An Improved Methodology for Multi-Criteria Evaluations in the Shipping Industry

Abstract

SummaryThis paper presents a reliable, easy and more objective approach for ranking and determining preference in a multi-criteria decision-making problem within the shipping industry. Through the integration of the improved score function, fuzzy Shannon's entropy method and the interval-valued intuitionistic fuzzy M-TOPSIS method, for ranking and for representing the aggregated effect of positive and negative evaluations in the performance ratings of the alternatives based on interval-valued intuitionistic fuzzy set (IVIFS) data. The integration of the improved score function, fuzzy Shannon's entropy method and the intervalvalued intuitionistic fuzzy M-TOPSIS method in this paper has provided a whole new approach for solving multi-criteria decision-making problems. The improved score function which is applied to the calculation of the separation measures of each alternative from the positive and negative ideal solutions. Reflect and model the fuzziness and hesitation of the decision-maker subjective assessment, while the fuzzy Shannon's entropy method is been used for calculating the criteria weight. The proposed method has successfully been applied to rank and determined the most appropriate shipping partner for a shipping company located in Malaysia, and for a modified hypothetical example which is based on the selection of a preferred Ship as a reference for a new design. The model has been compared with existing model and we can conclude, it provides a better alternative method for ranking and for the determination of preference in a multi-criteria decision-making problem.

Systematic model for lean product development implementation in an automotive related company

“…The M-TOPSIS method is "described as a process of calculating the distance between the alternatives and the reference points in the D+D−-plane and constructing the R value to evaluate the quality of alternative" (Ren et al, 2007;Aikhuele & Turan, 2016a). The M-TOPSIS method is unique for its ability to solve ranking reversals issues in TOPSIS and to evaluate failure when alternatives are symmetrical.…”

“…The model which is based on fuzzy Shannon's entropy and the Modified Technique for Order Preference by Similarity to the Ideal Solution (M-TOPSIS) model originally proposed in (Ren et al 2007;Aikhuele & Turan 2016a;b) is applied to the lean product development practices with respective to the criteria; management and leadership, financial capabilities, skills and expertise and organization culture which was initially introduced in (Shah and Ward 2003) for lean manufacturing, which the author believe could provide a guide or roadmap for product development managers on lean implementation route and in the adoption of lean practices in the product development process.…”

Application of Intuitionistic Fuzzy Topsis Model for Troubleshooting an Offshore Patrol Boat Engine

“…The intuitionistic fuzzy set (IFS) was introduced by Atanassov [16], unlike the traditional fuzzy set theory, the IFS theory is characterized by a membership function and a non-membership function. The benefits of its applications have been addressed in [17]- [20].…”

Interval-valued intuitionistic fuzzy multi-criteria model for design concept selection

“…It creates an understanding of the inherent relationship between the Relative closeness (R) value and alternative evaluation. The Modified-TOPSIS model is "described as a process of calculating the distance between the alternatives and the reference points in the D + and the D -plane which results in the construction of the R value to evaluate the quality of the alternative" (Aikhuele & Turan, 2016a;Ren et al, 2007).…”

“…To improve the results of the Modified-TOPSIS model and to avoid the bias of using a single distance measure, in this paper, an Interval-Valued Intuitionistic Fuzzy Modified TOPSIS (IVIF-Modified TOP-SIS) (Aikhuele & Turan, 2016a) and a Fuzzy Analytical Hierarchy Process (FAHP) model is proposed for design concept evaluation by using partly the algorithm originally presented by Bai (2013), where an improved score function is applied for calculating the separation measure of alternatives. The FAHP model is used to determine the criteria weight.…”