A comparative study of multiple-criteria decision-making methods under stochastic inputs

This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative

Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

The attractiveness of countries for FDI. A fuzzy approach

This paper presents a new method for measuring the attractiveness of countries for FDI. A ranking is built using a fuzzy expert system whereby the function producing the final evaluation is not necessarily linear and the weights of the variables, usually defined numerically, are replaced by linguistic rules. More precisely, weights derive from expert opinions and from econometric tests on the determinants of countries’ FDI. As a second step, the view-point of investors from two different investing economies, the UK and Italy, are taken into account. Country-specific factors, such as the geographic, cultural and institutional distances existing between the investing and the partner economies are included in the analysis. This shows how the base ranking changes with the investor’s perspective.foreign direct investments; fuzzy expert systems; attractiveness

The Attractiveness of Countries for FDI. A Fuzzy Approach

This paper presents a new method for measuring the attractiveness of countries for FDI. A ranking is built using a fuzzy expert system whereby the function producing the final evaluation is not necessarily linear and the weights of the variables, usually defined numerically, are replaced by linguistic rules. More precisely, weights derive from expert opinions and from econometric tests on the determinants of countries’ FDI. As a second step, the view-point of investors from two different investing economies, the UK and Italy, are taken into account. Country-specific factors, such as the geographic, cultural and institutional distances existing between the investing and the partner economies are included in the analysis. This shows how the base ranking changes with the investor’s perspectiveforeign direct investments; fuzzy expert systems; attractiveness;

Location Planning of Urban Distribution Center under Uncertainty: A Case Study of Yogyakarta Special Region Province, Indonesia

Purpose: The paper aims at proposing a framework of hybrid spatial-fuzzy multi-criteria decision-making and demonstrating application of the framework to evaluate and select the appropriate location for Urban Distribution Center in Yogyakarta Special Region Province, Indonesia. The study has been inspired by the need to evaluate the Urban Distribution Center initiative, i.e., Jogja Inland Port by local government that has been hampered due to lack of participating companies. Design/methodology/approach: The proposed framework consists of two steps of analysis. First, spatial analysis to generate alternative locations using weighted Geographical Information System data; second, fuzzy multi-criteria decision-making to select the best location. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution was applied to deal with multi-criteria, multiple stakeholders, and uncertainty. Accessibility, security, connectivity of multi-modal transport, costs, environmental impact, proximity to customers, proximity to suppliers, resource availability, expansion possibility, service quality, are taken as the decision criteria. Local government of Yogyakarta province, practitioners, and logistic expert, are involved as representative participants in evaluating the Urban Distribution Center location of Yogyakarta Special Region Province. Findings: The proposed framework has indicated that the Jogja Inland Port is not the best alternative. A joint warehouse managed by a group of private companies located in Berbah (Sleman district) appears to be the best alternative location for Urban Distribution Center. Consistent results are also found by using other approaches (Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution and Set Pair Analysis). Research limitations/implications: In addition to the government, expert, and practitioners that involved in this study, future research could engage local residents as decision makers to refine the results, as various stakeholders may come up with different preferences. Practical implications: From a practical point of view, the application of combined approach (integrating spatial analysis using weighted Geographical Information System data and fuzzy multi-criteria decision making) is a promising approach in dealing with Urban Distribution Center location problem which is characterized by multi-criteria, multiple stakeholders, spatial-related issues, and uncertainty. Social implications: The unsuccessful establishment of Jogja Inland Port implies that Urban Distribution Center location problem is a complex system, involving multifaceted factors that should be considered simultaneously. Originality/value: The research proposes a framework to evaluate and select the appropriate location for Urban Distribution Center through combined approach of weighted Geographical Information System data and fuzzy multi-criteria decision-making which involves relevant stakeholders.Peer Reviewe

Spherical harmonics coeffcients for ligand-based virtual screening of cyclooxygenase inhibitors

Background: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. Methodology/Principal Findings: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. Conclusions/Significance: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort