The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America

Abstract

This study aims to help managers develop a proper strategy and policy for their company’s future. After the global COVID-19 pandemic, developed countries decided to change their production and relocate and re-industrialize. The U.S.’s big electronics and automobile companies are not an exception to this rule. The lithium batteries are the main instrument of mobile phone and electric vehicles. The leading lithium battery supplier for the U.S mobile phone companies is China. Argentina, Bolivia, and Chile (in South America) have some of the largest lithium mines in the world; these countries are known as the lithium triangle. Among the 86 million tonnes of lithium resources worldwide, 49.9 million tonnes exist in this area. The researchers in this study surveyed the best country for constructing a battery for companies in the U.S. Because of the growth of electric vehicles and their use of the lithium battery, the world is facing astronomical prices for lithium. To emphasize this issue and help managers create good policy, this study combined multiple methods. The improved suitability-feasibility-acceptability (SFA) strategy is integrated with the Bayesian best-worst method (BBWM) and measurement of alternatives and rankings according to compromise solution (MARCOS) multicriteria methods to determine the best destination. For comparison, based on the SFA strategy, seven criteria are introduced: commercially viable reserves, national minimum wage, corporate income tax, accessibility to mining companies, accessibility to the waterway, population, and political stability index. The Bayesian BWM analysis reveals that the foremost factor is corporate income tax, whereas MARCOS’s findings indicate that Chile is the best country to construct the lithium battery industry. To verify the proposed approach, a comparison analysis also is performed.

Investigating the energy security performance, productivity, and economic growth for the EU

“…This approach contains three beginning points that aid in making sound decisions: reference points, relationships between alternatives, and the utility of each alternative. 45 The CODAS technique introduced by Ghorabaee et al 46 computes the Euclidean and taxicab distances as the primary and secondary measures in order to identify the best choice. 47,48 The Euclidean distance (ED) is used to select the options, while the taxicab distance (TD) is utilized to compare two alternatives when their EDs are quite close.…”

“…The MARCOS method put forth by Stević et al 44 is an MCDM approach appropriate for solving models with more criteria. This approach contains three beginning points that aid in making sound decisions: reference points, relationships between alternatives, and the utility of each alternative 45 . The CODAS technique introduced by Ghorabaee et al 46 computes the Euclidean and taxicab distances as the primary and secondary measures in order to identify the best choice 47,48 .…”

Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology

“…The method determines the utility functions of the options with the help of ideal and non-ideal solutions. Scholars have successfully employed MARCOS in various areas [82][83][84]. The following steps are portrayed in IF-MARCOS for making decisions [9].…”

Machine Learning-Driven Approach for Large Scale Decision Making with the Analytic Hierarchy Process

“…The AHP described later in Section 2.1 has been applied in different areas such as the polymer extrusion process [18], sustainable supplier selection [9], engineering [5,19], operational risk in power substations [20], online shopping [10], plant location [21], undergraduate elective course planning [22], the management of architectural heritage in smart cities [23], supplier selection in the automotive industry [24], universities rankings [13], and others [2,5,25,26]. However, this method suffers from a notable drawback: it requires n × (n − 1)/2 comparisons of n alternatives for each criterion to solve the decision problem [11].…”

“…The methods from MADM, the focus of this study, can be separated into (i) aggregation methods, whose main representatives are the MAUT, such as Analytic Hierarchy Process (AHP) [4] and Analytic Network Process (ANP) [34], (ii) outranking methods, such as the Preference Ranking Organization Method of Enrichment Evaluations (PROMETHEE) [35] and Élimination et Choix Traduisant la Realité (ELECTRE) [36] and their derivatives, and (iii) interactive methods, such as Multi-Objective Linear Programming (MOLP) [8,37]. For a more complete overview of optimization and MCDM, see [9,12,[19][20][21]26,27,38,39].…”