Artificial intelligence‐enabled smart city management using multi‐objective optimization strategies

Abstract

AbstractThis article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.