Multi-objective Evolutionary computation methods were used extensively in the past and are being used in the present for the solution of different functional areas of supply chain optimization problems. A considerable number of significant developments in the methods of Evolutionary Multi-objective Optimization (EMO) and applications are reported for a wide range of relevant optimization problems in the areas of engineering, medical science, scientific field, aero space, and science and technology. This paper presents a brief literature review of the impact of the fast-growing EMO field of research. After analyzing, the paper concludes with a discussion of some of the most promising research trends in the years to come in the area of supply chain management.

Now the Evolutionary algorithms have become an increasingly popular design and optimization tool in the past few years. In the mid-1980s, these algorithms started to reach the research communities with a constantly growing development of new algorithms and applications (Thomas et al., 1997). One of the most important aims of multi-objective optimization is to assist decision-makers to make a satisfactory decision taking into account the balance among several conflicting objectives. One of them is the use of evolutionary algorithms to solve multi-objective optimization problems. The growing importance of this field is reflected by a significant increment of technical papers in international conferences, journals and books in the fields.

In this paper, we intend to give a detailed literature in the field of evolutionary multi-objective combinatorial optimization in the last 20 years. One optimization area of particular business interest is the functional areas of supply chain management. An excellent comprehensive review of relevant applications can be found in Dimopoulos and Zalzala (2000); and Carlos (2006). The aim of this paper is to review the existing methods to solve multi-objective problems, and evolutionary multi-objective applications for the solution of multi-objective production and operations optimization problems.

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