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Quality and reliability engineering technique are widely used as improvement tools in manufacturing industries, involving the design experiments, quality function deployment (QFD), and survival analysis, focuses on improving the lifecycle of products. However, started at early last decade, some researches in improving the service lifecycle in some service industry consider to implements design of experiment in service industry, followed by other tools such as QFD, and the reliability engineering tools. This research proposed alternatives incorporated quality and reliability engineering methods in evaluating and improving the lifecycle of service. Starting with evaluating the weaknesses of service using SERVQUAL method, finding the improvement plan by implementing QFD, designing the robust service design using Taguchi designed experiment methods, and then implementing survival analysis for ensuring the reliability of designed services. This framework has successfully implemented in designing and evaluating existing airport service system with some recommendation generated in.

The quality of a decision is influenced by the level of expertise of the Decision Makers (DMs). In Group Decision Making, alternatives’ scores are obtained by integrating the DMs opinions and the importance weights of the DMs greatly affect the resulted value. Expertise level is defined as the ability to differentiate consistently and expressed as the CWS-Index, a ratio between the Discrimination and Inconsistency. The DMs give their evaluations in pairwise comparison of Fuzzy Preference Relations (FPR) and the additivity property of FPR generates the estimators needed to get the CWS-Indexes and the expertise-based ranking of DMs. The weights of the DMs are obtained by using Induced Ordered Weighted Averaging (IOWA) operator and Basic Unit Monotonic Increasing functions and the resulted weights are used to evaluate the available alternatives to get the best one based on Fuzzy Majority and IOWA operators. This paper proposes an expertise-based weight allocation method for DMs and a numerical example is discussed to illustrate this expertise-based model to get the best alternative and it is concluded that the higher the DMs’ expertise level, the higher his/her weight would be, and these weights affect the alternatives’ score and the rank of the alternatives.