Effective control of production operations usually leads to improved energy efficiency in manufacturing systems. In this paper, we investigate energy consumption reduction in production systems by scheduling of machine startup and shutdown. Specifically, we consider serial production lines with finite buffers and machines having Bernoulli reliability model. This machine reliability model is applicable in production situations, where the downtime is relatively short and comparable to machine cycle time (e.g., automotive paint shops and general assembly). Using transient analysis of the systems at hand, an analytical performance evaluation technique is developed for Bernoulli serial lines with time-dependent machine efficiencies. In addition, trade-off between productivity and energy-efficiency in production systems is discussed and the energy-efficient production problem is formulated as a constrained optimization problem. The effects and practical implications of operations schedule are demonstrated using a numerical study on automotive paint shop operations.

Research efforts for energy consumption reduction in manufacturing systems have been centered at technology and process innovation. These projects, however, often involve major capital investment of new equipment and material. In this paper, we explore energy saving opportunities through improvement in factory floor operations. Specifically, in the framework of Bernoulli serial lines, we consider production systems with stripping operations. In such systems, the in-process buffers have to be depleted at the end of each shift to avoid quality deterioration during off-shift periods. Transient analysis of the systems are carried out and formulas to calculate the performance measures are derived. In addition, we investigate the effect of machine startup schedule on the system performances and develop optimal startup schedule which, as shown in the paper, can lead to significant improvement in energy utilization efficiency.

Conventionally, improving production efficiency, flexibility and responsiveness has been the primary research focus of production management, while energy consumption has received relatively little attention. Energy consumption plays a more and more important role in manufacturing environment. This is mainly driven by energy cost and environmental concerns. When the energy system becomes complicated and coupled with ongoing production, it is very difficult to hunt the “hidden treasure” which affects the overall benefit of a manufacturing system. This paper provides a systematic method for energy management in a production system. We start from dynamic production transient analysis and provide quantitative analysis for energy saving opportunity in a system. Furthermore, energy saving is integrated into production system which includes downtime and throughput to provide integrated energy management framework for a production system. A case study is conducted to demonstrate its potential on energy savings in a multi-stage manufacturing system.

In manufacturing industry, downtimes have been considered as major impact factors of production performance. To improve the system performance in real-time and properly allocate limited resources/efforts to different stations, it is necessary to quantify the impact of each station downtime event on the production throughput of the whole transfer line. Complete characterization of the impact requires a careful investigation of the transients of the line dynamics disturbed by the downtime event. We study in this paper, the impact of single isolated downtime event on the performance of inhomogeneous serial transfer lines. Our mathematical analysis suggests that the impact of any isolated downtime event is only apparent in the relatively long run when the duration exceeds a certain threshold called opportunity window. The size of the opportunity window not only depends on the initial buffer levels but also depends on the location and the processing speed of the slowest station. Analytical solutions of the opportunity window and the loose upper bounds for the recovery time are also provided in the paper as two separate theorems. Despite the fact that the paper focuses on the transient analysis of single isolated downtime event, the results are applicable to opportunistic maintenance scheduling, personnel staffing and downtime cost estimation.