PostgreSQL:关系型数据库结构在药物片剂生产过程中的容许批量大小上的应用

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Michael Simonis , Stefan Nickel
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引用次数: 0

摘要

具有关联批量和滞后订单的多级产能批量大小问题(MLCLSP-L-B)用于药片生产过程,以合理确定材料生产批量的大小,从而使成本保持在最低水平,不超出生产资源能力,并满足客户需求。不确定的需求行为是当今全球药片市场的特点。制药公司要求采用具有概率需求的 MLCLSP-L-B 解决方法。在片剂生产系统的工业应用中实施该模型需要高效的数据处理,因为数据量大,而且需要存储模拟需求场景。本文介绍了 MLCLSP-L-B 与概率需求和关系数据库结构(RDS)的首次集成。文中概述了 RDS 处理海量数据的建模技术。虚拟环境提供了实施软件 PostgreSQL 和基础设施环境。此外,还利用研究数据进行了数值实验,以评估所开发的 RDS 的敏捷性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PostgreSQL: Relational database structures application on capacitated lot-sizing for pharmaceutical tablets manufacturing processes
Multi-level capacitated lot-sizing problems with linked lot sizes and backorders (MLCLSP-L-B) are used in pharmaceutical tablets manufacturing processes to right-size material production lots so that costs are kept at a minimum, production resource capacities are not exceeded, and customer demand is fulfilled. Uncertain demand behavior characterizes today’s global tablets market. Pharmaceutical companies request solution approaches that solve the MLCLSP-L-B with probabilistic demand. Implementing this model in industrial applications for tablets manufacturing systems requires efficient data processing due to the amount of data and the capability to store simulated demand scenarios. This paper covers the first integration of the MLCLSP-L-B with probabilistic demand and Relational Database Structures (RDS). Modeling techniques for the RDS to process massive data are outlined. A virtual environment provides the implementation software PostgreSQL and infrastructure environment. Additionally, numerical experiments with research data are used to evaluate the agility and efficiency of the developed RDS.
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来源期刊
Software Impacts
Software Impacts Software
CiteScore
2.70
自引率
9.50%
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0
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16 days
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