A joint design of production, maintenance planning and quality control for continuous flow processes with multiple assignable causes

• Published in: CIRP journal of manufacturing science and technology Vol. 43; pp. 214 - 226
• Main Authors: Wan, Qiang, Zhu, Mei, Qiao, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Continuous flow process; Economic production quantity; Maintenance; Multiple assignable causes; Statistical process control; Statistical process control; Economic production quantity; Multiple assignable causes; Maintenance; Continuous flow process
• ISSN: 1755-5817
• DOI: 10.1016/j.cirpj.2023.04.006

Although the joint optimisation of the three aspects - production, quality control and maintenance - helps to promote efficiency and obtain operational cost savings in the production process, these three concepts are usually studied separately in the literature. Moreover, almost all existing integration studies on this issue are centred on piece part manufacturing, and the integrated modelling in a continuous flow production environment has not been well investigated. In addition, most existing integrated models assume only one assignable cause can occur during the manufacturing process, which is not in accord with the actual production condition because of the usual complexity of production systems. In real cases, production processes are usually subject to a multiplicity of assignable causes. Based on these facts, this paper presents a joint design scheme of production, maintenance, and statistical process control for continuous flow processes with multiple assignable causes. The mathematical model is solved using a developed genetic algorithm that minimises the expected total cost (ETC) per process cycle. A comparison between the one-cause and multi-cause models from an economic aspect is conducted to verify the advantages of the suggested model. A sensitivity analysis is also carried out on some selected variables concerning the ETC and expected production quantity to gain an in-depth insight into the concern.