Large scale batch scheduling problems with complex constraints are difficult and time-consuming to solve. Therefore, this paper addresses the green batch scheduling problem on non-identical parallel machines with time-of-use electricity prices. The objective of the problem is to minimise total electricity costs (TEC) in production. Two kinds of algorithms—single-population genetic algorithms (SPGA) and multi-population genetic algorithm (MPGA)—are proposed to solve the problem. In the algorithms, the products are allocated into batches and are then allocated to machines randomly. A greedy strategy is designed to arrange the production sequence and the starting time of the batches. Furthermore, a self-adaptive parameter adjustment strategy is proposed to enhance the adaptability of the algorithm. Computational experiments with CPLEX solver have been conducted to evaluate the performance of the algorithms. On small instances, both SPGA and MPGA can achieve approximate results compared with those obtained by CPLEX, and can also achieve smaller TEC on large instances with less computing time. In addition, the proposed MPGA implemented by parallel computing outperforms SPGA in getting better results with nearly the same computing time.

中文翻译：

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带有贪心策略的遗传算法用于异机并行绿色批处理调度

具有复杂约束的大规模批处理调度问题很难且耗时。因此，本文解决了使用分时电价的不相同并行机上的绿色批处理调度问题。该问题的目的是使生产中的总电费（TEC）最小化。为了解决该问题，提出了两种算法：单种群遗传算法（SPGA）和多种群遗传算法（MPGA）。在算法中，将产品分批分配，然后随机分配给机器。设计了一个贪心策略来安排生产顺序和批次的开始时间。为了提高算法的适应性，提出了一种自适应参数调整策略。已经使用CPLEX求解器进行了计算实验，以评估算法的性能。在小型实例上，SPGA和MPGA均可达到与CPLEX相比的近似结果，并且在大型实例上也可以以较少的计算时间实现较小的TEC。此外，在几乎相同的计算时间下，通过并行计算实现的拟议MPGA优于SPGA。