Energy-efficient control in serial production lines: Modeling, analysis and improvement⋆. (July 2021)

• Record Type: Journal Article
• Authors: Cui, Peng-Hao; Wang, Jun-Qiang; Li, Yang; Yan, Fei-Yi
• Is Part Of: Journal of manufacturing systems. Volume 60(2021)
• Page Start: 11
• Page End: 21
• Publication Date: 2021-07
• Subjects: Energy efficiency -- Markov chain model -- N-policy -- Serial production lines; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.04.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A Markov chain model is established for energy-efficient serial production lines. The integrated dynamics of production and energy consumption is explored. A N-policy based optimal control method is proposed to improve production energy efficiency. Abstract: Control of production operations is one of the most cost-effective methods to improve energy efficiency in production lines. Although many research efforts have been devoted to utilizing production control to boost production energy efficiency, it is not clear how production dynamics change in reaction to the energy saving operations, which is important to capture energy saving opportunities. In this paper, an optimal energy saving control method based on N-policy is introduced to reduce the system energy consumption while only slightly decreasing productivity. A Markov chain model is established to interpret the dynamics of serial production lines with N-policy. The analytical formulas of production rate and energy consumption are derived. Then, an energy-efficient control model is formulated to balance the productivity and energy consumption. For two-machine production lines, the monotonicity property of the model is proved, and the analytical N-policy is derived. For longer production lines, an adaptive genetic algorithm is utilized to solve the energy-efficient control model. Case studies validate the effectiveness of the proposed optimal energy saving control method.