Handling Uncertain Information with Fuzzy Logic in Locating New Infrastructure

Abstract

Considerable research has been conducted in location optimization for new infrastructures such as highways and rail lines, as well as fixed facilities such as sports arenas, warehouses, and airports. Uncertainty plays a crucial role in determining the final location of such facilities. For example, there may be uncertainties about land-use and site characteristics as well as about demand for the new facilities. There may be disagreements among various stakeholders that complicate reaching a consensus due to budgetary constraints and differing political views. While some uncertainties can be quantitatively represented, others can only be represented qualitatively (e.g., low, medium, or high). Not all of these uncertainties can be precisely and mathematically modeled. While deep learning and other probabilistic techniques have been developed to deal with uncertainties that can be represented numerically, fuzzy logic has been recognized as a preferred choice for handling qualitative uncertainties. This paper identifies situations with uncertainties in locating new infrastructure and offers solutions for handling the qualitative uncertainties with fuzzy logic. An example is presented to illustrate the approach to handling uncertainties that can only be represented qualitatively. The results are promising for future research dealing with uncertainties represented as linguistic variables, thereby improving the decision-making process. The method can be applied in other domains involving uncertainty.

Received: 24 December 2024 | Revised: 16 June 2025 | Accepted: 30 June 2025

Conflicts of Interest

Manoj K. Jha is an Editorial Board Member for Journal of Data Science and Intelligent Systems and was not involved in the editorial review or the decision to publish this article. The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Manoj K. Jha: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.