A survey of current continuous nonlinear multi-objective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.

Survey of multi-objective optimization methods for engineering

Multi-objective optimization using genetic algorithms: A tutorial

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

An extension of TOPSIS for group decision making

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Linear And Nonlinear Programming

The standard goal programming model only allows linear continuous preference functions to be modelled. In this paper methods for formulating other possible preference structures, such as discontinuities and varying penalty levels dependent on the distance from the goals are proposed. Techniques for ensuring minimal model growth and ease of solution are discussed. An example to illustrate these methods is given.

Expanding the flexibility of goal programming via preference modelling techniques

This paper examines the concepts of detection and restoration of pareto efficiency to pareto inefficient goal programmes. An overview of the subject is given and pareto efficiency is defined in terms of objectives, priority levels, and the overall model. The concept of pareto unboundedness in introduced. Simplex-based methods for efficient detection of inefficiency and unboundedness are given. Two new methods for restoration, based on preference information given by the decision maker are detailed. The performance of these techniques is analysed on a set of goal programmes.

Goal programming and Pareto efficiency

The aim of this work is to be a useful instrument for helping finance practitioners on the selection of suitable mutual fund portfolios. The portfolio selection problem is characterized by imprecision and/or vagueness inherent in the required data and more generally, in the context where investors have to make decisions. In order to mitigate these problems, a three stage model has been proposed based on a multi-index model and considering several market scenarios described in an imprecise way by an expert. The proposed fuzzy model allows the Decision Maker to select, by means of an outranking method, a suitable portfolio taking into account the uncertainty related to the market scenarios and the imprecision and/or vagueness associated with the model data.

Dylan Jones

Papers

Expanding the flexibility of goal programming via preference modelling techniques

Goal programming and Pareto efficiency

An interactive three-stage model for mutual funds portfolio selection ☆

Detecting IIS in infeasible linear programmes using techniques from goal programming

A practical weight sensitivity algorithm for goal and multiple objective programming