A fuzzy ordinary regression method for modeling customer preference in tea maker design

Faced with fierce competition in marketplaces, manufacturers need to determine the appropriate settings of engineering characteristics of the new products so that the best customer preferences of the products can be obtained. To achieve this, functional models relating customer preferences to engineering characteristics need to be developed. As information regarding functional relationships between customer preferences are generally subjective or heuristic in nature, development of the customer preference models involve two uncertainties, namely fuzziness and randomness. Existing approaches use only fuzzy-based technologies to address the uncertainty caused by fuzziness. They are not designed to address the randomness of the observed data which is caused by a limited knowledge of the variability of influences between customer preferences and engineering characteristics. In this article, a fuzzy ordinary regression method is proposed to develop the customer preference models which are capable of addressing the two uncertainties of crispness and fuzziness of the customer preferences. A case study of a tea maker design which involves both uncertainties is used to demonstrate the effectiveness of the proposed method

OR for entrepreneurial ecosystems : a problem-oriented review and agenda

Innovation-driven entrepreneurship has become a focus for economic development and received increasing attention from policy makers and academics over the last decades. While consensus has been reached that context matters for innovation and entrepreneurship, little evidence and decision support exists for policy makers to effectively shape the environment for growth-oriented companies. We present the entrepreneurial ecosystem concept as a complex systems-based approach to the study of innovation-driven entrepreneurial economies. The concept, in combination with novel data sources, offers new opportunities for research and policy, but also comes with new challenges. The aim of this paper is to take stock of the literature and build bridges for more transdisciplinary research. First, we review emergent trends in ecosystem research and provide a typology of four overarching problems based on current limitations. These problems connect operational research scholars to the context and represent focal points for their contributions. Second, we review the operational research literature and provide an overview of how these problems have been addressed and outline opportunities for future research, both for the specific problems as well as cross-cutting themes. Operational research has been invaluable in supporting decision-makers facing complex problems in several fields. This paper provides a conceptual and methodological agenda to increase its contribution to the study and governance of entrepreneurial ecosystems

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

Setting competitiveness indicators using BSC and ANP

[EN] In this paper a new approach to assess companies' competitiveness performance in an efficient and reliable way is presented. It introduces a rigorous methodology, based on multi-criteria techniques, which seeks to assist managers of companies within a specific industrial sector in providing information about their relative position in order to define improvement action plans. The approach combines the use of the analytic network process (ANP) method with the balanced scorecard (BSC) to achieve competitiveness indicators. The ANP method allows the aggregation of experts judgments on each of the selected indicators used into one company competitiveness index (CCI). To demonstrate the goodness of the methodology, a case study of the plastic sector of Venezuela has been carried out. Three companies have been analysed using the CCI proposed. The participating experts agreed that the methodology is useful and an improvement from current competitiveness measurement techniques. They found the results obtained coherent and the use of resources significantly less than in other methods.Poveda Bautista, R.; Baptista, DC.; García Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research. 50(17):4738-4752. doi:10.1080/00207543.2012.657964S47384752501

A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples

Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements

<p>Abstract</p> <p>Background</p> <p>Studies on bacterial signal transduction systems have revealed complex networks of functional interactions, where the response regulators play a pivotal role. The AtoSC system of <it>E. coli </it>activates the expression of <it>atoDAEB </it>operon genes, and the subsequent catabolism of short-chain fatty acids, upon acetoacetate induction. Transcriptome and phenotypic analyses suggested that <it>atoSC </it>is also involved in several other cellular activities, although we have recently reported a palindromic repeat within the <it>atoDAEB </it>promoter as the single, <it>cis</it>-regulatory binding site of the AtoC response regulator. In this work, we used a computational approach to explore the presence of yet unidentified AtoC binding sites within other parts of the <it>E. coli </it>genome.</p> <p>Results</p> <p>Through the implementation of a computational <it>de novo </it>motif detection workflow, a set of candidate motifs was generated, representing putative AtoC binding targets within the <it>E. coli </it>genome. In order to assess the biological relevance of the motifs and to select for experimental validation of those sequences related robustly with distinct cellular functions, we implemented a novel approach that applies Gene Ontology Term Analysis to the motif hits and selected those that were qualified through this procedure. The computational results were validated using Chromatin Immunoprecipitation assays to assess the <it>in vivo </it>binding of AtoC to the predicted sites. This process verified twenty-two additional AtoC binding sites, located not only within intergenic regions, but also within gene-encoding sequences.</p> <p>Conclusions</p> <p>This study, by tracing a number of putative AtoC binding sites, has indicated an AtoC-related cross-regulatory function. This highlights the significance of computational genome-wide approaches in elucidating complex patterns of bacterial cell regulation.</p

Multiple controls affect arsenite oxidase gene expression in Herminiimonas arsenicoxydans

<p>Abstract</p> <p>Background</p> <p>Both the speciation and toxicity of arsenic are affected by bacterial transformations, i.e. oxidation, reduction or methylation. These transformations have a major impact on environmental contamination and more particularly on arsenic contamination of drinking water. <it>Herminiimonas arsenicoxydans </it>has been isolated from an arsenic- contaminated environment and has developed various mechanisms for coping with arsenic, including the oxidation of As(III) to As(V) as a detoxification mechanism.</p> <p>Results</p> <p>In the present study, a differential transcriptome analysis was used to identify genes, including arsenite oxidase encoding genes, involved in the response of <it>H. arsenicoxydans </it>to As(III). To get insight into the molecular mechanisms of this enzyme activity, a Tn<it>5 </it>transposon mutagenesis was performed. Transposon insertions resulting in a lack of arsenite oxidase activity disrupted <it>aoxR </it>and <it>aoxS </it>genes, showing that the <it>aox </it>operon transcription is regulated by the AoxRS two-component system. Remarkably, transposon insertions were also identified in <it>rpoN </it>coding for the alternative N sigma factor (σ⁵⁴) of RNA polymerase and in <it>dnaJ </it>coding for the Hsp70 co-chaperone. Western blotting with anti-AoxB antibodies and quantitative RT-PCR experiments allowed us to demonstrate that the <it>rpoN </it>and <it>dnaJ </it>gene products are involved in the control of arsenite oxidase gene expression. Finally, the transcriptional start site of the <it>aoxAB </it>operon was determined using rapid amplification of cDNA ends (RACE) and a putative -12/-24 σ⁵⁴-dependent promoter motif was identified upstream of <it>aoxAB </it>coding sequences.</p> <p>Conclusion</p> <p>These results reveal the existence of novel molecular regulatory processes governing arsenite oxidase expression in <it>H. arsenicoxydans</it>. These data are summarized in a model that functionally integrates arsenite oxidation in the adaptive response to As(III) in this microorganism.</p

Maximizing upgrading and downgrading margins for ordinal regression

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since upgrading and downgrading errors may not have the same importance, they should be considered as two different criteria to be taken into account when measuring the quality of a classifier. In Support Vector Machines, margin maximization is used as an effective and computationally tractable surrogate of the minimization of misclassification errors. As an extension, we consider in this paper the maximization of upgrading and downgrading margins as a surrogate of the minimization of upgrading and downgrading errors, and we address the biobjective problem of finding a classifier maximizing simultaneously the two margins. The whole set of Pareto-optimal solutions of such biobjective problem is described as translations of the optimal solutions of a scalar optimization problem. For the most popular case in which the Euclidean norm is considered, the scalar problem has a unique solution, yielding that all the Pareto-optimal solutions of the biobjective problem are translations of each other. Hence, the Pareto-optimal solutions can easily be provided to the analyst, who, after inspection of the misclassification errors caused, should choose in a later stage the most convenient classifier. The consequence of this analysis is that it provides a theoretical foundation for a popular strategy among practitioners, based on the so-called ROC curve, which is shown here to equal the set of Pareto-optimal solutions of maximizing simultaneously the downgrading and upgrading margins

A study of the wear process related to twin-screw extruders

Extruders are used in a wide range of process industries and high reliability is essential if cost effective manufacturing is to be maintained. A critical part of twin-screw extruders is the barrel that must withstand many different wear and corrosion environments depending on the end user. For many applications the extruder barrel is a critical component and it is essential that it performs in a predictable manner, providing the necessary design life-time. This project has addressed these aims by considering the wear/corrosion behaviour of current and potential extruder barrel materials from which a life prediction model has been developed. A wide range of engineering materials has been evaluated in the laboratory for abrasive wear resistance using a dry sand abrasive wear test according to ASTM G 65-93. An appraisal of the tests and the applicability of the results to the in-service conditions of an extruder has lead to further testing for abrasion and abrasion-corrosion resistance of four materials, namely Mild Steel, 440C, N18 and N18+5%TiC+5%TiN. Plastic deformation was the main feature of the damaged surfaces in the form of ploughing which has been modelled in terms of a low-cycle fatigue process. The relative hardness between material and abrasive was found to be an important parameter in controlling the rate of material removal. It has also been shown that the synergistic effect of abrasion-corrosion results in an accelerated material removal rate. The information from these tests has been used to develop a model of the wear of extruder barrels by abrasive particles. It is shown that there is a correlation between the particle size, wear debris size and wear groove size distributions. From a knowledge of the particle flux, the particle size distribution and the loading conditions, metal recession is predicted based on a low-cycle fatigue process. The wear rates for a wide range of Fe- and Ni-based materials are predicted to better than a factor of two. When corrosion is also present, the mechanism of metal recession depends on whether passive surface films are formed. For the Fe-based materials which exhibit direct dissolution of material, the wear/corrosion rate can be estimated by combining the metal loss rate under pure wear and pure corrosion conditions only. For the Ni-base alloys, thin passive films form in all the aqueous environments studied and corrosion rates are extremely low. However, during abrasive wear the passive films are removed and the overall metal recession rate is a combination of metal loss due to abrasive wear of the substrate and the continual formation and removal of surface passive films