Production and inventory control of auto parts based on predicted probabilistic distribution of inventory

作     者：JiSun Shin Sungshin Kim Jang-Myung Lee 

作者机构：Pusan National University 

出 版 物：《Digital Communications and Networks》 (数字通信与网络(英文))

年 卷 期：2015年第1卷第4期

页      面：292-301页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by Special Environment Navigation/Localization National Robotics Research Center of Pusan National University supported by the Industry Convergence Liaison Robotics Creative Graduates Education Program under the KIAT(N0001126) 

主　　题：Graphical modeling Dynamic Bayesian Network Production Adjusting Method Probabilistic distribution 

摘      要：Bayesian networks are probabilistic models used for prediction and decision making under uncertainty. The delivery quantity, the production quantity, and the inventory are changing according to various unexpected events. Then the prediction of a production inventory is required to cope with such irregular fluctuations. This paper considers a production adjustment method for an automobile parts production process by using a dynamic Bayesian network. All factors that may influence the production quantity, the delivery quantity, and the inventory quantity will be handled. This study also provides a production schedule algorithm that sequentially adjusts the production schedule in order to guarantee that all deadlines are met. Furthermore, an adjusting rule for the production quantities is provided in order to maintain guaranteed delivery.