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"Zionts, Stanley"
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Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead
This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields.
Journal Article
Multiple criteria decision making
Multiple Criteria Decision Making (MCDM) is all about making choices in the presence of multiple conflicting criteria. MCDM has become one of the most important and fastest growing subfields of Operations Research/Management Science. As modern MCDM started to emerge about 50 years ago, it is now a good time to take stock of developments. This book aims to present an informal, nontechnical history of MCDM, supplemented with many pictures. It covers the major developments in MCDM, from early history until now. It also covers fascinating discoveries by Nobel Laureates and other prominent scholars.
eBook
Functionality defense through diversity: a design framework to multitier systems
Diversification is one of the most effective approaches to defend multitier systems against attacks, failure, and accidents. However, designing such a system with effective diversification is a challenging task because of stochastic user and attacker behaviors, combinatorial-explosive solution space, and multiple conflicting design objectives. In this study, we present a systematic framework for exploring the solution space, and consequently help the designer select a satisfactory system solution. A simulation model is employed to evaluate design solutions, and an artificial neural network is trained to approximate the behavior of the system based on simulation output. Guided by a trained neural network, a multi-objective evolutionary algorithm (MOEA) is proposed to search the solution space and identify potentially good solutions. Our MOEA incorporates the concept of Herbert Simon’s satisficing. It uses the decision maker’s aspiration levels for system performance metrics as its search direction to identity potentially good solutions. Such solutions are then evaluated via simulation. The newly-obtained simulation results are used to refine the neural network. The exploration process stops when the result converges or a satisfactory solution is found. We demonstrate and validate our framework using a design case of a three-tier web system.
Journal Article
A Lagrangean Relaxation Approach for Very-Large-Scale Capacitated Lot-Sizing
In this paper, we develop a Lagrangean relaxation-based heuristic procedure to generate near-optimal solutions to very-large-scale capacitated lot-sizing problems (CLSP) with setup times and limited overtime. Our computational results show that large problems involving several thousand products and several thousand 0/1 integer variables can be solved in a reasonable amount of computer time to within one percent of their optimal solution. The proposed procedure is general enough to be applied directly or with slight modification to real-life production problems.
Journal Article
A Branch-and-Bound Method for the Fixed Charge Transportation Problem
In this paper we develop a new conditional penalty for the fixed charge transportation problem. This penalty is stronger than both the Driebeek penalties and the Lagrangean penalties of Cabot and Erenguc. Computational testing shows that the use of these penalties leads to significant reductions in enumeration and solution times for difficult problems in the size range tested. We also study the effect of problem parameters on the difficulty of the problem. The ratio of fixed charges to variable costs, the shape of the problem, arc density in the underlying network and fixed charge arc density are shown to have a significant effect on problem difficulty for problems involving up to 40 origins and 40 destinations.
Journal Article
An Interactive Programming Method for Solving the Multiple Criteria Problem
In this paper a man-machine interactive mathematical programming method is presented for solving the multiple criteria problem involving a single decision maker. It is assumed that all decision-relevant criteria or objective functions are concave functions to be maximized, and that the constraint set is convex. The overall utility function is assumed to be unknown explicitly to the decision maker, but is assumed to be implicitly a linear function, and more generally a concave function of the objective functions. To solve a problem involving multiple objectives the decision maker is requested to provide answers to yes and no questions regarding certain trade offs that he likes or dislikes. Convergence of the method is proved; a numerical example is presented. Tests of the method as well as an extension of the method for solving integer linear programming problems are also described.
Journal Article
Generating Pareto Solutions in a Two-Party Setting: Constraint Proposal Methods
This paper presents a class of methods, called constraint proposal methods , for generating Pareto-optimal solutions in two-party negotiations. In these methods joint tangents of the decision makers' value functions are searched by adjusting an artificial plane constraint. The problem of generating Pareto-optimal solutions decomposes into ordinary multiple criteria decision-making problems for the individual decision makers and into a coordination problem for an assisting mediator. Depending on the numerical iteration scheme used to solve the coordination problem, different constraint proposal methods are obtained. We analyze and illustrate the behaviour of some iteration schemes by numerical examples using both precise and imprecise answers from decision makers. An example of a method belonging to the class under study is the RAMONA method, that has been previously described from a practical point of view. We present the underlying theory for it by describing it as a constraint proposal method, and include some applications.
Journal Article