A joint design of production run length, maintenance policy and control chart with multiple assignable causes

• Published in: Journal of manufacturing systems 2017-01, Vol.42, p.44-56
• Main Authors: Salmasnia, Ali, Abdzadeh, Behnam, Namdar, Mohammadreza
• Format: Article
• Language: English
• Subjects: Economic production quantity; Maintenance; Multiple assignable causes; Particle swarm optimization; Statistical process monitoring
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2016.11.003

•A joint design of production run length, maintenance policy and control chart for manufacturing systems is proposed.•This study considers multiple assignable causes to make the model more adapted to the real manufacturing situations.•The expected total cost per production cycle is minimized subject to statistical quality constraints. Although economic production quantity, statistical process monitoring and maintenance are three major concepts in process optimization of industrial environments, they have been often investigated separately in literature. Furthermore, in studies that consider these three concepts simultaneously, it is assumed that there is only one assignable cause in the production process. This simplified assumption is unlikely to occur in real production processes due to the usual complexity of manufacturing systems, which may lead to a poor performance in both economic and statistical criteria if the assignable cause originating the shift is different from the one anticipated at the design of the chart. To overcome these drawbacks, this paper develops an integrated model ofeconomic production quantity, statistical process monitoring and maintenance in the presence ofmultiple assignable causes. The particle swarm optimization algorithm is used to minimize the expected total cost per production cycle, subject to statistical quality constraints. Also, a comparative study is performed to illustrate the effect of considering multiple assignable causes on model’s costs. Finally, a sensitivity analysis is conducted on the expected total cost per production cycle and process variable values to extend insights into the matter.