**1. Introduction**

Multi-criteria decision-making (MCDM) methods are classified into multiattribute decision-making and multi-objective decision-making in which the former determines the optimal alternatives and the latter finds the optimal alternatives that optimize the objective. The MCDM methods comprise a sequence of steps to derive the optimal solution to the decision-making problem. DM is the system of choosing the best alternative satisfying all the criteria to a great extent with the expert's assist, but the crucial thing is finding the criterion weight. At some circumstances, the criterion weights are assumed to be equal but it is not so in all the cases. The criterion weight states the significance of criteria and henceforth, the calculation of criterion weight is very essential. There are many methods to find the criterion weights such as analytic hierarchical process (AHP), analytic network process (ANP), best worst method (BWM), full consistency method (FUCOM), and stepwise weight assessment ratio analysis (SWARA). The method of SWARA appears to be simple and flexible in comparison with other methods of determining the criterion weight based on human expertise and it has several applications in prioritizing sustainability indicators of energy systems [1]. The method of TOPSIS (the technique for order of preference by similarity to ideal solution) is commonly used to rank the alternatives as it yields the best results in comparison with other methods and it has been discussed in a fuzzy environment by Neelima et al. [2] and Ansari et al. [3]. Babak et al. [4] and Houssine et al. [5] discussed TOPSIS under intuitionistic fuzzy and neutrosophic [6] environments.

The method of SWARA was used in combination with crisp COPRAS [7], fuzzy COPRAS [8], crisp VIKOR [9, 10], neutrosophic VIKOR [11], WASPAS [12], Delphi [13], ARAS, GRA [14], TOPSIS in different decision-making setting. The integrated approach of SWARA-TOPSIS was inferred to yield better results based on the study on its applications in supplier selection [15], reducing ecological risk factors [16], prioritizing the failures in a solar panel system. This integrated approach was discussed in the environments of fuzzy [17], intuitionistic, and neutrosophic [18, 19]. Ahmet et al. [20], Miranda et al. [21], and Nazanin et al. [22] discussed different data normalization techniques. To give a comprehensive picture of representing the expert's opinion, this integrated approach is discussed under plithogenic environment in this paper, which is not explored so far to the best of the knowledge. At recent times, researchers develop novel plithogenic MCDM methods. In these plithogenic decision-making models, the plithogenic operators together with the contradiction degree are used to find the aggregate opinion of the experts regarding the criterion satisfaction rate of the alternatives.

In this research work, plithogenic SWARA-TOPSIS is developed by applying plithogenic intersection operator to the expert's opinion on the initial decisionmaking matrix. The efficiency of different normalization techniques of the weighted matrices is determined by applying them to two different cases. The first case is plithogenic TOPSIS with equal criterion weight, and the second is plithogenic SWAR-TOPSIS. The comparison of both the cases will certainly unveil the efficiency of the proposed approach. The remaining content is segmented as follows, Section 2 presents the methodology; the section consists of the application of the proposed method to the decision-making of food processing technology; Section 4 discusses the result and the last section concludes the work (**Table 1**).


**Table 1.** *List of acronyms.* *Plithogenic SWARA-TOPSIS Decision Making on Food Processing Methods with Different… DOI: http://dx.doi.org/10.5772/intechopen.100548*
