SpringerBriefs in Operations Research – serie

This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other.

This book helps readers understand the main issues, challenges, strategies, and solution methods in Aggregate Planning (AP), an important part of Supply Chain Management.

Use of quantitative data, especially in financial markets, may provide rapid results due to the ease-of-use and availability of fast computational software, but this book advises caution and helps to understand and avoid potential pitfalls.It deals with often underestimated issues related to the use of financial quantitative data, such as non-stationarity issues, accuracy issues and modeling issues. It provides practical remedies or ways to develop new calculation methodologies to avoid pitfalls in using data, as well as solutions for risk management issues in financial market. The book is intended to help professionals in financial industry to use quantitative data in a safer way.

Written by an experienced operations research practitioner with a strong applied mathematics background, this book offers practical insights into how to approach optimization problems, how to develop intelligent and efficient mathematical models and algorithms, and how to implement and deliver software products to customers.

This book provides a concise introduction into the fundamentals and applied techniques of multiple criteria decision making in the finance sector. Based on an analysis of the nature of financial decisions and the general methods of financial modelling, risk management and financial engineering, the book introduces into portfolio management, banking management and credit scoring. Finally the book presents an overview of further applications of multi criteria analysis in finance and gives an outlook on future perspectives for the application of MCDA in finance.

Now that supply chain operations are facing new challenges due to the necessity of reducing costs and CO2 emissions, companies are increasingly leveraging cooperation from companies from other supply chains as a source of competitive advantage. Horizontal Collaboration has been proved an efficient tool for cutting logistic costs up to 49%.Zaragoza Logistics Center, member of the MIT-Scale Network, is publishing this book with the aim of building upon a framework of inter-organizational learning for horizontal collaboration on logistic operations. The outcome of this study is designed to help understand the relationship dynamics and managerial insights of horizontal collaboration for sustainable and profitable cooperation in freight distribution.The proposed collaborative practices are based on senior manager experiences from different industries: home appliances, automotive, retail, and food and beverage. Additionally, collaborative evidence from various successful pilot cases relevant to the distribution operations of the four industries are described, in order to illustrate the framework presented. This book fills a gap in the literature market and its insights encourages the readers to reflect on their own operational circumstances and challenges. Additionally, the book can be used as supplementary reading for master degree courses on supply chain collaboration, as well as courses in executive management.

The Analytic Hierarchy Process (AHP) has been one of the foremost mathematical methods for decision making with multiple criteria and has been widely studied in the operations research literature as well as applied to solve countless real-world problems. This book is meant to introduce and strengthen the readers’ knowledge of the AHP, no matter how familiar they may be with the topic.This book provides a concise, yet self-contained, introduction to the AHP that uses a novel and more pedagogical approach. It begins with an introduction to the principles of the AHP, covering the critical points of the method, as well as some of its applications. Next, the book explores further aspects of the method, including the derivation of the priority vector, the estimation of inconsistency, and the use of AHP for group decisions. Each of these is introduced by relaxing initial assumptions. Furthermore, this booklet covers extensions of AHP, which are typically neglected in elementary expositions of the methods. Such extensions concern different numerical representations of preferences and the interval and fuzzy representations of preferences to account for uncertainty. During the whole exposition, an eye is kept on the most recent developments of the method.

This book was motivated by the notion that some of the underlying difficulty in challenging instances of graph-based problems (e.g., the Traveling Salesman Problem) may be “inherited” from simpler graphs which – in an appropriate sense – could be seen as “ancestors” of the given graph instance. The authors propose a partitioning of the set of unlabeled, connected cubic graphs into two disjoint subsets named genes and descendants, where the cardinality of the descendants dominates that of the genes. The key distinction between the two subsets is the presence of special edge cut sets, called cubic crackers, in the descendants.The book begins by proving that any given descendant may be constructed by starting from a finite set of genes and introducing the required cubic crackers through the use of six special operations, called breeding operations. It shows that each breeding operation is invertible, and these inverse operations are examined. It is therefore possible, for any given descendant, to identify a family of genes that could be used to generate the descendant. The authors refer to such a family of genes as a “complete family of ancestor genes” for that particular descendant. The book proves the fundamental, although quite unexpected, result that any given descendant has exactly one complete family of ancestor genes. This result indicates that the particular combination of breeding operations used strikes the right balance between ensuring that every descendant may be constructed while permitting only one generating set.The result that any descendant can be constructed from a unique set of ancestor genes indicates that most of the structure in the descendant has been, in some way, inherited from that, very special, complete family of ancestor genes, with the remaining structure induced by the breeding operations. After establishing this, the authors proceed to investigate a number of graph theoretic properties: Hamiltonicity, bipartiteness, andplanarity, and prove results linking properties of the descendant to those of the ancestor genes. They develop necessary (and in some cases, sufficient) conditions for a descendant to contain a property in terms of the properties of its ancestor genes. These results motivate the development of parallelizable heuristics that first decompose a graph into ancestor genes, and then consider the genes individually. In particular, they provide such a heuristic for the Hamiltonian cycle problem. Additionally, a framework for constructing graphs with desired properties is developed, which shows how many (known) graphs that constitute counterexamples of conjectures could be easily found.

This book offers a simple introduction to the fundamentals and applications of the Analytic Hierarchy Process (AHP) without a pre-requisite for a sophisticated mathematical background. It provides a quick and intuitive understanding of the methodology using spreadsheet examples and explains in a step-by-step fashion how to use Super Decisions, a freely available software developed by the Creative Decisions Foundations. The book is intended to be a resource for decision makers with little or no exposure to the field of Operations Research (OR); however, the book can be used as a very gentle introduction to the AHP methodology and/or as an AHP hands-on supplement for standard OR textbooks. AHP is an intuitive and mathematically simple methodology in the field of multi-criteria decision making. Because of this, most AHP books assume the reader has basic OR mathematical background. However, AHP simplicity suggests that decision makers from all disciplines can take advantage of the methodology without struggling with the mathematics behind it. To fulfill this need, this book delivers a quick and practical understanding of the method that can be useful for corporate executives.

This book offers a simple introduction to the theory and practice of the Analytic Hierarchy Process (AHP) without a pre-requisite for a sophisticated mathematical background. AHP is an intuitive and mathematically simple methodology in the field of multi-criteria decision making in Operational Research (OR). Using Super Decisions v3, the newly developed software by the Creative Decisions Foundations, this book provides a quick and intuitive understanding of AHP using spreadsheet examples and step-by-step software instructions. Super Decisions v3 marks a drastic departure from the previous version 2 in terms of interface and ratings model development. In addition to a concise guide, instructional videos are also available to demonstrate how to use the different features of Super Decisions v3. Most AHP books assume the reader has basic OR mathematical background; however, AHP was developed with the goal that decision makers can take advantage of this methodology without struggling with the mathematics behind it. For this reason, only basic arithmetic knowledge is required from the readers. In conclusion, this book delivers a quick and practical understanding of the AHP methodology that can be useful for corporate executives and decision-makers in all fields.