| Summary: | A group health insurance plan is an insurance plan that provides healthcare coverage to a selected group of people. In various countries, group health insurance plans are one of the major benefits offered through employers in the private sector. In recent years, the numbers of group health insurance plans offered in the market of health insurance have been increasing rapidly. This is due to compulsory government policies, which are imposed on employers in the private sector leading to an increasing demand for this insurance plan. Accordingly, employers may face a wide variety of available group health insurance plan alternatives. Despite the fact that employers in the private sector have a crucial and significant role in the health insurance market all over the world, little is known about how employers evaluate and choose group health insurance plans to cover their employees against the payments of benefits as a result of sickness or injury. Therefore, the primary concern in this research is to propose a model to assist employers within the private sector to evaluate alternative group health insurance plans and to select the most appropriate, in order to provide the perfect health care environment for their employees. In this research, a new hybrid Fuzzy Multiple Criteria Decision Making (MCDM) model is proposed for the selection problem. The proposed model tackles some issues that may be associated with the selection of the group health insurance plan, such as modelling uncertainty, studying the dependence among decision attributes, deriving decision attributes importance weights and ranking various alternatives. In the proposed hybrid model, four extension approaches based on the Fuzzy Delphi, Fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL), Fuzzy Group Prioritisation and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are developed. Unlike the existing methods, the four proposed approaches, a new extended Fuzzy Delphi (FDE) method, a new extended Fuzzy DEMATEL method, a new Fuzzy Group Prioritisation (FGP) method and a new extended Fuzzy TOPSIS method, consider the importance weight of each member in group decision making since the selection problem needs evaluations from decision makers (DMs) with different levels of expertise and different perceptions. In the literature, there is some work on these methods, but to our knowledge, no research exists that combines these four methods. Moreover, the proposed model might be applied, due to its novelty, to any MCDM problem uncertainty in different. Furthermore, four new prototype decision support tools, termed Fuzzy Delphi Solver, Fuzzy DEMATEL Solver, Fuzzy Group Prioritisation Solver and Fuzzy TOPSIS Solver were developed in this study, based on the concepts of the four proposed approaches, in order to provide user-friendly interfaces for facilitating the application of these approaches. MATLAB software Version R2013a was adopted as a development environment for prototyping these new decision support tools in this study. The tools developed were validated internally by using hypothetical examples and checking the correctness of the results obtained by comparing them to other results generated from other software, such as Microsoft Excel or LINGO V13.0 software. In addition, a practical validation of the proposed hybrid Fuzzy MCDM model was investigated through conducting a case study of the Saudi health insurance industry. The main objectives of the case study were: 1) investigation of the evaluation process of selecting a group health insurance plan, including identifying the selection criteria and alternatives, studying the dependency issue, deriving the criteria weights, and ranking available alternatives; 2) application of the new decision support tools developed. In this case study, a group of nine DMs, Human Resources (HR) managers at nine different private companies in Saudi Arabia, were selected to take part of this case study. Their involvement achieved the first objective of the case study. At the end of the case study, a sensitivity analysis was conducted to indicate the robustness and the reliability of the results obtained. It is concluded that the proposed model is indeed beneficial. Finally, areas for further research were identified.
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