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

Towards a Circular Urban Metabolism with Sewer Wastewater Heat Recovery Systems (SWWHRS): Introducing a SWWHRS Planning Decision Support System

In this paper I describe how cities can reduce their dependence on fossil fuels for space and water heating by utilizing sewer wastewater heat as a low carbon energy source. I introduce the first stage of a planning decision support system for implementing sewer wastewater heat recovery systems. The model decision support system is intended for community energy planners and other relevant stakeholders to identify locations for matching sewer wastewater heat with appropriate thermal energy demand. This project demonstrates how ideal locations of sewer wastewater heat supply from municipal sewers can be matched with space/water heating demand using spatial analysis techniques and geographic information systems. This first proposed stage of a decision support system utilizes GIS to perform a site suitability analysis that can be used as the basis for further feasibility assessments in the planning of a sewer wastewater heat recovery system. Guelph, Ontario, Canada is used as a case study area. I go on to demonstrate the potential for reducing fossil fuel use in Guelph by identifying the volume of heat that can be recovered from each sewer segment and selecting several ideal locations that warrant further investigation into the feasibility of implementing a sewer wastewater heat recovery system. This proposed planning tool has potential for identifying significant carbon emission reduction opportunities in Ontario due to the large volume of natural gas consumed for space and water heating in the province`s urban residential and commercial zones and the prevalence of extensive sewer networks in all major urban areas. The decision support tool presented in this paper should however be utilized by a community energy planner in conjunction with other approaches for assessing how to reduce natural gas use for heating, as wastewater heat recovery is but one possible solution. Discussion of other approaches is beyond the scope of this research paper

Improving security requirements adequacy: an interval type 2 fuzzy logic security assessment system

Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts’ ratings

Decision support for middleware performance benchmarking

Along with the rapid development of computing systems, the heterogeneity amongst them also increases. With the usage of middleware technologies, the systems work together in a homogeneous environment and allow to integrate previously independent applications, together with new developments. Along with the growing popularity of middleware systems, the performance and efficiency of their underlying technology becomes crucial for the business. For that reason, applying benchmarks on such environments is highly required. Current technologies and literature focus on building diverse benchmarks in order to test the performance of the underlying middleware. The development of a standard benchmark for middleware is extremely challenging, as one needs to realistically stress the different software capabilities. However, information on how to do so, is generally missing, thus the users need to arbitrarily make crucial design decisions. This Master’s thesis aims at providing the means to ease the decision-making for selecting the appropriate middleware benchmark and enables the user to make crucial design decisions when the creation of a new middleware benchmark is intended. We propose the creation of the first Decision Support System for middleware performance benchmarking, capable of guiding the user through relevant components of a benchmark. The prototype is based on current web technologies using the REST architectural style and it provides a decision support approach for decision makers considering the creation of a new middleware benchmark or selecting the right middleware benchmark of choice. At the end, we validate through use cases to show that the functionalities of the system are accomplished successfully

Selecting a Flexible Manufacturing System using Multiple Criteria Analysis

A visual 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) is described. The framework can be used in the preinvestment 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 prescreening 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 trade-offs 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 also be included. 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

Implementation approaches for leprosy prevention with single-dose rifampicin: a support tool for decision making

BACKGROUND: In the past 15 years, the decline in annually detected leprosy patients has stagnated. To reduce the transmission of Mycobacterium leprae, the World Health Organization recommends single-dose rifampicin (SDR) as post-exposure prophylaxis (PEP) for contacts of leprosy patients. Various approaches to administer SDR-PEP have been piloted. However, requirements and criteria to select the most suitable approach were missing. The aims of this study were to develop an evidence-informed decision tool to support leprosy programme managers in selecting an SDR-PEP implementation approach, and to assess its user-friendliness among stakeholders without SDR-PEP experience. METHODOLOGY: The development process comprised two phases. First, a draft tool was developed based on a literature review and semi-structured interviews with experts from various countries, organisations and institutes. This led to: an overview of existing SDR-PEP approaches and their characteristics; understanding the requirements and best circumstances for these approaches; and, identification of relevant criteria to select an approach. In the second phase the tool's usability and applicability was assessed, through interviews and a focus group discussion with intended, inexperienced users; leprosy programme managers and non-governmental organization (NGO) staff. PRINCIPAL FINDINGS: Five SDR-PEP implementation approaches were identified. The levels of endemicity and stigma, and the accessibility of an area were identified as most relevant criteria to select an approach. There was an information gap on cost-effectiveness, while successful implementation depends on availability of resources. Five basic requirements, irrespective of the approach, were identified: stakeholder support; availability of medication; compliant health system; trained health staff; and health education. Two added benefits of the tool were identified: its potential value for advocacy and for training. CONCLUSION: An evidence-informed SDR-PEP decision tool to support the selection of implementation approaches for leprosy prevention was developed. While the tool was evaluated by potential users, more research is needed to further improve the tool, especially health-economic studies, to ensure efficient and cost-effective implementation of SDR-PEP

Automated Support for Model Selection Using Analytic Hierarchy Process

Providing automated support for model selection is a significant research challenge in model management. Organizations maintain vast growing repositories of analytical models, typically in the form of spreadsheets. Effective reuse of these models could result in significant cost savings and improvements in productivity. However, in practice, model reuse is severely limited by two main challenges: (1) lack of relevant information about the models maintained in the repository, and (2) lack of end user knowledge that prevents them from selecting appropriate models for a given problem solving task. This study built on the existing model management literature to address these research challenges. First, this research captured the relevant meta-information about the models. Next, it identified the features based on which models are selected. Finally, it used Analytic Hierarchy Process (AHP) to select the most appropriate model for any specified problem. AHP is an established method for multi-criteria decision-making that is suitable for the model selection task. To evaluate the proposed method for automated model selection, this study developed a simulated prototype system that implemented this method and tested it in two realistic end-user model selection scenarios based on previously benchmarked test problems

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decisionsupport system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-RadiomicsGenomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visuallyunderstandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information

An Intelligent decision support system for machine tool selection

The selection of appropriate machines and equipments is one of the most critical decisions in the design and development of a successful production environment. Considering the detailed specifications related to the functional requirements, productivity, quality, flexibility, cost, etc., and the number of available alternative machine tools in the market, the selection procedure can be quite complicated and time consuming. In this thesis, a user-friendly decision support system called Intelligent Machine Tool Selection is developed for machine tool selection. The software guides decision-maker in selecting available machines via effective algorithms: Analytic Hierarchy Process (AHP). It has some special features which may not be included in other decision support systems. The user does not need to have detailed technical knowledge as he is guided by simple questions throughout the selection process. The user first determines the relevant criteria to be considered (such as productivity, flexibility, etc.) and then makes a pair-wise comparison of each criterion to the others. There are many sub-criteria such as machine power, spindle speed, tool magazine capacity, etc. which are used to determine the scores for each criterion. If desired, some important requirements for an application, such as power and force, can be determined using process models which are also integrated to the software. The software can store the relevant new information associated with the user so that it can be made available to facilitate the successive decision-making processes. After a list of machines with their specifications is retrieved from the database based upon the user specified requirements, the selected criteria are considered in the AHP process. The application of the system is presented through several examples. Furthermore, sensitivity analysis is used to determine the most critical criterion and the most critical measure of performance. Cost analysis is carried out for the purchasing decision of a selected machine tool and its additional options. Reliability and precision analysis guide decision-maker selection of suitable machines

Sistem Pendukung Keputusan Dalam Pemilihan Karyawan Terbaik Di Pt. Smartlink Global Media Dengan Metode Weight Product

Best and qualified employees will make a company be increased in operation and can develop rapidly. However, the constraints on the PT. Smartlink Global Media which is a private enterprise telecommunications provider in Solo, still not optimal in the implementation of the election of the best employees. It is therefore necessary human resource management in an enterprise with the selection of the best employees to boost morale in improving operations, dedication and performance of the company so that it becomes better, namely to create a decision support system in the selection of the best employees by using the method of weight product. Product weight method is a method using multiplication decision to link the value of the criteria, which is where the value for each criterion should be raised to a first weighing the relevant criteria (Monica et al, 2015). This decision support system is a tool that can provide solutions and help administrators in the electoral process is computerized tebaik employees to be more effective and efficient. From calculations using the method of weight product can know the best employees of the alternatives that exist in a company. The research proves that the application is able to assist in the selection process admin selecting the best employees with methods of product weight, as well as providing the best employee information effectively and efficiently with a percentage of 91.5