The Different Ways of Using Utility Function with Multi-choice Goal Programming
By studying the way a previous study is using utility function with multi-choice goal programming (MCGP), some drawbacks are identified. These drawbacks can mistakenly result in an incomplete representativeness of the original MCGP with utility functions
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Abstract By studying the way a previous study is using utility function with multichoice goal programming (MCGP), some drawbacks are identified. These drawbacks can mistakenly result in an incomplete representativeness of the original MCGP with utility functions model and thus lead to the inability of the model to appropriately assess and express the real preference structure of a decision maker (DM). This study recommends some points and proposes another ways of using utility functions with MCGP. These new ways are validated by using them to express different DM preference structures pertaining to a goal, which underlies his/her real oral statements during decision making. Keywords Decision maker statements · Decision making · Goal programming Multi-choice goal programming · Preference structure · Utility function
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1 Introduction Decision-making via goal programming (GP) is ubiquitous nowadays. As a good tool for solving multi-criteria decision-making (MCDM) problems with multiple objectives, GP has gained its wide popularity [1]. It takes into account multi-criteria and multi-objective concerns of multiple goals, and is based on mathematical programming, which is quite different from other algorithm-based decision approaches Z.-Y. Zhuang (B) School of Computer and Computing Science, City College, Zhejiang University, Hangzhou 310015, China e-mail: [email protected] C.-T. Chang The Graduate Institute of Buisness Management, Chang Gung University, 259 Webhua 1st Rd.,Taoyuan 333, Taiwan e-mail: [email protected] G.-C. Yang et al. (eds.), Transactions on Engineering Technologies, Lecture Notes in Electrical Engineering 275, DOI: 10.1007/978-94-007-7684-5_28, © Springer Science+Business Media Dordrecht 2014
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[2, 3]. It is still popular now and continues to be irrigated by researchers and practitioners [4]. As a special extension of linear programming, GP was first introduced by Charnes and Cooper [5]. Since then, important extensions and numerous applications have been proposed [6]. The literature is abundant with applications of GP. Over the decades, GP has been used to support real-world decision-making processes in many fields such as communication, energy, manufacturing, medical healthcare, vendor selection, pricing, and so on [7–13]. The literature is also abundant with GP variants (i.e., the GP extension models or specific-purposed formulations for GP). The extension models include, for example, weighted GP (WGP) [14], interactive GP [15], integer GP instead of continuous GP, interval GP (IGP), fuzzy GP (FGP) [16], multi-choice GP (MCGP) [17], multisegment GP (MSGP) [18], percentage GP (%GP) [19], etc. There are studies about the reformulations of either the objective measurement or the goal constraints, too. They are the value-added components of GP that can enhance the solving range and widen the use of GP in different application scenarios. This category includes, but not limited to (for space reasons we just list some recent works), formulating arbitrary
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