Original title: A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview
Authors: Absalom E. Ezugwu
The article delves into sustainable practices in industries, focusing on parallel machine scheduling’s impact on resource use and efficiency. Investigating the unrelated parallel machine scheduling problem (UPMSP), it explores how metaheuristic optimization algorithms align with sustainable development goals (SDGs). The study assesses various algorithms like genetic algorithms and ant colony optimization, evaluating their efficacy in optimizing schedules. They analyze these algorithms based on resource use, energy consumption, environmental impact, and social responsibility in production. Using UPMSP instances with sustainability-related constraints, they aim for a comprehensive analysis. Ultimately, the research aims to illuminate how these optimization methods can contribute to sustainable development in scheduling, offering potential solutions for industries striving to align with global sustainability objectives.
Original article: https://arxiv.org/abs/2311.12802