Resource saving has become an integral aspect of manufacturing in industry 4.0. This paper proposes a multisystem optimization (MSO) algorithm, inspired by implicit parallelism of heuristic methods, to solve an integrated production scheduling with resource saving problem in textile printing and dyeing. First, a real-world integrated production scheduling with resource saving is formulated as a multisystem optimization problem. Then, the MSO algorithm is proposed to solve multisystem optimization problems that consist of several coupled subsystems, and each of the subsystems may contain multiple objectives and multiple constraints. The proposed MSO algorithm is composed of within-subsystem evolution and cross-subsystem migration operators, and the former is to optimize each subsystem by excellent evolution operators and the later is to complete information sharing between multiple subsystems, to accelerate the global optimization of the whole system. Performance is tested on a set of multisystem benchmark functions and compared with improved NSGA-II and multiobjective multifactorial evolutionary algorithm (MO-MFEA). Simulation results show that the MSO algorithm is better than compared algorithms for the benchmark functions studied in this paper. Finally, the MSO algorithm is successfully applied to the proposed integrated production scheduling with resource saving problem, and the results show that MSO is a promising algorithm for the studied problem.

中文翻译：

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纺织品印染资源节约问题的集成生产计划的多系统优化。

节省资源已成为工业4.0中制造业不可或缺的一部分。提出了一种启发式方法的隐式并行性的多系统优化（MSO）算法，以解决纺织品印染中的一种资源节约问题的集成生产调度问题。首先，将具有资源节约的实际集成生产计划表述为多系统优化问题。然后，提出了MSO算法来解决由几个耦合子系统组成的多系统优化问题，并且每个子系统可能包含多个目标和多个约束。提出的MSO算法由子系统内部演化和跨子系统迁移运算符组成，前者是由优秀的进化算子对每个子系统进行优化，而后者则是完成多个子系统之间的信息共享，以加速整个系统的全局优化。在一组多系统基准功能上测试了性能，并与改进的NSGA-II和多目标多因子进化算法（MO-MFEA）进行了比较。仿真结果表明，对于本文研究的基准函数，MSO算法优于比较算法。最后，将MSO算法成功地应用于提出的具有资源节约问题的集成生产调度中，结果表明，MSO是解决该问题的有前途的算法。在一组多系统基准功能上测试了性能，并与改进的NSGA-II和多目标多因子进化算法（MO-MFEA）进行了比较。仿真结果表明，对于本文研究的基准函数，MSO算法优于比较算法。最后，将MSO算法成功地应用于提出的具有资源节约问题的集成生产调度中，结果表明，MSO是解决该问题的有前途的算法。在一组多系统基准功能上测试了性能，并与改进的NSGA-II和多目标多因子进化算法（MO-MFEA）进行了比较。仿真结果表明，对于本文研究的基准函数，MSO算法优于比较算法。最后，将MSO算法成功地应用于提出的具有资源节约问题的集成生产调度中，结果表明，MSO是解决该问题的有前途的算法。