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

Comments on properties of the minmax solutions in goal programming – a reply

By picking a combination of four, three or two of these four restrictions, the following 11 cases are created: ð f1ðxÞ; f2ðxÞ; f3ðxÞ and f4ðxÞÞ; ð f1ðxÞ; f2ðxÞ and f3ðxÞÞ; ð f1ðxÞ; f2ðxÞ and f4ðxÞÞ; ð f1ðxÞ; f3ðxÞ and f4ðxÞÞ; ð f2ðxÞ; f3ðxÞ and f4ðxÞÞ; ð f1ðxÞ and f2ðxÞÞ; ð f1ðxÞ and f3ðxÞÞ; ð f1ðxÞ and f4ðxÞÞ; ð f2ðxÞ and f3ðxÞÞ; ð f2ðxÞ and f4ðxÞÞ and ð f3ðxÞ and f4ðxÞÞ: These 11 cases are examined and the results are presented in Table 1. Since Wi1⁄4 1 and Ki 1⁄4 b i ð8 i 2 I Þ; the ratio ðb i fiðxÞÞ=b i is computed as a measure of weighted and normalized deviation for all objectives of each case. It can be observed that for all six cases when two objectives are considered both ratios are the same. Out of four cases when three objectives are considered two cases result in a mismatch of ratios (ie the solution is not balanced). Finally, the only case with four objectives also resulted in a mismatch.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601369.pdf

Reply to the comments of Ganjavi et al

The topic of how to formulate effective goal programmes using the major variants is covered in Chapter 3. This chapter expands on those concepts by introducing techniques developed to expand the flexibility of goal programming and enhance its use.

Advanced Topics in Goal Programming Formulation

PurposeDisturbances in terms of major crises such as pandemics, fluctuations in demand and oil price, energy consumption and supply chain can significantly impair the maintenance programs effectiveness and efficiency. Hence, there is an urgent need for an agile asset performance management (AAPM) framework.Design/methodology/approachThis paper's main objective is to design a comprehensive framework for an AAPM system that sustains the desired asset performance by reacting efficiently, quickly and intelligently to the changes in the operating context parameters and asset health conditions. Such a framework is adaptive to changes in scenarios and aims to systematise the decision support process, considering different objectives.FindingsThe development of the proposed framework has led to identifying an innovative way of seamless integration between crucial reliability and asset management tools. Also, the methodology implementation is expected to promote the practical use of its reliability tools and enable asset stakeholders to break silo working for clear communication around asset performance.Originality/valueThe implementation of the AAPM framework follows a new approach developed during this research and coined by the authors as the “8S approach.”

/pdf/a-conceptual-framework-for-an-agile-asset-performance-1qrwswwnr5.pdf

A conceptual framework for an agile asset performance management process

material to justify the validity of Conclusion 1 and the related models, in the case of typical OR=MS Multiple Objective Programming (MOP) models. Let me recall that the first example submitted in the Viewpoint contradicts Conclusion 1 even for two attributes in the case of discrete or, more generally, nonconvex problems (integrality requirements in this model can be replaced with continuous variables and nonlinear equations: xi ÿ xi 1⁄4 0). The second counterexample submitted in the Viewpoint, although simplified, is a proper Multiple Objective Linear Programming (MOLP) model in the three criteria space and it is even characterised by the differentiable efficient frontier (which is not common in the case of MOLP models). This example contradicts Conclusion 1 in the case of MOLP models with more than two criteria (attributes) as well as it contradicts the validity of model (7) proposed for general case, where reference levels can take any values not necessarily being fixed at their ideal values (as shown in the Viewpoint it reduces to model (6) if the reference levels are fixed at the ideal values or larger). One may also notice that even the unique minmax solution, and therefore the efficient one, may be not perfectly equilibrated, like in the following MOLP:

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2601195.pdf

Dylan Jones

Papers

Comments on properties of the minmax solutions in goal programming – a reply

Reply to the comments of Ganjavi et al

Advanced Topics in Goal Programming Formulation

A conceptual framework for an agile asset performance management process

Final reply to comments of Professor Ogryczak