Abstract:
Real-world optimization problems demand sophisticated algorithms. Over the years bio-
inspired approach, a subset of computational intelligence has demonstrated remarkable success in real-world
use cases, especially where exact or deterministic algorithms are ineffective. Petroleum product scheduling
is a complex optimization task belonging to the combinatorial problem category. The problem size and the
constraints compound the complexity of the petroleum product scheduling problem. However, conventional
optimization methods such as the exact or deterministic algorithm produced a poor solution quality to
the petroleum products scheduling problem. Therefore, this study leverages the potency of a bio-inspired
approach, Ant Lion Optimizer (ALO) in its basic state to enhance the solution quality. This is in line with
She-Shin Yang’s proposition, father of bio-inspired algorithms who advocated for the application of existing
bio-inspired algorithms to tackle real-world problems rather than developing new algorithms. Bio-inspired
is a computational paradigm that models the characteristics of natural biological entities to solve complex
problems. We also used the Chaotic Particle Swarm Optimization (CPSO) algorithm for the same problem to
unveil the efficacy of the roulette wheel function in ALO. The results show a 24.8% and 23.9% reduction in
the original cost of distribution on ALO and CPSO respectively. Also, 99.5% of the constraints are met. Thus,
problems of scarcity, minimum allocation and product availability are solved using the penalty constraint
handling method. The exact algorithm showed a 14% reduction in the original cost. However, despite the
effectiveness, further work on constraint handling methods and other bio-inspired computation approaches
such as Genetic algorithms and their variants could be possible in the future scope. Moreover, other real-
world problem domains such as power distribution in the power, sector could be a possible application of
the ALO.
