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

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

Motivations and experiences of UK students studying abroad

This report summarises the findings of research aimed at improving understanding of the motivations behind the international diploma mobility of UK student

Artificial Neural Network Representations for Hierarchical Preference Structures

In this paper, we introduce two artificial neural network formulations that can be used to predict the preference ratings from the pairwise comparison matrices of the Analytic Hierarchy Process (AHP). First, we introduce a modified Hopfield network that can be used to exactly determine the vector of preference ratings associated with a positive reciprocal comparison matrix. The dynamics of this network are mathematically equivalent to the power method, a widely used numerical method for computing the principal eigenvectors of square matrices. However, we show that the Hopfield network representation is incapable of generalizing the preference patterns, and consequently is not suitable for approximating the preference ratings if the preference information is imprecise. Then we present a feed-forward neural network formulation that does have the ability to accurately approximate the preference ratings. A simulation experiment is used to verify the robustness of the feed-forward neural network formulation with respect to imprecise pairwise judgments. From the results of this experiment, we conclude that the feed-forward neural network formulation appears to be a powerful tool for analyzing discrete alternative multicriteria decision problems with imprecise or fuzzy ratio-scale preference judgments

Transboundary Air Pollution in Europe: An Interactive Multicriteria Tradeoff Analysis

In this paper. the acid rain problem in Europe is discussed, stressing the transboundary tradeoffs between abatement costs of sulphur emission reduction and corresponding deposition levels in the different countries. An interactive decision support methodology is proposed which utilizes a powerful nonlinear multicriteria software package to evaluate various scenarios and tradeoffs. The concepts are illustrated using previously published data. The results from the tradeoff analysis show that reasonable deposition levels can be reached with limited transfers of funds between countries. The extent of these transfers can be controlled by selecting appropriate target levels for the criteria across countries

Solving Multiple Objective Programming Problems Using Feed-Forward Artificial Neural Networks: The Interactive FFANN Procedure

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated set either by assigning preference "values" to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Choo 1983). From the computational results, the Interactive FFANN Procedure produces good results and is robust with regard to the neural network architecture

Interactive Multiple Objective Programming Using Tchebycheff Programs and Artificial Neural Networks

A new interactive multiple objective programming procedure is developed that combines the strengths of the Interactive Weighted Tchebycheff Procedure (Steuer and Choo 1983) and the Interactive FFANN Procedure (Sun, Stam and Steuer 1993). In this new procedure, nondominated trial solutions are generated by solving Augmented Weighted Tchebycheff Programs (Steuer 1986), based on which the decision maker articulates his/her preference information by assigning "values" to these solutions or by making pairwise comparisons. The elicited preference information is used to train a feed-forward artificial neural network, which in turn is used to screen new trial solutions for presentation to decision maker in the next iteration. Computational results are reported, comparing the current procedure with the Interactive Weighted Tchebycheff Procedure and the Interactive FFANN Procedure. The results show that this new procedure yields good quality solutions

"Playstation eyetoy games" improve upper extremity-related motor functioning in subacute stroke:a randomized controlled clinical trial

Aim. To evaluate the effects of "Playstation EyeToy Games" on upper extremity motor recovery and upper extremity-related motor functioning of patients with subacute stroke. Methods. The authors designed a randomized, controlled, assessor-blinded, 4-week trial, with follow-up at 3 months. A total of 20 hemiparetic inpatients (mean age 61.1 years), all within 12 months post-stroke, received 30 minutes of treatment with "Playstation EyeToy Games" per day, consisting of flexion and extension of the paretic shoulder, elbow and wrist as well as abduction of the paretic shoulder or placebo therapy (watching the games for the same duration without physical involvement into the games) in addition to conventional program, 5 days a week, 2-5 hours/day for 4 weeks. Brunnstrom's staging and self-care subitems of the functional independence measure (FIM) were performed at 0 month (baseline), 4 weeks (post-treatment), and 3 months (follow-up) after the treatment. Results. The mean change score (95% confidence interval) of the FIM self-care score (5.5 [2.9-8.0] vs 1.8 [0.1-3.7], P=0.018) showed significantly more improvement in the EyeToy group compared to the control group. No significant differences were found between the groups for the Brunnstrom stages for hand and upper extremity. Conclusion. "Playstation EyeToy Games" combined with a conventional stroke rehabilitation program have a potential to enhance upper extremity-related motor functioning in subacute stroke patients.</div