A packaging industry optimization based on three metaheuristics methods

Abstract

Packaging is one of the most important elements in the value chain of transportation and logistics requirements who is frequently overlooked. It has evolved from a basic cardboard box to a complicated, coordinated system that ensures items travel securely and affordably across the supply chain, and to assure that it need to be optimized using metaheuristics that solves complex issues of minimization or maximizing of a function in order to obtain nearly optimal solutions the fastest way. There are many metaheuristics, but in this research, we will only discuss three optimization algorithms that can help us reduce the cost of packaging in a company by programming them with MATLAB software. The first algorithm is the best-known particle swarm optimization in the optimization field, which is inspired by the simulation movement of a flock of birds. The second algorithm is simulated annealing, which is inspired by annealing in metallurgy, a heat treatment technique that affects both temperature and energy. Last but not least, there's the genetic algorithm, which relies on bio-inspired operators like mutation, crossover, and selection to produce high-quality outcomes for optimization issues. We'll use the test functions to compare their performance in terms of uptime and convergence, and then apply it to our industrial optimization problem.