A decision making methodology for material selection using an improved compromise ranking method

An ever increasing variety of materials is available today, with each having its own characteristics, applications, advantages, and limitations. In choosing the right material, there is not always a single definite attribute of selection and the designers and engineers have to take into account a large number of material selection attributes. This paper presents a logical procedure for material selection for a given engineering application. The procedure is based on an improved compromise ranking method considering the material selection attributes and their relative importance for the application considered. Two examples are included to illustrate the approach.     

Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study

Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.     

Material selection for thin-film solar cells using multiple attribute decision making approach

This paper presents a material selection approach for selecting absorbent layer material for thin-film solar cells (TFSCs) using multiple attribute decision making (MADM) approach. In this paper, different possible materials for absorbent layer and their properties like band gap, absorption coefficient, diffusion length, thermodynamic compatibility and recombination velocity is taken into consideration and MADM approach is applied to select the best material for thin-film solar cells. It is observed that Copper Indium Gallium Diselinide (CIGS) is the best material for the absorbent layer in thin-film solar cells out of all possible candidates. It was observed that the proposed result is in accordance with the experimental findings thus justifying the validity of the proposed study.     

Automated sampling decision scheme for the AVM system

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic virtual metrology based intelligent sampling decision (ISD) scheme had been previously developed for reducing the sampling rate and sustaining the virtual metrology (VM) accuracy. However, the desired sampling rate of the ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot adaptively increase the default sampling rate in the ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the default sampling rate in ISD, which may result in unnecessary waste. Accordingly, this paper proposes an automated sampling decision (ASD) scheme to adaptively and automatically modify the sampling rate online and in real time for continuous improvement. The ASD scheme can monitor the VM accuracy online as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the ASD scheme can automatically reduce the sampling rate while the VM accuracy performs well.     

RISK DISK: A PC decision aid for human performance analysis

RISK DISK_CR is a set of IBM compatible PC diskettes that provide estimates from data, of the probabilities that certain tasks are finished within relevant time limits (windows) by different agents (crews). An important application is to performance of time-sensitive safety-related human interactions required during nuclear power plant operation.

The probability of satisfactory performance may be assessed from nuclear plant-specific data, augmented by other relevant data as desired, using modified log-normal and Weibull models. Procedures for assessing model fit, and for deriving informative error bounds, are made available.

There is no implication that RISK DISK actually calculates risk, in the sense of probability of an event multiplied by its consequence or penalty.     

Enterprise risk management: a DEA VaR approach in vendor selection

Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. The development and current status of ERM is presented, with a demonstration of how risk modelling can be applied in supply chain management. Within supply chain management, a major managerial decision is vendor selection. We start with discussion of the advanced ERM technology, i.e. value-at-risk (VaR) and develop DEA VaR model as a new tool to conduct risk management in enterprises. A vendor selection set of data is used to demonstrate how this model can be used to assess supply risks in ERM. Such models provide means to quantitatively improve decision making with respect to risk.     

Multi-criteria decision analysis for prospective sustainability assessment of alternative technologies and fuels for individual motorized transport

Reducing greenhouse gas (GHG) emissions in the transport sector is one of the biggest challenges in the German energy transition. Furthermore, sustainable development does not stop with reducing GHG emissions. Other environmental, social and economic aspects should not be neglected. Thus, here a comprehensive sustainability assessment for passenger vehicles is conducted for 2020 and 2050. The discussed options are an internal combustion engine vehicle (ICEV) fuelled with synthetic biofuel and fossil gasoline, a battery electric vehicle (BEV) with electricity from wind power and electricity mix Germany and a fuel cell electric vehicle (FCEV) with hydrogen from wind power. The life cycle-based assessment entails 13 environmental indicators, one economic and one social indicator. For integrated consideration of the different indicators, the MCDA method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is chosen. For the assessment, a consistent assessment framework, i.e. background scenario and system boundaries, and a detailed modelling of vehicle production, fuel supply and vehicle use are the cornerstones. The BEV with wind power is the most sustainable option in 2020 as well as in 2050. While in 2020, the second rank is taken by the ICEV with synthetic biofuel from straw and the last rank by the FCEV, in 2050 the FCEV is the runner-up. With the help of MCDA, transparent and structured guidance for decision makers in terms of sustainability assessment of motorized transport options is provided.

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A heuristic-based approach to integrate the product line selection decision to the supply chain configuration

Product platform development (PPD) as an approach to mass customisation (MC) helps an organisation to reduce costs as well as ensure timely deliveries. Varieties are offered to different market segments by combining and incorporating different modules at different levels. Modules at different levels are essentially features that a customer segment is looking for. It is apparent that overall optimisation would require simultaneous consideration of not only PPD but also other supply chain constraints. In this paper, an attempt has been made to develop a mixed integer linear programming (MILP) model for sourcing, production planning, and PPD decisions. Based on analyses of the model, a heuristic solution procedure has been suggested. The heuristic developed in this paper attempts to decompose the problem and then assimilate the outputs from the simpler parts to obtain the final solution. Finally, a simple example to illustrate the solution procedure is presented.     

REX: An intelligent decision and analysis aid for reliability and risk studies

A review of the role of expert system technology in performing Probabilistic Risk Assessments (PRA) is presented. Important aspects of the system analysis part of PRA studies are discussed. Intensive human activities in system analysis are studied and represented in a rule based expert system called REX. Object oriented programming method is used to develop REX. All important aspects of REX development including its structure and important rules used are described. Finally our experience with REX is presented.     

A strategic decision model for the selection of advanced technology

This paper introduces models for the selection of advanced technologies. It is based on the concept that technologies are mutually dependent. As such, higher productivity could be realized by simultaneously selecting dependent technologies. Synergistic benefits are realized if such technologies are selected. This paper, therefore, uses the analytic hierarchy process (AHP) and an adjusted priority method to evaluate and rank technologies. A greedy heuristic algorithm is then used to allocate resources to the different technologies.     

Multiple comparisons with the best for supplier selection with linear profiles

In some manufacturing processes, the quality of a process or product is characterised by a linear profile. Comparing the process yield of multiple suppliers with linear profiles is an important task in supplier evaluation. In this study, we consider linear profiles with two-sided specifications and present the multiple comparisons with the best method based on the process yield index to select the best supplier. A subset contains the best supplier determined from the confidence interval of the difference between the process yield indices of the unknown best supplier and all of the suppliers. A simulation study is used to conduct the statistical power analysis. The results confirm that the larger the number of levels or the number of profiles, the larger the power of test. The simulation results indicate that our proposed method can effectively identify the best supplier. Two real examples are used to illustrate the applications of our proposed method.     

Multicriteria decision aid models for the prediction of securities class actions: evidence from the banking sector

In recent years, there has been an increase in the number and value of securities class actions (SCAs), attracting the attention of various stakeholders such as investors, managers, policy makers, lawyers, etc. The present study extends the literature, by investigating for the first time the development of a classification model to forecast SCAs filed against US banks. Our results show that the proposed multicriteria decision aid model achieves a satisfactory accuracy, by classifying correct around 80 % of the banks in an out-of-sample testing. We obtain similar results when we use a walk-forward approach, instead of a tenfold cross-validation technique, for the estimation and testing of the model. Further analysis indicates that the classification accuracies can improve further by the inclusion of a corporate governance indicator that relates to executive and director compensation and ownership.     

Case-based reasoning for selection of the best practices in low-carbon city development

Cities emit extensive carbon emissions, which are considered a major contributor to the severe issue of climate change. Various low-carbon development programs have been initiated at the city level worldwide to address this problem. These practices are invaluable in promoting the development of low-carbon cities. Therefore, an effective approach should be developed to help decision makers select the best practices from previous experience on the basis of the impact features of carbon emission and city context features. This study introduces a case-based reasoning methodology for a specific city to select the best practices as references for low-carbon city development. The proposed methodology consists of three main components, namely, case representation, case retrieval, and case adaption and retention. For city representation, this study selects city context features and the impact features of carbon emission to characterize and represent a city. The proposed methodology is demonstrated by applying it to the selection of the best practices for low-carbon development of Chengdu City in Sichuan Province, China.     

Justification for the selection of manufacturing technologies: a fuzzy-decision-tree-based approach

In this paper, a developed model for the justification of alternative manufacturing technologies is presented. The approach, based on fuzzy decision trees, provides a methodology capable of identifying patterns within a technology case repository to support the evaluation of manufacturing systems. Experts are highly influential individuals in the decision process; they provide support and guidance when selecting investments. The experience-oriented task is founded on previous cases or an experts’ experience, and therefore difficult to express in a rational form. The concept is based on a number of characteristics of the case-based reasoning, rule induction and expert system theory. Structured around the fuzzy-decision-tree data-mining technique, the framework provides the ability of using regulated case information to act as structured experience for assisting in the decision process. Fuzzy induction extracts formal rules from a set of experience data, and the expert system philosophy computes the experience base of human expertise for problem-solving. A test case indicates the stability of the classification algorithm and verifies the applicability within the domain.     

Training for emergency management: tactical decision games.

Training of the non-technical skills that are crucial to effective management of emergency situations is an issue that is currently receiving increasing emphasis in the petrochemical sector. A case study is presented of the explosion and fires at the Texaco Refinery, Milford Haven, UK, which occurred in July 1994 (HSE, The explosion and fires at the Texaco Refinery, Milford Haven, 24 July 1994. HSE: London, 1997), with particular focus on the human factors aspects of the event. A key issue identified by the official report into this incident was the importance of emergency management training. This paper outlines a novel, low-fidelity training intervention, the tactical decision game (TDG), which is designed to enhance the non-technical skills (decision making, situation awareness, communication and co-ordination, teamwork, and stress management) required for emergency management. It is proposed that enhanced learning of these non-technical skills, through experience and directed practice following repeated exposure to TDGs, will lead to more efficient emergency management, particularly when dealing with hazardous materials.     

Foundations for a robust theory of decision making: the simple case

Summary We provide axiomatic foundations to model imprecision in a subject’s preferences and beliefs through a set of probability distributions and a set of utility functions, in the simple case.     

Interactive selection model for supplier selection process: an analytical hierarchy process approach

Supplier Selection Process (SSP) becomes increasingly important for most manufacturing firms as it helps to reduce directly cost to the bottom line. The selection process involves the determination of quantitative and qualitative factors so as to select the best possible suppliers. It is essential to identify the relationship with the suppliers in terms of tangible factors. Owing to subjective human judgement in determining the relative importance of those selection factors, a method called Chain of Interaction is proposed to solve the problems associated with the dynamic nature of supply chain management. Focusing on the goodwill of an Analytic Hierarchy Process, an Interactive Selection Model is suggested to systemize the earlier steps, such as the determination of buyer-supplier relationships and formation of selection criteria, before the implementation of the Analytic Hierarchy Process with the help of Multi-Criterion Decision Making software called Expert Choice. The proposed Interactive Selection Model can be applied to supplier selection through the identification of buyer-supplier interactions and the valid data-collection methods. A numerical example is presented and the pros and cons of the model examined to seek further modification.     

Using geospatial data for decision making for a MSW management plan in the city: a case study in Bangkok,Thailand

Clean Technologies and Environmental Policy - Under the pressure of rapid urbanization, many cities encounter a great challenge in providing decent and efficient waste management service. Many...