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Li, Lianhui,Xu, Guanying,Wang, Hongguang Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.3
Supplier evaluation is of great significance in green supply chain management. Influenced by factors such as economic globalization, sustainable development, a holistic index framework is difficult to establish in green supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty and incompleteness and the index weight is variable. To solve these problems, an index framework is established after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper is feasible and effective.
Lianhui Li 한국정보처리학회 2024 Journal of information processing systems Vol.20 No.3
Driven by the vague assessment big data, a product service system (PSS) evaluation method is developed basedon a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index valuesof PSS alternatives are solved by the integration of the stakeholders’ vague assessment comments presented inthe form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solvingof PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcomethe shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closenessof seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with thehighest RE closeness is selected as the best alternative. The results of comparison examples show that thepresented model can compensate for the shortcomings of existing traditional methods.
Lianhui Li,Guanying Xu,Hongguang Wang 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.3
Supplier evaluation is of great significance in green supply chain management. Influenced by factors such aseconomic globalization, sustainable development, a holistic index framework is difficult to establish in greensupply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertaintyand incompleteness and the index weight is variable. To solve these problems, an index framework is establishedafter comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is putforward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paperis feasible and effective.
( Jianguo Duan ),( Nan Xie ),( Lianhui Li ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4
In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system’s multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.