流水车间加工信息测度与控制

Measurement and control of processing information in a flow shop

流水车间加工信息的有效测度是实施加工批量决策的前提.当前多品种小批量生产信息控制研究多以生产技术、范式及智能算法等理论优化工作为主,极少开展生产的加工批量与其状态控制所需信息之间关系的研究,导致研究结论过于理论化而难以应用于现实生产.针对此研究局限性,本文从信息论角度出发,根据制造马尔科夫过程,定义流水车间的加工批量信息熵并解析其度量特性.进一步,构建流水车间加工批量信息熵函数,理论上证明其函数单调递减性.通过建立流水车间加工批次信息测度模型,确定生产线批次加工信息的组成结构,实现对工件加工所需信息量的测度.实证研究中,将平均调整准备时间、平均加工时间及完工时间等观测数据应用于实际企业的减速器生产线,做出不同参量与加工批量变化关系图,确定相关参数条件下生产线的最优加工批量.根据研究结果,提出有效减少管理与控制生产线所需信息量的对策,在验证所建熵模型科学性与有效性的同时,也为实际生产中的批量决策提供准确的现实指导.

Effective measurement of processing information in a flow shop is the prerequisite for implementing processing batch decisionmaking.Current research on information control of multivariate and small-batch production mainly focuses on optimizing the production technology and paradigms or intelligent algorithms.However,only a few studies have investigated the relationship between processing batches and the information required for static control,leading to research conclusions that are difficult to directly guide actual production.Considering the limitations of the above-mentioned research,this article first defines the information entropy function of batch processing in the flow shop from the perspective of the information theory.This function is then constructed,and its characteristics are analyzed,confirming that this function is monotonically decreasing.By constructing the processing batch information measurement model for a flow shop,the composition structure of the processing batch information for the production line is theoretically determined for the first time and measurement of the amount of information required for a job processing batch is achieved.An empirical study on the actual production line of reducer parts was conducted to verify the developed entropy models.Different relationships between different parameters and processing batch changes are drawn by applying the observation data,such as average adjustment preparation,average processing,and completion times,to develop the processing batch information measurement model.Additionally,the optimal processing batch for the actual production line is obtained under relevant parameter conditions.Based on the results acquired,some countermeasures are proposed to effectively reduce the amount of information required for managing and controlling the production line,which verifies the scientific nature and effectiveness of the developed entropy models and provides realistic guidance for batch decision-making in actual production.