A belief-rule-based inference method for aggregate production planning under uncertainty
A belief-rule-based inference method for aggregate production planning under uncertainty
复制标题
不确定性下基于置信规则的总生产计划推理方法
DOI：
10.1080/00207543.2011.652262
复制
发表时间：
2013-01-01
影响因子：
9.2
通讯作者：
Qi, Chao
中科院分区：
文献类型：
--
作者：
Li, Bin;Wang, Hongwei;Qi, Chao
为综合生产计划(APP)寻找高性能的解决方案对学术界和实践者来说都是一个巨大的挑战。在现实世界的问题中，严重的需求波动使得预测很难可靠。从近期到远期，预测误差可能会有偏差并放大，而不稳定的需求通常以不确定的形式进行预测。针对需求不确定的APP，提出了一种新的分层信任规则推理(BRBI)方法。作为一个具有信念规则结构的专家系统，BRBI可以通过相应的信息表示、因果推理和识别算法来辅助决策者规划生产、劳动力和库存水平。可以使用操作数据和专家知识来构建、初始化和调整信念规则库(BRB)。开发了一种推理机算法，可以同时处理确定性输入和区间输入。为了使该方法同时适用于连续和离散生产设置，采用不同的变换技术，提出了BRBI的连续模式和切换模式。为了逼近APP情况下的隐藏模式，提出了BRB结构和参数的同时辨识和两步辨识。两步辨识包含了由k-均值和模糊c-均值扩展而来的信任k-均值(BKM)聚类算法。BKM保证了最优聚类既能促进人的认知，又能提高识别和推理的准确性。以某油漆厂为例，进行了确定性预测环境下的对比研究和灵敏度分析，并以汽车生产为例，说明了BRBI方法在区间预测环境中的优势，并对同时辨识和两步辨识进行了对比。
Finding high-performance solutions for aggregate production planning (APP) poses a significant challenge for both academics and practitioners alike. In real-world problems, severe demand fluctuations make forecasts hardly reliable. Forecast errors can be biased and magnifying from immediate to distant periods, and unstable demands are usually forecast in uncertain forms. For APP under uncertain demands, a new hierarchical belief-rule-based inference (BRBI) method is proposed. As an expert system with a belief-rule structure, BRBI can assist decision-makers in planning production, workforce and inventory levels with corresponding information representation, causal inference and identification algorithms. Operational data and expert knowledge can be employed to construct, initialise, and adjust the belief-rule base (BRB). An inference engine algorithm is developed to handle both deterministic and interval inputs. In order to make the method applicable to both continuous and discrete production settings, continuous mode and switching mode for BRBI are proposed using different transformation techniques. To approximate hidden patterns in APP situations, simultaneous identification and two-step identification for structure and parameter of BRB are developed. The two-step identification contains a belief k-means (BKM) clustering algorithm extended from k-means and fuzzy c-means. BKM ensures that an optimal cluster can both facilitate human cognition and improve accuracy of identification and inference. A paint-factory example is utilised to conduct comparative studies and sensitivity analyses in deterministic forecast context, and an automotive production example is implemented to illustrate BRBI's advantage in interval forecast context and to contrast simultaneous identification and two-step identification.