Kazuyuki SEKITANI

静岡大学工学部システム工学科で開講している授業科目「プログラムコンテスト」において,敢闘賞の決定を最短距離DEAによって行った.プログラムコンテストはグループで協調して自発的に学習に取り組むProblem Based Learningの1つであり,コンテスト形式は各グループが互いに競争して研鑽する仕掛けの1つである.競争の前提として,グループの能力が均一であることが望まれるが,グループ編成の実務上では実現困難である.どのグループも質の高いグループ活動を最後まで持続しコンテストに意欲的に参加することを期待し,2011年度から「敢闘賞」を新設した.新設の経緯と「敢闘賞」決定に用いたDEA,そしてその評価結果を報告する.

The conventional AHP supposes that several objects are completely compared by a single evaluator and hence, we can not directly apply it to an evaluation system with a lot of number of alternatives/criteria and multiple evaluators. Applying it to a personnel administration evaluation system in a section of a organization, we are faced with the problem of a large number of alternatives and multiple evaluators. There are several project teams in the section of the organization and all employees belong to each team. Because all project leaders compare some pairs of members within each team, multiple evaluators carry out a part of overall pairwise comparisons among all employees. This study develops a framework of AHP which is available to the evaluation system including multiple evaluators and a lot of alternatives or criteria. Dealing with a lot of alternatives or criteria, we does not assume complete pairwise comparisons and we permit different pairwise comparison values of the same pair by several evaluators. In order to determine priority weights of alternatives (criteria), we proposes the logarithmic least squared method with the input-data of such pairwise comparison values. We show that the proposed framework of AHP is a geometric mean method in the case where a single evaluator compares all pair of alternatives. We prove that it is a group AHP which deals with the geometric mean among multiple comparison values of the same pair as the representative pairwise comparison value. We apply the framework of AHP to a personnel evaluation in software company in order to illustrate its validity.