1 Professor, Department Management Studies, Nehru Institute of Engineering and Technology, Coimbatore, India. Email: nietdrshanthi@nehrucolleges.com
2 Assistant Professor, Department Management Studies, Nehru Institute of Engineering and Technology, Coimbatore, India. Email: janani199205@gmail.com
3 Assistant Professor, Artificial Intelligence and Machine Learning, SEA College of Engineering and Technology, KR PURAM, Bangalore, India. Email: dhanalekshmiyadav@gmail.com
4 Assistant Professor, Department of Computer Science and Engineering, Ahalia School of Engineering and Technology, Palakkad, India. Email: kavitha.s@ahalia.ac.in
5 Associate Professor, Akshaya Institute of Management Studies, Coimbatore, India. Email: us.senthilkumar@yahoo.com
6 Assistant Professor, Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India. Email: aharini1608@gmail.com
Received: 20th Feb, 2026 | Revised: 4th Mar, 2026 | Accepted: 25th Mar, 2026 | Available Online: 10th Apr, 2026
Carbon dioxide (CO₂) emission from freight transportation constitutes a major driver of urban air pollution and climate change. This paper introduces a novel application of the Whale Optimization Algorithm (WOA) to address the Environmental Vehicle Routing Problem (EVRP), wherein the primary objective is minimizing vehicular CO₂ emissions while simultaneously satisfying customer demand and vehicle capacity constraints. WOA can replicate humpback whale hunting methods and circling prey behavior, which allows it to achieve a beneficial balance between global and local exploration and exploitation. A modified WOA is proposed incorporating a greedy permutation-based solution encoding, an adaptive penalty mechanism for constraint handling, and a local search operator derived from 2-opt improvement. Computational experiments across six benchmark EVRP instances with 10 to 100 customers demonstrate that WOA achieves an average CO₂ reduction of 8.6% compared to the Artificial Bee Colony (ABC) algorithm, 16.2% compared to Genetic Algorithm (GA), and 12.4% compared to Particle Swarm Optimization (PSO). The Wilcoxon signed-rank test is used to test statistical significance. The suggested strategy offers practical decision-making information to the green fleet management in distribution networks of cities.
Keywords: Whale Optimization Algorithm; Environmental Vehicle Routing Problem; Green Logistics; CO₂ Emission; Swarm Intelligence; Metaheuristics.
How to cite this article: Shanthi S, Janani M, Dhana Lekshmi MC, Kavitha S, Senthilkumar US, Harini A. Whale Optimization Algorithm for Solving Environmental Vehicle Routing Problem in Pharmaceutical Green Logistics for Drug Delivery. Int J Drug Deliv Technol. 2026;16(29s):93-100. DOI: 10.25258/ijddt.16.29s.12
Source of support: Nil.
Conflict of interest: The authors declare no conflict of interest.