# A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs
## 1 Introduction  
Achieving the goal of developing a product with all-embracing design features starts from brainstorming activities in the design phase of the product when several conceptual design concepts have been established. An important task at this stage is decision making on identification of the optimal conceptual design. Decision making in the preliminary design phase and extensive design concept selection from several conceptual designs can be accrued to the robust design of a product [1, 2]. The number of design features that are embedded in the optimal design concept is also important because they depict the multifarious functions that the product can perform. A good way to develop a product with several design features is to examine the features of different conceptual designs during the concept selection phase. Selecting an optimal design implies that the design has a satisfactory performance considering all the design features [3, 4]. Also, an optimal design can be developed so that the design features from other conceptual designs can be added to the design. This makes the decision process important, and the efforts put into it cannot be overemphasized. Design engineers provide several design solutions in the developmental stage before a detailed analysis is carried out [5]. Provision of several design solutions is necessary because the management of a manufacturing firm wants to reduce the cost of fabrication and produce an extensive product that will have a high demand in a competitive market and extended useful life. Also, the firm may be interested in selecting a design that is realistic in terms of completion time and utilization of existingtechnologies of fabrication. In essence, selecting an optimal design concept from a set of alternative designs becomes inevitable considering the fact that all the design solutions have several benefits and shortcomings [2]. Research has shown that an excellent way to arrive at an optimal solution in the decision-making process in this scenario is to introduce the Multi-Criteria Decision Model (MCDM) [6; 7]. In the preliminary phase of an equipment or a product, the design features and sub-features are identified alongside various design alternatives in order to allow for decision making on the optimal design concept to be modelled as an MCDM. Basically, MCDMs can be broadly divided into two categories, which are the Multi-Attribute Decision Model (MADM) and Multi-Objective Decision Model (MODM) [8; 9]. The MADM is applicable in cases that involves making a choice from a set of alternatives in a discrete or well-defined solution space. The MODM is applied to solve decision problems with several goals where there are no discrete sets of explicitly defined alternatives. Also, the MODM also applies to scenarios where the alternatives are to be ranked based on several criteria. In this case, the decision process is performed at different times in order to satisfy the various objectives of the decision criteria [9; 10]. Several MADMs have been introduced to solve real-life decision-making problems, but there is a need to investigate the suitability of these models in the design process. Among the MADMs used in decision-making processes are the Multi-Attribute Utility Theories (MAUTs). MAUTs include the Analytic Hierarchy Process (AHP), Weighted Decision Matrix (WDM), Analytic Network Process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIseKrierijumska Optimizacija I Kompromisno Resenje (VIKOR) and Elimination and Choice Translating Reality (ELECTRE), among others [11]. Several efforts have been made by researchers to apply these MADMs in the selection of an optimal design from a set of alternative conceptual designs. Considering the fact that the design features that are usually applied as criteria in the decision process are different dimensions and units, researches have introduced the theory of fuzzy membership functions and rough numbers into the MADMs.