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This study aims to develop a valid and reliable Islamic financial literacy (IFL) scale that can capture all the segments of the Islamic financial sectors and which could be considered applicable for all jurisdictions across the globe.

To build the measure, this study followed a scale development process by collecting 698 a priori items from 81 respondents. Later, it generated an item pool through the analysis of the items with experts and gave the last form (40 items) to 287 respondents in Turkey with another IFL scale that is frequently used in the literature and a scale assessing religiosity. With explanatory factor analysis, the scale demonstrates a four-factor construct with 20 items. This construct provides good fit indexes and reliability scores.

Results of the correlation analysis and comparison of the fit indexes of alternative structures provided supportive evidence for discriminant and convergent validity of the scale and its sub-dimensions. As a result, an applicable scale is developed for countries where Islamic financial institutions are operating and where they are not.

One of the strengths of this study is that it represents a comprehensive scale development for the entire Islamic financial system, including banking, takāful (Islamic insurance) and fund management. In addition, the attempt to design an IFL scale applicable to any economy or individual is a pioneering attempt in the literature.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.