This analysis demonstrates optimization methods in transport solutions, highlighting challenges in data collection and privacy.
This work introduces a new method for transportation optimisation decision-making that utilizes LINGO software and fuzzy logic-powered optimisation. The main aim is to minimize variance in accounting for expenses. A sophisticated three-stage stratified random sampling procedure supported by randomised response mechanisms is utilized to achieve this. It primarily contributes a framework through which policymakers can make significant enhancement in the method of collecting data, especially for such cases in which privacy among respondents is very critical. It addresses challenges that face data collection involving sensitive issues and remains within data economy as well as integrity by bringing in fuzzy logic seamlessly in cooperation with randomized response technique.
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Ganie et al. (2025) studied this question.
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