34978103
Inter-company comparison using modified TOPSIS with objective weights
Abstract Simultaneous consideration of multiple financial ratios is required to adequately evaluate and rank the relative performance of competing companies. This paper formulates the inter-company comparison process as a multi-criteria analysis model, and presents an effective approach by modifying TOPSIS for solving the problem. The modified TOPSIS approach can identify the relevance of the financial ratios to the evaluation result, and indicate the performance difference between companies on each financial ratio. To ensure that the evaluation result is not affected by the inter-dependence of the financial ratios, objective weights are used. As a result, the comparison process is conducted on a commonly accepted basis and is independent of subjective preferences of various stakeholders. An empirical study of a real case in China is conducted to illustrate how the approach is used for the inter-company comparison problem. The result shows that the approach can reflect the decision information emitted by the financial ratios used. The comparison of objective weighting methods suggests that, with the modified TOPSIS approach, the entropy measure compares favourably with other methods for the case study conducted. Scope and purpose The performance evaluation and ranking of modern enterprises is a complex process, in which multiple financial ratios are required to be considered simultaneously. The purpose of this paper is to apply the framework of multi-criteria analysis to the inter-company comparison problem. An effective approach based on the concept used by the technique for order preference by similarity to ideal solution (TOPSIS) is developed to rank competing companies in terms of their overall performance on multiple financial ratios. The approach modifies the TOPSIS method by using weighted Euclidean distances to ensure a meaningful interpretation of the evaluation result. The use of objective weights for financial ratios based on Shannon's entropy concept reflects the context-dependent concept of informational importance. This ensures that the evaluation result is not affected by the inter-dependency of criteria and inconsistency of subjective weights. This approach is particularly applicable for situations where reliable subjective weights cannot be obtained. With its simplicity in both concept and computation, the approach can readily be incorporated into a computer-based system.
6763775
A Metaheuristic Approach to Fuzzy Project Scheduling
In practice, projects may contain many activities. To schedule such projects, under constraints of limited resource and precedence relations, it becomes an NP hard problem. Any exact algorithms will have difficulty solving such problems. In addition, many activities of a project are quite often imprecise and vague due to lack of sufficient information. Fuzzy set theory is the best way to describe such data. In this study, a fuzzy simulated annealing approach is developed to handle resource-constrained project scheduling with fuzzy data.
5730613
A Rule-Based Approach for Fuzzy Overhaul Scheduling
Decisions on an overhaul schedule are often required by a dredging company in Shanghai because the fleet of dredges require regularly maintenance due to their heavy duty work. The decision on an overhaul schedule attempts to determine how the overhaul project completion time can be minimized. However, there are a number of constraints where jobs in the overhaul project have precedence relations, and human resources are limited. In addition, job duration times are fuzzy, and jobs may have several performance modes. Because of the complexity of computation, any exact approaches may be not viable. This paper proposes a fuzzy rule-based heuristic approach using the information available in the system. The approach developed here provides a fast scheduling result in such a complex decision, aiding readjustment of production in time. In addition, the approach also helps the company to save significant costs annually in decisions on overhaul projects.
32278799
Personalized Multilingual Web Content Mining
Personalized multilingual Web content mining is particularly important for user who wants to keep track of global knowledge that is relevant to his/her personal domain of interest over the multilingual WWW. This paper presents a novel concept-based approach to personal multilingual Web content mining by constructing a personal multilingual Web space using self-organising maps. Multilingual linguistic knowledge required to define the multilingual Web space is made available by encoding all multilingual concept-term relationships using a multilingual concept map. With this map as the linguistic knowledge base, a concept-based multilingual text miner is developed to reveal the conceptual content of multilingual Web documents and to form concept categories of multilingual Web documents on a concept-based browsing interface. To construct the personal multilingual Web space, a concept-based user profile is generated from a user’s bookmark file for highlighting the user’s topics of information interests on the browsing interface. As such, personal multilingual Web mining activities ranging from explorative browsing to user-oriented concept-focused information filtering are facilitated.
7026890
Improving Business Failure Predication Using Rough Sets with Non-financial Variables
Rough set models with financial variables have proven to be effective in predicting business failure. To enhance the predictive performance of rough set models, this paper includes a non-financial variable, auditor switching, into the modeling process, in addition to 14 financial ratios commonly used in business failure research. An empirical study on 62 failed firms and 62 one-to-one matching non-failed firms in Taiwan between 1998 and 2005 is conducted, using available data for the three years before failure. Six rough set models are constructed individually with and without the auditor switching variable, using the three-year data respectively. The empirical study shows that the non-financial variable is the most significant attribute and plays an essential role in enhancing the performance of rough set models. These findings highlight the effectiveness of rough set models for business failure prediction and particularly the importance of incorporating non-financial variables in business failure research.
46527427
Form design of product image using grey relational analysis and neural network models
This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product form elements and product image, thus identifying the most influential elements of product form for a given product image. A grey prediction (GP) model and a neural network (NN) model are used individually and in conjunction with the GRA model, in order to predict and suggest the best form design combination. An experimental study on the form design of mobile phones is conducted to evaluate the performance of these models. Based on expert surveys, the concept of Kansei Engineering is used to extract and evaluate the experimental samples, and a morphological analysis is used to extract form elements from these sample mobile phones. The evaluation result shows that all the NN-based models outperform the GP-based models, suggesting that the NN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image. The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones.
7363085
An Empirical Study On The Generation Of Zero Anaphors In Chinese
In this paper, we describe the creation of rules for generating Chinese zero anaphors through a sequence of experiments in a stepwise enhanced manner. In the experiments, we basically examined the occurence of zero anaphors in a real text and the ones generated by the algorithms employing the rules, assuming the same semantic and discourse structures as the text. The factors of locality, syntactic constraints, discourse structure and salience of objects were considered in the rules. The results of the experiment show that 93% of the zero anaphors in the text can be correctly generated by an algorithm using a rule involving all the above factors.
10821054
A Kernel-Based Two-Stage One-Class Support Vector Machines Algorithm
One-class SVM is a kernel-based method that utilizes the kernel trick for data clustering. However it is only able to detect one cluster of non-convex shape in the input space. In this study, we propose an iterative two-stage one-class SVM to cluster data into several groups. In the first stage, one-class SVM is used to find an optimal weight vector for each cluster in the feature space, while in the second stage the weight vector is used to refine the clustering result. A mechanism is provided to control the optimal hyperplane to work against outliers. Experimental results have shown that our method compares favorably with other kernel based clustering algorithms, such as KKM and KFCM on several synthetic data sets and UCI real data sets.