Advancements in optimization techniques for industrial engineering: a comparative study of emerging metaheuristic algorithms and fuzzy-based decision-making methods

Abstract

The modern world is a competitive one. Any organization that wants to succeed must keep up with the changing demands of the global marketplace. The success factors for any organization are hence ongoing quality improvement and wise decision-making. The most effective use of time and available resources are the other key elements that greatly influence an organization's performance. Nowadays companies today face new challenges in discovering strategies and even techniques that will enable top management to identify and select from the market those assets that offer the ideal combination between the acquisition cost and economic performance. This is because businesses operate in highly competitive environments where it can be difficult to survive on the market. In manufacturing industry, Non-traditional machining (NTM) techniques are more recent production methods that emerged as a result of the fast invention of new materials that are challenging to cut and the rising demand for producing intricate part shapes. For each of these NTM processes, there are different adjustable parameters. The combination of the several input parameters of these NTM methods determines how effective they are. But because there are so many control parameters available and there are so many contradicting responses, it can be challenging to select the ideal set of input parameters for a given NTM process. Thus, in this research work, we have taken two optimization problems from the areas of manufacturing and financial supply chain management and applied two different optimization techniques to solve them. In first part we used five foraging behavior-based metaheuristic multi-objective optimization techniques for parametric optimization of the NTM processes and applied a statistical nonparametric test (Friedman test) to find the best technique. In the second part of this research proposes a fuzzy logic managerial decision tool for asset acquisition. Here, with the help of four decision-makers we applied three fuzzy logics along with the fuzzy-TOPSIS approach to choose the optimum investment strategy while taking into account their tacit knowledge. The proposed fuzzy logic managerial decision tools produced excellent results coupled with strong economic performance, in order to assist investment choices made by an investment firm on global markets. This is done from the point of view of industrial engineering in order to help society and, mostly the mankind as a whole.