Hsu- Shih

An extension of TOPSIS for group decision making

An extension of TOPSIS (technique for order performance by similarity to ideal solution), a multi-attribute decision making (MADM) technique, to a group decision environment is investigated. TOPSIS is a practical and useful technique for ranking and selection of a number of externally determined alternatives through distance measures. To get a broad view of the techniques used, we provide a few options for the operations, such as normalization, distance measures and mean operators, at each of the corresponding steps of TOPSIS. In addition, the preferences of more than one decision maker are internally aggregated into the TOPSIS procedure. Unlike in previous developments, our group preferences are aggregated within the procedure. The proposed model is indeed a unified process and it will be readily applicable to many real-world decision making situations without increasing the computational burden. In the final part, the effects of external aggregation and internal aggregation of group preferences for TOPSIS with different computational combinations are compared using examples. The results have demonstrated our model to be both robust and efficient.

A hybrid MCDM model for strategic vendor selection

Proposed in this study is a hybrid model for supporting the vendor selection process in new task situations. First, the vendor evaluation problem is formulated by the combined use of the multi-criteria decision-making (MCDM) approach and a proposed five-step hybrid process, which incorporates the technique of an analytic network process (ANP). Then the modified TOPSIS (technique for order performance by similarity to idea solution) is adopted to rank competing products in terms of their overall performances. The newly developed ANP will eventually yield the relative weights of the multiple evaluation criteria, which are obtained from the nominal group technique (NGT) with interdependence. An example is solved to illustrate the effectiveness and feasibility of the suggested model. The empirical study has demonstrated how the approach can be used for the strategic vendor selection problem.

A hybrid neural network approach to bilevel programming problems

A combined neural network and tabu search hybrid algorithm is proposed for solving the bilevel programming problem. To illustrate the approach, two numerical examples are solved and the results are compared with those in the literature.

A review of Hopfield neural networks for solving mathematical programming problems

The Hopfield neural network (HNN) is one major neural network (NN) for solving optimization or mathematical programming (MP) problems. The major advantage of HNN is in its structure can be realized on an electronic circuit, possibly on a VLSI (very large-scale integration) circuit, for an on-line solver with a parallel-distributed process. The structure of HNN utilizes three common methods, penalty functions, Lagrange multipliers, and primal and dual methods to construct an energy function. When the function reaches a steady state, an approximate solution of the problem is obtained. Under the classes of these methods, we further organize HNNs by three types of MP problems: linear, non-linear, and mixed-integer. The essentials of each method are also discussed in details. Some remarks for utilizing HNN and difficulties are then addressed for the benefit of successive investigations. Finally, conclusions are drawn and directions for future study are provided.

An interactive approach for integrated multilevel systems in a fuzzy environment

This study proposes a total solution of an interactive approach for integrated multilevel systems or multilevel programming problems (MLPPs) in a fuzzy environment. Simulating the actual decision-making process of the hierarchical structure of an organization, MLPP is a practical and useful approach to decentralized planning problems. Because of the complexity of the problems, there are no traditional techniques efficient enough to obtain the numerical solution of a reasonable size problem. Hence, Shih et al. [1] propose a fuzzy approach for MLPPs, to simplify the complex structure, which is proven to be feasible and efficient. The imprecise MLPPs will be involved when the coefficients of MLPPs cannot be estimated exactly. Because of such a complicated situation in the real world, we will take advantage of an interactive technique to improve the flexibility and robustness of its decision through progressive articulation of decision information from decision makers (DMs). Roughly speaking, there are two interactive procedures for imprecise MLPPs: inside loop and outside loop. The former is for the preference of the DMs, represented by fuzzy membership functions; the latter for the imprecision of coefficients, described by possibility distributions or cut-off values. Special considerations will be given to the compensatory operator, positive and negative ideal solutions, risk attitude, and @e-constraints. In the final section, linear-programming type and network-flow type of imprecise MLPPs will be solved separately as an integrated multilevel system.

A forecasting decision on the sales volume of printers in Taiwan: An exploitation of the Analytic Network Process

This study applies the Analytic Network Process (ANP) to forecast the sales volume of printers in Taiwan for adjusting the recycling and treatment fee as an incentive for recycling industries. When historical data are lacking and when a broad spectrum of social impact is involved, the ANP, with the capacity to manage dependence and feedback among the factors, can serve as a tool to forecast outcomes by using expert judgment. The priorities derived from numerical judgment are similar to probabilities. They are obtained from the limit supermatrix of the ANP that represents forecasts for the next period. The result of back testing has shown that the ANPs percentage error is small compared with those of some naive statistical techniques. Sensitivity analysis is also made to ensure robustness of the model. Finally, the characteristic strengths of the Analytic Hierarchy Process (AHP) and ANP in forecasting are discussed to simplify their use in future applications.

Establishing talent management for company's succession planning through analytic network process: Application to an MNC semiconductor company in Taiwan

This study is to design a leadership development program for a companys succession planning which will be important for its future competition. A semiconductor assembly and testing multinational corporation (MNC) in Taiwan was selected for interviews of its high level management to address the business strategy and challenge. Along with an investigation of the literature, this research works on the experiences and leadership competencies needed for those in leadership positions. The major consideration on designing leadership development program is to deal with many intangible factors, as well as dependent relationship among experiences and leadership competencies. Then ANP approach is applied to overcome both difficulties through pairwise comparisons by experts. A weight system of experiences and leadership competencies is then built for designing the leadership development program, as well as the decision basis of leadership selection. This model was approved by top management at the case company and being implemented to prove its validity.

Determining a Subsidy Rate for Taiwan's Recycling Glass Industry: An Application of Bi-Level Programming

This study attempts to optimize the operations of the Recycling Fund Management Board (RFMB), founded by the Environmental Protection Administration of the R.O.C. Government (on Taiwan), through the decision of a subsidy rate for the domestic glass recycling industry. The hierarchical and interactive nature between the two parties is modelled by bi-level programming, where the RFMB plays the upper-level decision unit while the recycling industry is the lower-level counterpart. In order to solve the problem by optimization software, the bi-level formulation is transformed to a single-level problem via Karush-Kuhn-Tucker optimality conditions and is further transformed to a 0−1 mixed integer programming problem by variable substitution. The problem is solved with real-world data, and the obtained solutions are analysed and compared with the RFMB’s current operations. The results suggest that the proposed approach can improve the operations of the RFMB.

A Multiple Criteria Decision Making Method Based on Relative Value Distances

Abstract This paper proposes a new multiple criteria decision-making method called ERVD (election based on relative value distances). The s-shape value function is adopted to replace the expected utility function to describe the risk-averse and risk-seeking behavior of decision makers. Comparisons and experiments contrasting with the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method are carried out to verify the feasibility of using the proposed method to represent the decision makers’ preference in the decision making process. Our experimental results show that the proposed approach is an appropriate and effective MCDM method.