Research On Evaluating Logistics System Of Manufacturer Under The MC Environment Based On Fuzzy Information

The arrival of the economic globalization induces an ever more competitive environment in which manufacturers operate. In order to occupy favorable position in fierce competitive battle, organizations must provide high-quality products to meet customer’s requirements, therefore, it is imperative to transform from mass production to mass customization(MC). Mass customization refers to the production mode that produces varied and often individually customized products and services with the efficiency of mass production, without any increase in the total cost. No doubt MC is an ideal mode that the manufacturing industry will pursue far in21century. The using of reasonable logistic system can help manufacturers to reduce costs and increase profits, and at the same time ensure that speed products to customers, therefore, it is an important group decision making problem to choose the suitable logistics system for the companies which are ready for implementing MC.In reality, the inherent vagueness or uncertainty in the process of evaluating logistics system under the environment of MC presents a special challenge to the effective selection of the suitable system. The uncertainty and vagueness are due to a number of reasons:the data available for evaluating is often limited, especially when a company first implements MC in the industry; the obtained information was from a different source; the information was presented by various ways and the evaluating process involves various inputs in the form of human judgment. In the traditional evaluation method, most of the input variables were assumed to be precise and were treated as crisp numerical data, as a matter of fact, it is too difficult to quantify all the indices, and how to make full use of limited information and the decision makers’experience is the core of this study, the major contents of the dissertation are summarized as follows:(1)In order to deal with the uncertain multiple attribute group decision making problems of logistics system key indices analysis in MC, several analysis methods based on uncertain and vague information were put forward, which can be applied in different scale manufacturers. For reflecting the vagueness and uncertainty of information environment and avoiding the loss of the judgment information to a greater degree, the linguistic decision-making theory was introduced to determine the attributes weights of logistics system in MC. In small and medium-sized businesses, the differences of experts’cultural level and work experience were lower, they can use the same linguistic variable set to express their preference values, while the experts in large and medium enterprises have large differences, they usually express their judgments using different linguistic term sets. Then, based on the group linguistic information and complex linguistic information, two approaches were presented to determine the weights of key attributes. Experts in multinational companies or conglomerates express their preferences in their preferred or familiar formats, depending on their cultural background, language and value systems. Although the free expressions of preferences make the problem complicated, it is obviously that they make it more realistic, therefore, a multi-format information-based approach of determining the weights of key attributes was proposed.(2)In order to reflect the complexity of logistics systems under the environment of MC in an effective way, a group linguistic information-based approach of evaluating logistics systems was proposed. In this approach, firstly, decision-makers used linguistic variables to express their preference values of the logistics system alternatives, and a transformation function was used to change the variables into triangular fuzzy numbers; secondly, a generalized induced ordered weighted operator was introduced to integrate the given preference values with the weights of key attributes and decision-makers, and then the comprehensive values of each alternative were obtained; finally, the suitable alternative could be chosen based on the priority of comprehensive values.(3)According to studying the uncertainty and complexity of logistics systems in MC, the team in charge of implement MC mode often gave orient expectation of some logistics system indices, a methodology based on axiomatic design was proposed considering index expectation. In this methodology, firstly, based on the degree which each logistics system alternative’s preference values with respect to the orient expectation, the evaluation matrix of expectation accordance degree was obtained; secondly, estimators checked the degree to meet expectations of each alternative, and then calculated the comprehensive information content by using axiomatic design; finally, on the basis of the order of the comprehensive information, managers can judge which logistics system has more advantage than the other systems.(4)For reflecting vagueness of logistics systems under the environment of MC in an effective way, estimators tend to give evaluation values of the same logistics system alternative about their personal preferences in many different ways, a multi-format information-based analysis approach of system selection was proposed. Firstly, decision-makers express their preferences of the relative importance weights of logistics system alternatives in their preferred formats; secondly, some transformation functions were used to unify the multi-format information into complementary judgment matrix, and priorities of complementary judgment matrix were resolved by using normalizing rank aggregation method, then the group evaluation matrix was determined by aggregating the weights of decision-makers; thirdly, the comprehensive values of each alternative were obtained by integrated with the weights of key attributes; Finally, on the basis of ranking the comprehensive values, the suitable scheme was acquired.Finally, some real-world logistics system selection cases were presented to demonstrate the feasibility and validity of the proposed approach. In theory, the uncertain information-based methods of evaluating logistics system under the environment of MC, to a great extent, improves the flexibility of system, and enriches the system evaluation theory. In practical, when manufacturers use the logistics system selection methods based on uncertain information, the applicability of the logistics system can be enhanced, more customer satisfaction can be achieved, and resource allocation can be optimized.