| Summary: | "Preface Operations research has been adapted by management science scholoars to manage realistic problems for a long time. Among these methods, mathematical programming models play a key role in optimizing a system. However, traditional mathematical programming focuses on single-objective optimization rather than multi-objective optimization as we encounter in real situation. Hence, the concept of multi-objective programming was proposed by Kuhn, Tucker and Koopmans in 1951 and since then became the main-stream of mathematical programming. Multi-objective programming (MOP) can be considered as the natural extension of single-objective programming by simultaneously optimizing multi-objectives in mathematical programming models. However, the optimization of multi-objectives triggers the issue of the Pareto solutions and complicates the derived answer. In addition, more scholars incorporate the concepts of fuzzy sets and evolutionary algorithms to multi-objective programming models and enrich the field of multi-objective decision making (MODM). The content of this book is divided into two parts: methodologies and applications. In the first part, we introduced most popular methods which are used to calculate the solution of MOP in the field of MODM. Furthermore, we included three new topics of MODM: multi-objective evolutionary algorithms (MOEA), expanding De Novo programming to changeable spaces, including decision space and objective space, and network data envelopment analysis (NDEA) in this book. In the application part, we proposed different kind of practical applications in MODM. These applications can provide readers the insights for better understanding the MODM with depth. "--
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