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

Much discussion has taken place over the need for good modelling practice when formulating and solving goal programmes (GP’s) However, a considerable amount of poor or erroneous modelling still exists in the literature

An Example of Good Modelling Practice in Goal Programming: Means for Overcoming Incommensurability

An extended goal programming model for the multiobjective integrated lot-sizing and cutting stock problem

Multi-objective modelling of the UK fisheries of the English Channel

The aim of this paper is to investigate the application of Goal Programming (GP) to portfolio evaluation and selection. The shares analysed are those in the British FTSE 100 index. A two stage model is proposed. The first stage predicts the sensitivity of the shares to specific factors using GP and regression analysis. The second stage of the model selects a portfolio using a GP model based on the decision maker’s scenarios and preferences. A comparison between the sensitivities predicted by the first-stage of the GP model and that of the regression analysis is made. Keywords: Goal Programming, Portfolio Selection, Regression Analysis

Dylan Jones

Papers

An Example of Good Modelling Practice in Goal Programming: Means for Overcoming Incommensurability

An extended goal programming model for the multiobjective integrated lot-sizing and cutting stock problem

Multi-objective modelling of the UK fisheries of the English Channel

A Comparison Between Goal Programming and Regression Analysis for Portfolio Selection

Resource allocation in the North Sea fishery: a goal programming approach