International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013 ISSN: 2231-5381 http://www.ijettjournal.org Page 2183 Product Evaluation Using Entropy and Multi Criteria Decision Making Methods Kshitij Dashore, Shashank Singh Pawar, Nagendra Sohani, Devendra Singh Verma Department of Mechanical Engineering, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore (M.P.), India Abstract: There is variety of products of different brands available in the market for the customer of different levels which can satisfy their specific demands. The customer has been offered by means of variety of products of the same species and category with different features and attribute. This enhance the competition between the brands, resultantly make efforts to stimulate the customers towards their products by means of different policies, which sometimes can make customer confuse between the brands and their products to – what to pick and what not to. In this research paper we have taken nine laptops of different brands of nearly same range of specifications and Multi Criteria Decision Making (MCDM) Methods are applied to choose the best option among the different alternatives. Entropy method is used to evaluate the weight of the feature attributes. Keywords -Multi Criteria Decision Making Methods, Entropy, TOPSIS, Advance TOPSIS, SAW, WPM. I. INTRODUCTION There is wide range of laptop available in market with unique features and attributes. Based on different demands of the customer, manufacturers have to provide different variety of the product with different attributes and features. Customers get difficulty in selecting the best product from the ranges available in market. Multi criteria decision making method provides ranking solution to differentiate the range on the basis of product feature and product attributes. In this research paper Multi Criteria Decision Making Methods {Simple Additive Method (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Advance TOPSIS, Weighted Product Method(WPM) } are applied on different brands of laptop to choose the best option among the different alternatives. The specifications of the products taken under study are listed in the TABLE I, later in the paper. Entropy method is used to evaluate the weight of the feature attributes. II. METHODOLOGY To find out the best quantitative solution from the alternatives, multi criteria decision making process provides ranking solution of the alternatives to select the best alternatives. In this research paper we applied entropy method because it is highly reliable for information measurement and provide high accuracy in determination of weight of the feature attribute of the product. A MCDM problem can be expressed in matrix format as: ∗ ܣ1 ܣ2 ܦ= ܣ3 ⋮ ܣ ⎣ ⎢ ⎢ ⎢ ⎢ ⎡ ܥ1 ܥ2 ݔ11 ݔ12 ݔ21 ݔ22 ܥ3 ⋯ ܥ ݔ13 ⋯ ݔ1 ݔ23 ⋯ ݔ2 ݔ31 ݔ32 ⋮ ⋮ ݔ1 ݔ2 ݔ33 ⋯ ݔ3 ⋮ ⋮ ⋮ ݔ3 ⋯ ݔ ⎦ ⎥ ⎥ ⎥ ⎥ ⎤ W = [w1 w2 w3 … wn] Where A1, A2, A3 .........., Am are possible alternatives among which decision makers have to choose, C1, C2, C3, ........., Cn are criteria with which alternatives performance are measured, xij is the performance value of alternatives Ai with respect to criterion Cj, wj is the weight of criterion Cj. A) ENTROPY According to the degree of index dispersion, the weight of all indicators is calculated by information entropy. Suppose we have a decision matrix B with m alternatives and n indicators: Step 1: In matrix B, feature weight P ij is of the j th alternatives to the j th factor: p ij = ௑௜௝ ∑ ௑௜௝ ೘ ೔సభ , (1≤ i ≤ m, 1≤ j ≤ n) Step 2: The output entropy e j of the j th factor becomes e j = -k ∑ ݌ ln ݌ ௠ ௜ୀଵ , ( k= 1/ ln m, 1≤ j ≤ n) Step 3: Variation coefficient of the j th factor: g j can be defined by the following equation: d j = 1- e j , (1≤ j ≤ n) Step 4: Calculate the weight of entropy w j : w j = gj / ∑ gj ୫ ୧ୀଵ , (1≤ j ≤ n) B) TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)