Abstract
Multi-choice goal programming (MCGP) has been widely used to find satisfying solutions for multiple criteria/objective decision problems in which the target value of “the more, the better,” or “the less, the better” can easily be obtained. This paper proposes two new models for representing the triangular and trapezoidal membership functions, which improve the efficacy of fuzzy MCGP (FMCGP). Two real-world applications are provided in this study to demonstrate the usefulness of the proposed models. Furthermore, the same problems are resolved by using the proposed nature-inspired optimization method (NIOM) to find the differences between them. While the artificial bee colony (ABC) algorithm is a well-known NIOM technique, studies have shown that it has an excellent performance with high-quality solutions. Thus, this study initially uses the ABC algorithm to find the differences between MCGP and ABC. Finally, some insightful information is obtained from the comparison to contribute to the NIOM and MCGP fields and their respective applications.
Highlights
Fuzzy multiple objective decision making (FMODM) has become more important for helping companies make an appropriate decision under environmental uncertainty
Chang [1]-[3] proposed a series of multi-choice goal programming (MCGP) methods that effectively solve multiaspiration level problems to contribute to the field of multiple objective decision making (MODM)
Research With the current status of available knowledge, this study is the first to investigate the relationship between MCGP and nature-inspired optimization method (NIOM), aiming with two contributions between them and further improve the usefulness of MCGP
Summary
Fuzzy multiple objective decision making (FMODM) has become more important for helping companies make an appropriate decision under environmental uncertainty. Chang [1]-[3] proposed a series of multi-choice goal programming (MCGP) methods that effectively solve multiaspiration level problems to contribute to the field of multiple objective decision making (MODM). Tabrizi et al [13] first introduced the fuzzy method to MCGP, called the FMCGP, to formulate triangular MF and enrich MCGP for qualitative decision-making issues. This paper proposes two new methods to formulate popular triangular and trapezoidal MFs. In addition, a NIOM method is provided to enrich the related fields of MCGP. To prove the novelty of the NIOM and the proposed FMCGP methods, these same methods are used to solve the same set of MODM problems with triangular and trapezoidal MFs. A comparison of the accuracy of the solutions of both methods is further provided.
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