Green machine scheduling consists in the allocation of jobs in order to maximize production, in view of the sustainable use of energy. This work addresses the unrelated parallel machine scheduling problem with setup times, with the minimization of the makespan and the total energy consumption. The latter takes into account the power consumption of each machine in different operation modes. We propose multi-objective extensions of the Adaptive Large Neighborhood Search (ALNS) metaheuristic with Learning Automata (LA) to improve the search process and to solve the large scale instances efficiently. ALNS combines ad-hoc destroy and repair (also named removal and insertion) operators and a local search procedure. The LA is used to adapt the selection of insertion and removal operators within the framework of ALNS. Two new algorithms are developed: the MO-ALNS and the MO-ALNS/D. The first algorithm is a direct extension of single objective ALNS by using multi-objective local search. As this method does not offer much control of the diversification of the Pareto front approximation, a second strategy employs the decomposition approach similar to MOEA/D algorithm. The results show that the MO-ALNS/D algorithm has better performance than MO-ALNS and MOEA/D in all indicators. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but it is indeed an efficient way to extend ALNS to multi-objective problems.

鉴于能源的可持续利用，绿色机器调度包括分配工作以最大程度地提高产量。这项工作解决了与建立时间无关的并行机器调度问题，并最大程度地缩短了工期和总能耗。后者考虑了不同操作模式下每台机器的功耗。我们提出了带有学习自动机（LA）的自适应大邻域搜索（ALNS）元启发式方法的多目标扩展，以改善搜索过程并有效解决大规模实例。ALNS结合了临时销毁和修复（也称为删除和插入）运算符和本地搜索过程。LA用于在ALNS框架内调整插入和删除运算符的选择。开发了两种新算法：MO-ALNS和MO-ALNS / D。第一种算法是通过使用多目标本地搜索对单目标ALNS进行直接扩展。由于此方法不能很好地控制Pareto前沿逼近的多样化，因此第二种策略采用类似于MOEA / D算法的分解方法。结果表明，在所有指标上，MO-ALNS / D算法的性能均优于MO-ALNS和MOEA / D。这些发现表明，分解策略不仅对进化算法有益，而且确实是将ALNS扩展到多目标问题的有效方法。第二种策略采用类似于MOEA / D算法的分解方法。结果表明，在所有指标上，MO-ALNS / D算法的性能均优于MO-ALNS和MOEA / D。这些发现表明，分解策略不仅对进化算法有益，而且确实是将ALNS扩展到多目标问题的有效方法。第二种策略采用类似于MOEA / D算法的分解方法。结果表明，在所有指标上，MO-ALNS / D算法的性能均优于MO-ALNS和MOEA / D。这些发现表明，分解策略不仅对进化算法有益，而且确实是将ALNS扩展到多目标问题的有效方法。