  Sustainable development has emerged as a global priority, and industries are
increasingly striving to align their operations with sustainable practices.
Parallel machine scheduling (PMS) is a critical aspect of production planning
that directly impacts resource utilization and operational efficiency. In this
paper, we investigate the application of metaheuristic optimization algorithms
to address the unrelated parallel machine scheduling problem (UPMSP) through
the lens of sustainable development goals (SDGs). The primary objective of this
study is to explore how metaheuristic optimization algorithms can contribute to
achieving sustainable development goals in the context of UPMSP. We examine a
range of metaheuristic algorithms, including genetic algorithms, particle swarm
optimization, ant colony optimization, and more, and assess their effectiveness
in optimizing the scheduling problem. The algorithms are evaluated based on
their ability to improve resource utilization, minimize energy consumption,
reduce environmental impact, and promote socially responsible production
practices. To conduct a comprehensive analysis, we consider UPMSP instances
that incorporate sustainability-related constraints and objectives.
