Exact reoptimisation under gradual look-ahead for operational control in production and logistics
Exact reoptimisation under gradual look-ahead for operational control in production and logistics
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DOI：
10.1080/23302674.2022.2141590
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Fabian Dunke;S. Nickel
中科院分区：
文献类型：
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作者：
Fabian Dunke;S. Nickel
在决策过程中，关于未来的信息通常具有不同的不确定性程度。对于不久的将来，信息通常被假定为确定性的;具有前瞻性的在线优化处理这种情况。相反，更遥远的未来通常充满不确定性。未来越远，不确定性的程度就越明显;逐步前瞻的在线优化考虑了这种预测信息。生产和物流中的操作任务通常是由这些信息类型的混合体组成的。我们提出了一种基于数学规划（MP）的方法，该方法结合了近期和更遥远的未来的信息视野，通过精确的重新优化来解决在线优化问题。为此，我们研究如何MP配方离线问题转移到在线的情况下，通过调整他们逐步前瞻性的信息。此外，我们采用基于采样的鲁棒性来解释长期的不确定性。在线版本的组合问题的核心，从生产和物流（包装，路由，批量，调度）的数值实验中，我们说明了如何在实践中应用的方法。此外，分析允许建立一个基于样本的前瞻和预测值，表明提高预测能力的好处。
In decision making, information about the future typically comes in different uncertainty degrees. For the near-future, information is often assumed as deterministic; online optimisation with look-ahead deals with such situations. The more distant future, contrarily, is usually afflicted with uncertainty. The farther in the future, the more pronounced the degree of uncertainty; online optimisation with gradual look-ahead considers such forecasting information. Operational tasks in production and logistics are often coined by mixtures of these information types. We propose a methodology based on mathematical programming (MP) which combines information horizons for the near and more distant future to solve online optimisation problems with gradual look-ahead by exact reoptimisation. To this end, we investigate how MP formulations for offline problems are transferred to the online case by adapting them to gradual look-ahead information. Further, we employ a sampling-based robustification to account for long-term uncertainty. In numerical experiments on online versions of combinatorial problems which lie at the heart of many operational problems from production and logistics (packing, routing, lot sizing, scheduling), we illustrate how the methodology can be applied in practice. Moreover, the analysis allows to establish a sample-based look-ahead and forecasting value indicating the benefit of improving forecasting capabilities.