowing through supply chains at any given time worldwide. Therefore the scheduling of all the stages of material arriving at different geographical points at precise times is a highly significant problem. Moreover, the theoretical work in this area has larger ramifications for operational scheduling in many application areas. The scheduling models and algorithms presented and illustrated in the book will be done so in the context of extensive use of computer systems in a "real time context." The "just-in-time" and "real-time" theoretical work in the book will be of value and interest to many Engineers, Computer Scientists and Operations Research/Management Science researchers, students and practitioners. The book will survey and synthesize all the research in this topical area.

The positive reciprocal pairwise comparison matrix (PCM) is one of the key components which is used to quantify the qualitative and/or intangible attributes into measurable quantities. This book examines six understudied issues of PCM, i.e. consistency test, inconsistent data identification and adjustment, data collection, missing or uncertain data estimation, and sensitivity analysis of rank reversal. The maximum eigenvalue threshold method is proposed as the new consistency index for the AHP/ANP. An induced bias matrix model (IBMM) is proposed to identify and adjust the inconsistent data, and estimate the missing or uncertain data. Two applications of IBMM including risk assessment and decision analysis, task scheduling and resource allocation in cloud computing environment, are introduced to illustrate the proposed IBMM.

This book provides students and researchers with a resource that includes the current application of the multi-criteria decision theory in a variety of fields, including the environment, health care, engineering, and architecture. There are many critical parameters (criteria) that can directly or indirectly affect the consequences of various decisions. The application of the multi-criteria decision theory focusses mainly on the use of computational methods which include multiple criteria and orders of preference for the evaluation and the selection of the best option among many alternatives based on the desired outcome. The theory of multi-criteria decision making (MCDM) is an approach that can be extremely useful for students, managers, engineers of manufacturing companies, etc.

This edited collection addresses the question of which capabilities and competencies enable Behavioral Operational Research to provide sustained improvement to decision processes. The aim is to show how a focus on capability and competency will not only meet short-term requirements for problem solving and decision support, but also build a solid foundation for the future. The contributors present recent advances in Behavioral OR, with a focus on the ways in which users of models deal with incomplete and imprecise information, subjective boundaries and uncertainty. These chapters are structured around three key dimensions of BOR: capabilities, cognition and aspects of practice.

In the 21st century, advancements in the digital world are bringing about rapid waves of change in organizational management. As such, it is increasingly imperative to discover ways for businesses to adapt to changes in the markets and seize various digital marketing opportunities. Improving Business Performance Through Innovation in the Digital Economy is an essential reference source for the latest research on the impact of digital computing. It investigates new economic and entrepreneurial approaches to enhancing community development. Featuring research on topics such as business ethics, mobile technology, and cyber security, this book is ideally designed for knowledge workers, business managers, executives, entrepreneurs, small and medium enterprise managers, academicians, researchers, students, and global leaders seeking coverage on the management of sustainable enterprises.

This book aims to stimulate debate in the growing and highly controversial area of measuring scholarly work. The authors examine key aspects of this topic through the lens of the latest theoretical developments in service science and associated fields. It includes chapters explaining the theoretical developments and methodological aspects of measuring the quality of academic teaching and research, while other chapters provide a review and analysis of various types of scholarly work metrics and processes with examples from several countries, cultures, and educational systems. The current growing concern about higher education (HE) quality has prompted institutions to divide university teachers' work into different areas and to design methods aimed at measuring the productivity of these areas. It is widely accepted that the need to evaluate HE service quality is a relevant issue for any society. However, the authors argue that most of the current practices used in the pursuit of this objective are jeopardizing the future of the university as a place of knowledge generation, science evolution and professional education.

This book introduces the advances in synchromodal logistics and provides a framework to classify various optimisation problems in this field. It explores the application of this framework to solve a broad range of problems, such as problems with and without a central decision-maker, problems with and without full information, deterministic problems, problems coping with uncertainty, optimisation of a full network design problem. It covers theoretical constructs, such as discrete optimisation, robust optimisation, optimisation under uncertainty, multi-objective optimisation and agent based equilibrium models. Moreover, practical elaborated use cases are presented to deepen understanding. The book gives both researchers and practitioners a good overview of the field of synchromodal optimisation problems and offers the reader practical methods for modelling and problem-solving.

Using numerical examples to illustrate their concepts and results, this book examines recently developed fuzzy multi-criteria methods, such as Intuitionistic Fuzzy TOPSIS, Intuitionistic Fuzzy TOPSIS & DEA-AHP, Intuitionistic VIKOR, Pythagorean WASPAS, Pythagorean ENTROPI, Hesitant CBD, Hesitant MABAC, Triangular EDAS, Triangular PROMETHEE, q-Rung Orthopair COPRAS, and Fuzzy Type - 2 ELECTRE. Each chapter covers practical applications in addition to fresh developments and results. Given its structure and scope, the book can be used as a textbook in graduate courses on operations research and industrial engineering. It also offers a valuable resource for scientists working in a range of disciplines that require multi-criteria decision making.

This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) characterized by uncertain ("hesitant") information. Because of its clarity and self contained explanations, the book can also be adopted as a textbook from graduate and advanced undergraduate students.

The focus of this book is on establishing theories and methods of both decision and game analysis in management using intuitionistic fuzzy sets. It proposes a series of innovative theories, models and methods such as the representation theorem and extension principle of intuitionistic fuzzy sets, ranking methods of intuitionistic fuzzy numbers, non-linear and linear programming methods for intuitionistic fuzzy multi-attribute decision making and (interval-valued) intuitionistic fuzzy matrix games. These theories and methods form the theory system of intuitionistic fuzzy decision making and games, which is not only remarkably different from those of the traditional, Bayes and/or fuzzy decision theory but can also provide an effective and efficient tool for solving complex management problems. Since there is a certain degree of inherent hesitancy in real-life management, which cannot always be described by the traditional mathematical methods and/or fuzzy set theory, this book offers an effective approach to using the intuitionistic fuzzy set expressed with membership and non-membership functions. This book is addressed to all those involved in theoretical research and practical applications from a variety of fields/disciplines: decision science, game theory, management science, fuzzy sets, operational research, applied mathematics, systems engineering, industrial engineering, economics, etc.

This fifth edition of "Product Lifecycle Management" updates and adds to the successful fourth edition, the most frequently cited PLM publication. It gives the reader a thorough explanation of Product Lifecycle Management (PLM) and provides them with a full understanding and the skills to implement PLM within their own business environment. This new and expanded edition is fully updated to reflect the many technological and management advances made in PLM since the release of the fourth edition. "Product Lifecycle Management" will broaden the reader's understanding of PLM, nurturing the skills needed to implement PLM successfully and to achieve world-class product performance across the lifecycle. Among the components of PLM described are product-related business processes, product data, product data management (PDM) systems, other PLM applications, best practices, company objectives and organisation. This book also describes the relationships of PLM with the Internet of Things, Industry 4.0, Digital Twins and Digital Threads. "Product Lifecycle Management" (5th edition) explains what PLM is, and why it is needed. It describes the environment in which products are ideated, developed, manufactured, supported and retired, before addressing the main components of PLM and PLM Initiatives. Key activities in PLM Initiatives described include organisational change management (OCM) and project management. The final part of the book addresses the PLM Initiative, showing the typical steps and activities of a PLM project or initiative.

This book is a further development of the theory of parametric control. It includes: numerical methods of testing (verification) of software implementation of mathematical models by assessing the stability of mappings defined by the model; sufficient conditions for the existence of the solutions of some types of problems of dynamic optimization; the existence of continuous dependence of optimal values of criteria on exogenous functions and parameters; and the existence of points of bifurcation of extremals of such problems. It demonstrates that this theory offers a constructive methodology for middle-term forecasting, macroeconomic analysis and estimation of optimal values of economic characteristics on the basis of advanced global mathematical models, namely Computable General Equilibrium (CGE) Model, Dynamic Stochastic General Equilibrium (DSGE) Model, and Hybrid Econometric model. In addition, it includes conditions for the applicability of the computational experiments' results, into practice.

This book defines and studies a combinatorial object called the pedigree and develops the theory for optimising a linear function over the convex hull of pedigrees (the Pedigree polytope). A strongly polynomial algorithm implementing the framework given in the book for checking membership in the pedigree polytope is a major contribution. This book challenges the popularly held belief in computer science that a problem included in the NP-complete class may not have a polynomial algorithm to solve. By showing STSP has a polynomial algorithm, this book settles the P vs NP question. This book has illustrative examples, figures, and easily accessible proofs for showing this unexpected result. This book introduces novel constructions and ideas previously not used in the literature. Another interesting feature of this book is it uses basic max-flow and linear multicommodity flow algorithms and concepts in these proofs establishing efficient membership checking for the pedigree polytope. Chapters 3-7 can be adopted to give a course on Efficient Combinatorial Optimization. This book is the culmination of the author's research that started in 1982 through a presentation on a new formulation of STSP at the XIth International Symposium on Mathematical Programming at Bonn.

This book represents the compilation of several research approaches on opera tional freight carrier planning carried out at the Chair of Logistics, University of Bremen. It took nearly three years from the first ideas to the final version, now in your hands. During this time, several persons helped me all the time to keep on going and to re-start when I got stuck in a dead end or when I could not see the wood for the trees. I am deeply indebted to them for their encouragement and comments. Prof. Dr. Herbert Kopfer, holder of the Chair of Logistics, introduced me into the field of operational transport planning. He motivated and supervised me. Furthermore, he supported me constantly and allowed me to be as free as possible in my research and encouraged me to be as creative as necessary. In addition, I have to thank Prof. Dr. Hans-Dietrich Haasis, Prof. Dr. Martin G. Mohrle and Prof. Dr. Thorsten Poddig. On behalf of all my colleagues, who supported me in numerous ways, I have to say thank you to Prof. Dr. Dirk C. Mattfeld, Prof. Dr. Christian Bierwirth, Henner Gratz, Prof. Dr. Elmar Erkens, Nadja Shigo and Katrin Dorow. They all helped me even with my most obscure and dubious problems. My family supported me all the time. They always showed me their trust and encouraged me continuously. Special thanks are dedicated to my parents Monika and Heinz-Jiirgen."

This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management. Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization. While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow's scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation.

This book employs game theory to warfare and in particular to military operations. It aims at scrutinizing the validity of the two ideas that have governed the literature on war and warfighting: One is the Clausewitzian Fog of War, which suggests that he who is able to "see" through the gunsmoke and observe his opponent's moves before he has to commit to some strategy himself, should be able to gain an advantage over that enemy; the other is the tradition of understanding military conflict as a zero-sum game. Combined, these ideas seem to imply that war always gives rise to a second-mover advantage. This book questions the validity of this presumption at the operational level of military planning. It provides a simple but rigorous game-theoretic framework in order to analyse operational alternatives for a whole range of typical conflicts Western military forces are facing, including the most recent ones such as Anti-Access/Area-Denial and supporting host nations' counterinsurgency campaigns.

Numerical Methods in Environmental Data Analysis introduces environmental scientists to the numerical methods available to help answer research questions through data analysis. One challenge in data analysis is misrepresentation of datasets that are relevant directly or indirectly to the research. This book illustrates new ways of screening dataset or images for maximum utilization, introducing environmental modeling, numerical methods, and computations techniques in data analysis. Throughout the book, the author includes case studies that provide guidance on how to translate research questions into appropriate models. Individuals working with data sets or images generated from environmental monitoring centers or satellites will find this book to be a concise guide for analyzing and interpreting their data.

This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to have some basic knowledge of probability theory, Markov processes and matrix theory.

Newtonian Nonlinear Dynamics for Complex Linear and Optimization Problems explores how Newton's equation for the motion of one particle in classical mechanics combined with finite difference methods allows creation of a mechanical scenario to solve basic problems in linear algebra and programming. The authors present a novel, unified numerical and mechanical approach and an important analysis method of optimization.

This book presents a consistent methodology for making decisions under uncertain conditions, as is almost always the case. Tools such as value of information and value of flexibility are explored as a means to make more complex and nuanced decisions. The book develops the complete formalism for assessing the value of acquiring information with two novel approaches. Firstly, it integrates the fuzzy characteristics of data, and secondly develops a methodology for assessing data acquisition actions that optimize the value of projects from a holistic perspective. The book also discusses the formalism for including flexibility in the project decision assessment. Practical examples of oil- and gas-related decision problems are included and discussed to facilitate the learning process. This book provides valuable advice and case studies applicable to engineers, researchers, and graduate students, particularly in the oil and gas industry and pharmaceutic industry.

Waiting in lines is a staple of everyday human life. Without really noticing, we are doing it when we go to buy a ticket at a movie theater, stop at a bank to make an account withdrawal, or proceed to checkout a purchase from one of our favorite department stores. Oftentimes, waiting lines are due to overcrowded, overfilling, or congestion; any time there is more customer demand for a service than can be provided, a waiting line forms. Queuing systems is a term used to describe the methods and techniques most ideal for measuring the probability and statistics of a wide variety of waiting line models. This book provides an introduction to basic queuing systems, such as M/M/1 and its variants, as well as newer concepts like systems with priorities, networks of queues, and general service policies. Numerical examples are presented to guide readers into thinking about practical real-world applications, and students and researchers will be able to apply the methods learned to designing queuing systems that extend beyond the classroom. Very little has been published in the area of queuing systems, and this volume will appeal to graduate-level students, researchers, and practitioners in the areas of management science, applied mathematics, engineering, computer science, and statistics.

The book takes the inventory control perspective to tackle empty container repositioning logistics problems in regional transportation systems by explicitly considering the features such as demand imbalance over space, dynamic operations over time, uncertainty in demand and transport, and container leasing phenomenon. The book has the following unique features. First, it provides a discussion of broad empty equipment logistics including empty freight vehicle redistribution, empty passenger vehicle redistribution, empty bike repositioning, empty container chassis repositioning, and empty container repositioning (ECR) problems. The similarity and unique characteristics of ECR compared to other empty equipment repositioning problems are explained. Second, we adopt the stochastic dynamic programming approach to tackle the ECR problems, which offers an algorithmic strategy to characterize the optimal policy and captures the sequential decision-making phenomenon in anticipation of uncertainties over time and space. Third, we are able to establish closed-form solutions and structural properties of the optimal ECR policies in relatively simple transportation systems. Such properties can then be utilized to construct threshold-type ECR policies for more complicated transportation systems. In fact, the threshold-type ECR policies resemble the well-known (s, S) and (s, Q) policies in inventory control theory. These policies have the advantages of being decentralized, easy to understand, easy to operate, quick response to random events, and minimal on-line computation and communication. Fourth, several sophisticated optimization techniques such as approximate dynamic programming, simulation-based meta-heuristics, stochastic approximation, perturbation analysis, and ordinal optimization methods are introduced to solve the complex stochastic optimization problems. The book will be of interest to researchers and professionals in logistics, transport, supply chain, and operations research.

This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

This proceedings volume convenes selected, peer-reviewed contributions presented at the POMS 2021 - International Conference on Production and Operations Management, which was virtually held in Lima, Peru, December 2-4, 2021. This book presents results in the field of Operations Management of key relevance to practitioners, instructors, and students. Topics focus on Operations Management, Logistics and Supply Chain Management, and Industrial and Production Engineering and Management, where mathematics and its applications play a role. In this work, readers will find a colorful collection of real-world case studies, accompanied by operations research-based managerial models. They touch on myriad topics, ranging from Artificial Intelligence and Data Analytics in Operations, Defense, Tourism, and other emerging issues in Operations Management to Healthcare Operations Management and Humanitarian Operations and Crisis Management. The POMS Lima 2021 International Conference has been organized by the Latin America & Caribbean Chapter of the Production and Operations Management Society, the most renowned professional and academic organization representing the interests of production and operations management professionals and academicians around the world. Since 2018, POMS International Conferences have been organized by POMS-LA, the first venue being in Rio de Janeiro, Brazil. Venue 2021 event was hosted by the Pontifical Catholic University of Peru and Pacific University, two Peruvian Latin-American leading academic institutions from Peru.

Businesses in the Asia-Pacific communities provide enormous opportunities for local entrepreneurs to develop and collectively collaborate with other economies. However, several challenges and success factors exist for effective business operations in the region. Innovative Management and Business Practices in Asia is a collection of innovative research that enhances understanding and collaboration in business, management, and technology in Asia for the present and in the future. While highlighting topics including corporate culture, international trade, and business administration, this book is ideally designed for managers, executives, CEOs, board members, corporate professionals, managing directors, deans, decision makers, professors, researchers, policymakers, industry practitioners, and students.