Global Weights Research Articles

Developing Competitive Strategies Based on SWOT Analysis in Porter s Five Forces Model by DANP

In such a competitive business area, companies which are especially pioneers in the sector try to develop sustainable and competitive strategies to retain their positions in the market. In this purpose, in literature many theories have been developed. Because each of those strategies have their own strong and weak sides, developing new strategical analyses keeps on. In this research an integrated model of SWOT (Strengths- Weakness-Opportunities-Threats) Analysis and Porter’s Five Forces Model has been used to develop a competitive strategy model for a business firm in food sector by DEMATEL (Decision Making Trial and Evaluation Laboratory) and ANP (Analytic Network Process) techniques. Based on results, in regards of relative important items those are related to strengths and opportunities in competitors force which have considerable weights in the global weights table, maxi-maxi type competitive strategies have been developed for the company.

Key success factors of international market development

PurposeThe purpose of this paper is to develop an evaluation model to determine the relative weights of key factors influencing international market development (IMD) success through analysis network process (ANP) during group decision-making. An empirical case of the Taiwan bulk shipping industry is used to illustrate the feasibility of the proposed approach.Design/methodology/approachThe literature review is performed to generate 20 key success factors (KSFs) along with four factor categories in IMD (such as organizational capability, environmental scanning, international strategy and internationalization behavior). Then, ANP is applied to develop an evaluation model that prioritizes the relative importance linking the above four factor categories with 20 evaluated KSFs.FindingsWith respect to the final weights for factor categories, “international strategy” and “environmental scanning” are the two most important criteria, followed by “organizational capability” and “internationalization behavior”. The results also showed that by reviewing the global weights of the 20 KSFs of IMD, “service as competitive advantage”, “market potential” and “risk taking” have the highest rankings.Practical implicationsThe findings indicate that firm expansion into international markets typically depends on a successful international strategy. Hence, to enhance their global market competitiveness, Taiwan bulk shipping firms should focus their efforts on planning international market entry strategy and prioritizing shipping services with high-potential target markets.Originality/valueTheoretically, the study results can provide both theoretical basis and empirical evidence, indicating the relative weights and priorities of KSFs of IMD for the Taiwan bulk shipping industry. From the managerial perspective, the analytical results can help managers focus on main factors and identify the best policy to improve their IMD practice and performance.

Multi-criteria selection of façade systems based on sustainability criteria

The environmental and economic impacts of alternative façade systems have been widely investigated in previous studies. However, sustainable design requires reconciliation between economic, environmental and social impacts; the three pillars of a sustainable system, and selection of façade only based on environmental impacts may not always provide a sustainable solution. Through a case study, this paper presents a systematic methodology for selection of the façade system for a building by accounting the social, economic, and environmental impacts of the decision. A comprehensive list of sustainability criteria for selection of façade systems is presented and discussed in detail. The selected sustainability criteria are then applied to identify the most sustainable facade system, among five different alternatives including double brickwork, aluminium composite panel, ceramic cladding, Autoclaved Aerated Concrete (AAC) panels and concrete blocks, to replace the existing worn façade of an actual building. Delphi technique, a method of eliciting and refining group judgments, is used to identify applicable sustainability criteria and their relative pair-wise importance scores, while AHP is used to identify the global relative importance weights for different sustainability criteria and rank different alternative façade systems.

An Efficient Particle Swarm Optimization-Based Neural Network Approach for Software Reliability Assessment

In this paper, an artificial neural network (ANN)-based dynamic weighted combination model trained by novel particle swarm optimization (PSO) algorithm is proposed for software reliability prediction. Different software reliability growth models (SRGMs) are merged based on the weights derived by the learning algorithm of the proposed ANN. To avoid trapping in local minima during training of the ANN, we propose a neighborhood-based adaptive PSO (NAPSO) algorithm for learning of the proposed ANN in order to find global optimal weights. We conduct the experiments on real software failure data sets for validation of the proposed dynamic weighted combination model (PDWCM). Fitting performance and predictability of the proposed PSO-based neural network are compared with the conventional PSO-based neural network (CPSO) and existing ANN-based software reliability models. We also compare the performance of the proposed PSO algorithm with the CPSO algorithm through learning of the proposed ANN. Empirical results indicate that the proposed PSO and CPSO-based neural network present fairly accurate fitting and prediction capability than the other existing ANN-based software reliability models. Moreover, the proposed PSO-based neural network is most promising for the purpose of software fault prediction since it shows comparatively better fitting and prediction performance results than the other models.

Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network

Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction.

Cluster synchronization of coupled systems with nonidentical linear dynamics

SummaryThis paper considers the cluster synchronization problem of generic linear dynamical systems whose system models are distinct in different clusters. These nonidentical linear models render control design and coupling conditions highly correlated if static couplings are used for all individual systems. In this paper, a dynamic coupling structure, which incorporates a global weighting factor and a vanishing auxiliary control variable, is proposed for each agent and is shown to be a feasible solution. Lower bounds on the global and local weighting factors are derived under the condition that every interaction subgraph associated with each cluster admits a directed spanning tree. The spanning tree requirement is further shown to be a necessary condition when the clusters connect acyclically with each other. Simulations for two applications, cluster heading alignment of nonidentical ships and cluster phase synchronization of nonidentical harmonic oscillators, illustrate essential parts of the derived theoretical results. Copyright © 2017 John Wiley & Sons, Ltd.

Clustered intrinsic label correlations for multi-label classification

Currently a consensus on multi-label classification is to exploit label correlations for performance improvement. Many approaches build one classifier for each label based on the one-versus-all strategy, and integrate classifiers by enforcing a regularization term on the global weights to exploit label correlations. However, this strategy might be suboptimal since it may be only part of the global weights that support the assumption. This paper proposes clustered intrinsic label correlations for multi-label classification (CILC), which extends traditional support vector machine to the multi-label setting. The predictive function of each classifier consists of two components: one component is the common information among all labels, and the other component is a label-specific one which highly depends on the corresponding label. The label-specific one representing the intrinsic label correlations is regularized by clustered structure assumption. The appealing features of the proposed method are that it separates the common information and the label-specific information of the labels and utilizes clustered structures among labels represented by the label-specific parts. The practical multi-label classification problems can be directly solved by the proposed CILC method, such as text categorization, image annotation and sentiment analysis. Experiments across five data sets validate the effectiveness of CILC, compared with six well-established multi-label classification algorithms.

Acceptability measurement and priority weight elicitation of triangular fuzzy multiplicative preference relations based on geometric consistency and uncertainty indices

This paper investigates consistency and uncertainty measurements as well as acceptability checking for triangular fuzzy multiplicative preference relations (TFMPRs). A geometric consistency index is proposed to measure the consistency degree of a TFMPR, and thresholds of acceptable consistency are discussed for TFMPRs. Uncertainty indices are introduced to measure indeterminacy degrees of triangular fuzzy judgments and of TFMPRs and a notion of acceptable TFMPRs are put forward by considering both acceptable consistency and acceptable uncertainty in TFMPRs. We also explore the elicitation of triangular fuzzy weights from acceptable TFMPRs and the aggregation of local triangular fuzzy weights for solving hierarchical multi-criteria decision-making problems. A geometric mean-based equation and a logarithmic least square model are developed to elicit normalized acceptable triangular fuzzy multiplicative weights from an acceptable TFMPR. An equation and a linear program are established to transform triangular fuzzy multiplicative weights into triangular fuzzy additive weights. A geometric weighting-based equation and a pair of linear programs are devised to generate global priority fuzzy weights from local triangular fuzzy multiplicative weights. Three numerical examples, including a hierarchical multi-criteria decision-making problem, are used to illustrate how to apply the developed framework, and comparative studies are provided to show its availability and advantages.

Identifying and prioritising job burnout factors using an integrated fuzzy DEMATEL and fuzzy ANP: an empirical study in paramedical society

Recently, many researchers believe that job stress is one of the main problems in the workplace. In an overview, job stress can be attributed to a response to the pressures of the workplace. Given the difficult circumstances under which paramedics work and recurrences of clients, patients and families who expect a definitive treatment, it is essential to consider the psychosocial and social needs of this group of therapists. This study identifies and prioritises factors of job burnout in the paramedical society as a real empirical study. To achieve this goal, the factors and sub-factors of job burnout are extracted by reviewing literature and gathering experts' judgments. To prioritise the factors of burnout, an integrated model is applied based on fuzzy DEMATEL and fuzzy ANP. Fuzzy DEMATEL is used to extract the relationships between factors and sub-factors. Fuzzy DEMATEL outputs are used to form super-matrix for fuzzy ANP. The ANP outputs are global weights of identified factors and sub-factors. Based on the calculated global weights, job burnout factors are prioritised in a paramedical society as a real case study.

Solving multi-objective facility location problem using the fuzzy analytical hierarchy process and goal programming: a case study on infectious waste disposal centers

The selection of a suitable location for infectious waste disposal is one of the major problems in waste management. Determining the location of infectious waste disposal centers is a difficult and complex process because it requires combining social and environmental factors that are hard to interpret, and cost factors that require the allocation of resources. Additionally, it depends on several regulations. Based on the actual conditions of a case study, forty hospitals and three candidate municipalities in the sub-Northeast region of Thailand, we considered multiple factors such as infrastructure, geological and social & environmental factors, calculating global priority weights using the fuzzy analytical hierarchy process (FAHP). After that, a new multi-objective facility location problem model which combines FAHP and goal programming (GP), namely the FAHP-GP model, was tested. The proposed model can lead to selecting new suitable locations for infectious waste disposal by considering both total cost and final priority weight objectives. The novelty of the proposed model is the simultaneous combination of relevant factors that are difficult to interpret and cost factors, which require the allocation of resources.

A test for the global minimum variance portfolio for small sample and singular covariance

Recently, a test dealing with the linear hypothesis for the global minimum variance portfolio weights was obtained under the assumption of non-singular covariance matrix. However, the problem of potential multicollinearity and correlations of assets constitutes a limitation of the classical portfolio theory. Therefore, there is an interest in developing theory in the presence of singularities in the covariance matrix. In this paper, we extend the test by analyzing the portfolio weights in the small sample case with a singular population covariance matrix. The results are illustrated using actual stock returns and a discussion of practical relevance of the model is presented.

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85–89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic.

Application of Analytic Hierarchy Process for Assessing Sustainable Development among Underprivileged Communities

<p>One of the purposes of sustainable development assessment is to identify the most importance criteria and sub-criteria of sustainable development that have the most significant contribution to the local community. To date, few studies have inquired into qualitative methods to assess these criteria and sub-criteria. In response to this gap in the literature, we propose an application of the analytic hierarchy process (AHP) method to prioritize thirteen sub-criteria of sustainable development among underprivileged community of Setiu Wetlands Terengganu. Consistency ratio and weighted geometric mean are the two important computation steps of the AHP prior to proposing global weights of sub-criteria. The computational results indicate that ‘Education’ is the most important sub-criteria with 15.4 % of global weight. At the other extreme ‘global economic partnership’ is the least important sub-criteria for this group of community. The outcome of the proposed method is a weight of sustainability for all sub-criteria which offers a guide to government in identifying the appropriate action for uplifting the community quality of life.</p>

Prioritization and Aggregation of Intuitionistic Preference Relations: A Multiplicative-Transitivity-Based Transformation from Intuitionistic Judgment Data to Priority Weights

This article proposes a goal programming framework for deriving intuitionistic fuzzy weights from intuitionistic preference relations (IPRs). A new multiplicative transitivity is put forward to define consistent IPRs. By analyzing the relationship between intuitionistic fuzzy weights and multiplicative consistency, a transformation formula is introduced to convert normalized intuitionistic fuzzy weights into multiplicative consistent IPRs. By minimizing the absolute deviation between the original judgment and the converted multiplicative consistent IPR, two linear goal programming models are developed to obtain intuitionistic fuzzy weights from IPRs for both individual and group decisions. In the context of multicriteria decision making with a hierarchical structure, a linear program is established to obtain a unified criterion weight vector, which is then used to aggregate local intuitionistic fuzzy weights into global priority weights for final alternative ranking. Two numerical examples are furnished to show the validity and applicability of the proposed models.

구간데이터 정규화와 계층적 분석과정에의 활용

Entani and Tanaka(2007)는 불확실한 데이터를 처리하기에 적합한 구간 평가결과를 얻는 새로운 방법을 제시하였다. 무엇보다 그들의 방법은 구간 데이터를 정규화하여 redundant 데이터를 제거하는데 특징이 있다. 더 나가 정규화된 구간데이터를 활용하여 계층분석과정(AHP)에서 최종 구간 우선순위벡터를 도출한다. 이 논문에서는 구간 데이터의 정규화 목적을 달성하기 위해 구간 데이터의 꼭지점을 구하는 쉽고 간편한 휴리스틱 방법을 제시한다. 한편 간단한 검사법을 활용하여 정규화된 데이터를 활용하여 최종 구간 우선순위벡터를 도출하는 방법을 제시하고자 한다. 아울러 Entani and Tanaka(2007)가 제시한 대안간 지배관계 규명 방법을 확장한 지배관계 규명 방법을 제시하고자 한다. Entani and Tanaka (2007) presented a new approach for obtaining interval evaluations suitable for handling uncertain data. Above all, their approach is characterized by the normalization of interval data and thus the elimination of redundant bounds. Further, interval global weights in AHP are derived by using such normalized interval data. In this paper, we present a heuristic method for finding extreme points of interval data, which basically extends the method by Entani and Tanaka (2007), and also helps to obtain normalized interval data. In the second part of this paper, we show that the solutions to the linear program for interval global weights can be obtained by a simple inspection. In the meantime, the absolute dominance proposed by the authors is extended to pairwise dominance which makes it possible to identify at least more dominated alternatives under the same information.

Weighted Global Artificial Bee Colony Algorithm Makes Gas Sensor Deployment Efficient.

This paper proposes an improved artificial bee colony algorithm named Weighted Global ABC (WGABC) algorithm, which is designed to improve the convergence speed in the search stage of solution search equation. The new method not only considers the effect of global factors on the convergence speed in the search phase, but also provides the expression of global factor weights. Experiment on benchmark functions proved that the algorithm can improve the convergence speed greatly. We arrive at the gas diffusion concentration based on the theory of CFD and then simulate the gas diffusion model with the influence of buildings based on the algorithm. Simulation verified the effectiveness of the WGABC algorithm in improving the convergence speed in optimal deployment scheme of gas sensors. Finally, it is verified that the optimal deployment method based on WGABC algorithm can improve the monitoring efficiency of sensors greatly as compared with the conventional deployment methods.

Triple bottom line performance evaluation of reverse logistics

Purpose The paper aims to incorporate the relationship of reverse logistics into the economic, environmental, and social sustainability, known as triple bottom line and developed a framework for reverse logistics performance evaluation. Design/methodology/approach The performance measures, based on triple bottom line approach, were selected, and fuzzy analytical hierarchy process and extent analysis approach was applied for estimating the weights, global weights of performance measures and hence, the reverse logistics performance index. Reverse logistics performance of three electronic companies were evaluated and compared for the demonstration of the methodology. Findings The results show that economic performance has highest performance index followed by environmental performance and social performance. “Recapturing value” and “return on investment” from economic, “minimum energy consumption” and “optimum use of raw material” from environmental and “community complaints” and “customer health and safety” from social perspective have higher performance indexes. Over all, “reduced packaging”, “use of recycled material” and “employee benefits” show very poor performance indexes. Research Limitations/implications The study will provide useful guidance to the academicians and practitioners for evaluating, improving and benchmarking the reverse logistics performance. Originality/value The analysis adds to the very few studies on triple bottom line aspects of reverse logistics and its performance evaluation. Also, fuzzy analytical hierarchy process and extent analysis is used first time being an efficient tool to tackle the fuzziness of the data involved in performance evaluation.

Advances on image interpolation based on ant colony algorithm.

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.

Linguistic Reasoning Petri Nets for Knowledge Representation and Reasoning

This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach.

Investing in Global Equity Markets with Particular Emphasis on Chinese Stocks

In this analysis of the risk and return of stocks in global markets, we build several models of stock selection and create optimized portfolios to outperform a global benchmark. We apply several applications of robust regression techniques in producing stock selection models and several Markowitz-based optimization techniques in portfolio construction in various global stock universes. We test separate Japanese and Chinese stock selection models because they are large markets, with large global benchmark weights or are frequently in the news. We find that (1) that robust regression applications are appropriate for modeling stock returns in global markets; and (2) mean-variance techniques continue to produce portfolios capable of generating excess returns above transactions costs; and (3) our models pass data mining tests such that the models produce statistically significant asset selection. We estimate expected return models in a global equity markets using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques.