Short Software Descriptions

This paper briefly presents the software for interactive decision support that was developed in 1990-1991 within the Contracted Study Agreement between the System and Decision Sciences Program at IIASA and several Polish scientific institutions, namely: Institute of Automatic Control (Warsaw University of Technology); Institute of Computing Science (Technical University of Poznaii); Institute of Informatics (Warsaw University); and Systems Research Institute of the Polish Academy of Sciences. This Contracted Study Agreement has been a continuation of the same type of activity conducted since 1985. Therefore many of the software packages are actually improved versions of the programs developed in 1985-1989. The theoretical part of the results developed within this scientific activity is presented in the IIASA Collaborative Paper CP-90-008 by A. Ruszczynski, T. Rogowski and A.P. Wierzbicki entitled "Contributions to Methodology and Techniques of Decision Analysis (First Stage)." Detailed descriptions of the methodology and the user guide for each particular software package are published in separate Collaborative Papers. Each software package described here is available in executable form for non-profit educational and scientific purposes, however, any profit-oriented or commercial application requires a written agreement with IIASA. Inquires about the software should be directed to the System and Decision Sciences Program at IIASA, Methodology of Decisions Analysis Project

A Nonlinear Multicriteria Model for Strategic FMS Selection Decisions

The strategic decision of selecting an optimal flexible manufacturing system (FMS) configuration is a complicated question which involves evaluating tradeoffs between a number of different, potentially conflicting criteria such as annual production volume, flexibility, production and investment costs, and average throughput of the system. Recently, several structured approaches have been proposed to aid management in the FMS selection process. While acknowledging the nonlinear nature of a number of the relationships in the model, notably between batch size and the number of batches produced of each part, these studies used linear simplifications to illustrate the decision dynamics of the problem. These linear models were shown to offer useful analytical tools in the FMS pre-design process. Due to the nonlinearities of the true relationships, however, the tradeoffs between the criteria could not fully be explored within the linear framework. This paper builds on the two-phase decision support framework proposed by Stam and Kuula (1989), and uses a modified nonlinear multicriteria formulation to solve the problem. The software used in the illustration can easily be implemented, is user-interactive and menu-driven. The methodology is applied to real data from a Finnish metal product company, and the results are compared with those obtained in previous studies

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design

This paper presents a MultiObjective Genetic Algorithm (MOGA) optimization framework for batch plant design. For this purpose, two approaches are implemented and compared with respect to three criteria, i.e., investment cost, equipment number and a flexibility indicator based on work in process (the so-called WIP) computed by use of a discrete-event simulation model. The first approach involves a genetic algorithm in order to generate acceptable solutions, from which the best ones are chosen by using a Pareto Sort algorithm. The second approach combines the previous Genetic Algorithm with a multicriteria analysis methodology, i.e., the Electre method in order to find the best solutions. The performances of the two procedures are studied for a large-size problem and a comparison between the procedures is then made

Geometric Ideas in Nonlinear and Multicriteria Optimization

Some geometric properties of the solution set for nonlinear and multicriteria programming problems and the related numeric algorithms are considered. The author deals with necessary and sufficient conditions for nonlinear programming problem stability (in the nonconvex case), with Pareto set stability, Pareto set connectedness conditions, with weak efficiency, efficiency and proper efficiency criteria. A study of numerical algorithms based on geometric properties of the so-called convolutions function is also considered. Necessary and sufficient convergence conditions for large classes of algorithms are presented and easy to check sufficient conditions are given. Further results deal with problems of using local unconstrained minimization algorithms to solve quasi-convex problems and the problem of using some convolution functions for constructing decision making procedures. New classes of inverse nonlinear programming problems are discussed and software implementations of DISO/PC-MCNLP are presented

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Selecting a Flexible Manufacturing System Using Multiple Criteria Analysis

This paper describes a visually interactive decision support framework designed to aid the decision maker, typically top management, in selecting the most appropriate technology and design, when planning a flexible manufacturing system (FMS). The framework can be used in the pre-investment stage of the planning process, after the decision in principle has been made to build an FMS. First, both qualitative and quantitative criteria are used to narrow the set of alternative system configurations under consideration down to a small number of most attractive candidates. After this pre-screening phase, a multiobjective programming model is formulated for each remaining configuration, allowing the manager to explore and evaluate the costs and benefits of various different scenarios for each configuration separately by experimenting with different levels of batch sizes and production volumes. The system uses visual interaction with the decision maker, graphically displaying the relevant tradeoffs between such relevant performance criteria as investment and production costs, manufacturing flexibility, production volume and investment risk, for each scenario. Additional criteria, when relevant, can be included as well. The ease of use and interpretation and the flexibility make the proposed system a powerful analytical tool in the initial FMS design process. The insights gained from experimenting with the different scenarios form the basis of understanding the anticipated impact of techno-economic factors on the performance of the FMS configuration, and provide valuable information for the implementation stage of building the FMS. An example using real data from a case study in the Finnish metal product industry is provided to illustrate the methodology

Inverse Nonlinear Programming Problem and its Application

Inverse nonlinear programming problems for a new class of optimization problems relevant for game theory, system optimization, multicriteria optimization, etc. are considered by the author. This paper deals with problem definitions, numerical methods and applications of the inverse nonlinear programming problem in multicriteria optimization. Some associated properties of related parametric optimization problems and software implementations are also considered

A Duality Procedure to Elicit Nonlinear Multiattribute Utility Functions.

The practical implementation of the Multiattribute Utility Theory is limited, partly for the lack of operative methods to elicit the parameters of the Multiattribute Utility Function, particularly when this function is not linear. As a consequence, most studies are restricted to linear specifications, which are easier to estimate and to interpret. We propose an indirect method to elicit the parameters of a nonlinear utility function to be compatible with the actual behaviour of decision makers, rather than with their answers to direct surveys. The idea rests on approaching the parameter estimation problem as a dual of the decision problem and making the observed decisions to be compatible with a rational decision making process.Multiple-Criteria Analysis, Multi-Attribute Utility Function, Simulation, Agriculture.