Bernard Roy (Roy, 1968). Evolutionary multiobjective optimization school (EMO) EMO algorithms start with an initial population, and update it by using processes designed to mimic natural survival-of-the-fittest principles and genetic variation operators to improve the average population from one generation to the next. The goal is to converge to a population of solutions which represent the nondominated set (Schaffer, 1984; Srinivas and Deb, 1994). More recently, there are efforts to incorporate preference information into the solution process of EMO algorithms (see Deb and Köksalan, 2010). Grey system theory based methods In the 1980s, Deng Julong proposed Grey System Theory (GST) and its first multiple-attribute decision-making model, called Deng's Grey relational analysis (GRA) model. Later, the grey systems scholars proposed many GST based methods like Liu Sifeng's Absolute GRA model, Grey Target Decision Making (GTDM) and Grey Absolute Decision Analysis (GADA). Analytic hierarchy process (AHP) The AHP first decomposes the decision problem into a hierarchy of subproblems. Then the decision-maker evaluates the relative importance of its various elements by pairwise comparisons. The AHP converts these evaluations to numerical values (weights or priorities), which are used to calculate a score for each alternative (Saaty, 1980). A consistency index measures the extent to which the decision-maker has been consistent in her responses. AHP is one of the more controversial techniques listed here, with some researchers in the MCDA community believing it to be flawed. The underlying mathematics is also more complicated and requires rational analysis, though it has gained some popularity as a result of commercially available software. Several papers reviewed the application of MCDM techniques in various disciplines such as fuzzy MCDM, classic MCDM, sustainable and renewable energy, VIKOR technique, transportation systems, service quality, TOPSIS method, energy management problems, e-learning, tourism and hospitality, SWARA and WASPAS methods. MCDM methods The following MCDM methods are available, many of which are implemented by specialized decision-making software: Aggregated Indices Randomization Method (AIRM) Analytic hierarchy process (AHP) Analytic network process (ANP) Balance Beam process Best worst method (BWM) Brown–Gibson model Characteristic Objects METhod (COMET) Choosing By Advantages (CBA) Conjoint Value Hierarchy (CVA) Data envelopment analysis Decision EXpert (DEX) Disaggregation – Aggregation Approaches (UTA*, UTAII, UTADIS) Rough set (Rough set approach) Dominance-based rough set approach (DRSA) ELECTRE (Outranking) Evaluation Based on Distance from Average Solution (EDAS) Evidential reasoning approach (ER) Goal programming (GP) Grey relational analysis (GRA) Inner product of vectors (IPV) Measuring Attractiveness by a categorical