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

Authors:
Pinki Gulia at Lovely Professional University
Pinki Gulia
Rakesh Kumar at Lovely Professional University
Rakesh Kumar
Vimal Shanmuganathan at Sri Eshwar College of Engineering
Vimal Shanmuganathan
Norah Saleh Alghamdi at Princess Nourah bint Abdulrahman University
Norah Saleh Alghamdi
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Abstract and Figures

This 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.
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ORIGINAL ARTICLE
Artificial intelligence-enabled smart city management using
multi-objective optimization strategies
Pinki
1
| Rakesh Kumar
1
| S. Vimal
2
| Norah Saleh Alghamdi
3
|
Gaurav Dhiman
3,4,5,6,7,8
| Subbulakshmi Pasupathi
9
| Aarna Sood
10
|
Wattana Viriyasitavat
11
| Assadaporn Sapsomboon
11
| Amandeep Kaur
12,13
1
Department of Mathematics, Lovely
Professional University, Phagwara, Punjab,
India
2
Department of Artificial Intelligence and Data
Science, Ramco Institute of Technology,
Rajapalayam, India
3
Department of Computer Sciences, College of
Computer and Information Sciences, Princess
Nourah bint Abdulrahman University, Riyadh,
Saudi Arabia
4
Department of Electrical and Computer
Engineering, Lebanese American University,
Byblos, Lebanon
5
Centre of Research Impact and Outcome,
Chitkara University, Rajpura, Punjab, India
6
Department of Computer Science and
Engineering, Graphic Era Deemed to be
University, Dehradun, India
7
Division of Research and Development,
Lovely Professional University, Phagwara,
India
8
MEU Research Unit, Middle East University,
Amman, Jordan
9
School of Computer Science and Engineering,
VIT Chennai, Chennai, India
10
Sanskriti School, Chankyapuri, India
11
Chulalongkorn Business School, Faculty of
Commerce and Accountancy, Chulalongkorn
University, Bangkok, Thailand
12
Chitkara Centre for Research and
Development, Chitkara University, Himachal
Pradesh, India
13
Department of Computer Science and
Engineering, University Centre for Research
and Development, Chandigarh University,
Gharuan, Mohali, India
Correspondence
Gaurav Dhiman, Department of Electrical and
Computer Engineering, Lebanese American
University, Byblos, Lebanon.
Email: gdhiman0001@gmail.com;
gauravdhiman.cse@geu.ac.in
Abstract
This article outlines an integrated strategy that combines fuzzy multi-objective pro-
gramming and a multi-criteria decision-making framework to achieve a number of
transportation system management-related objectives. To rank fleet cars using vari-
ous criteria enhancement, the Fuzzy technique for order of preference by resem-
blance 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 opti-
misation (MOO) under uncertainty has gained traction in recent years. Recently,
fuzzy set theory has been used to solve challenges in multi-objective linear program-
ming. 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 hypotheti-
cal 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 Func-
tions in fuzzy goal programming operation on the selected vehicles. The results pro-
vide valuable insights into the strengths and limitations of each technique, facilitating
informed decision-making in real-world optimization scenarios.
KEYWORDS
fuzzy set theory, intelligent transportation system, multi-objective linear programming, smart
city, TOPSIS
Received: 8 November 2023 Revised: 24 January 2024 Accepted: 26 February 2024
DOI: 10.1111/exsy.13574
Expert Systems. 2025;42:e13574. wileyonlinelibrary.com/journal/exsy © 2024 John Wiley & Sons Ltd. 1of30
https://doi.org/10.1111/exsy.13574
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