Scheduling in shop floor manufacturing using systematic solution space navigation

对于调度问题，存在许多解决方案的方法，而通过解决方案空间的导航从未被调查。现有的方法旨在优化算法本身或更好地表示或可视化的解决方案空间。使用动态生成的解空间的知识的优化策略的微调是失踪，直到今天。一个严重的研究赤字存在于寻找神的解决方案的基础上，通过解决方案空间的预期和动态适应的导航策略的领域。也缺少调查，指出如何重要的跨越，重新安排和索引的解决方案空间。尚未采取系统地索引解空间并在如此编码的空间中搜索的方法。该研究项目的主要目标是扩大遗传算法的经典操作（选择，变异，重组）与有针对性的导航在解决方案空间作为一个新的和额外的操作。特别有趣的是，可以在没有重复模式的情况下遍历解决方案空间。除了斐波那契数也设置额外的号码行进行调查。为了达到这一目标，该研究项目解决了以下研究问题：如何多维调度问题的解决方案空间可以被构造成能够解决和浏览以下某些标准？当从解空间中选择解时，传统的调度方法可以获得哪些性能优势？在遗传算法中，作为选择、变异和重组的补充，操纵操作可能带来哪些额外的好处？解决方案空间导航适用于哪些顺序场景？当有可能在非常巨大的解空间中找到好的解时，在扰动发生后直接提供了真实的时间规划的新可能性。这导致生产率的巨大提高，从而也增加了竞争优势。该项目的宏伟目标是将解决调度问题所需的时间至少减少一个数量级。相反60秒一个6秒是必要的找到一个好的解决方案，而不是一个小时只有6分钟。

For scheduling problems many approaches for solutions exist, while the navigation through the solution space was never investigated. Existing approaches aim for an optimisation of the algorithm itself or for the better representation or visualisation of the solution space. The fine tuning of an optimisation strategy using dynamically generated knowledge of the solution space is missing till today. A severe research deficit exists in the realm of looking for god solutions based on intended and dynamic adapted navigation strategies through the solution space. Also investigations are missing that point out how important the spanning, rearranging and indexing of the solution space is. The approach to systematically index the solution space and search in the so coded space has not yet been undertaken. The main goal of this research project is to enlarge the classical operators of genetic algorithms (selection, mutation, recombination) with the targeted navigation in the solution space as a new and additional operator. Specially interesting is the possibility to rummage through the solution space without recurring patterns. Beside prim numbers also Fibonacci-sets additional number rows are to be investigated. To reach this goal the research project addresses the following research questions: How the solution space of a multi-dimensional scheduling problem can be structured to be able to be addressed and browsed following certain criteria? Which performance benefit a conventional scheduling approach can get when during the opening solutions from the solution space can be selected following certain criteria? Which additional benefit is possible with manipulating operations in Genetic algorithms as a supplement for selection, mutation and recombination? For which order scenarios the solution space navigation is suitable? When it is possible to find good solutions in very huge solution spaces new possibilities for real time planning are offered directly after a disturbance occurs. This leads to tremendous productivity increase and therefore also to an increase in competitive advantages. The ambitious goal of the project is to cut the time needed to solve a scheduling problem by at least one magnitude. Instead 60 sec one 6 sec are necessary to find a good solution, instead one hour only 6 min.