Energy Saving Opportunities and Value of Information: A Trade-off in a Production Line

Abstract

A general framework for switching the machine off/on has been recently proposed in literature for single machines. However, the amount of information available along the production system is often limited and it might be not trivial to understand which information provides more benefits. This paper studies the performance of a production line when several control policies are applied at machine level. The amount of information at machine level varies and the trade-off with energy reduction is investigated. The considered performance measures are the energy consumed per part and the system throughput. Numerical results are based on discrete event simulation

Design Model of Flow Lines to Include Switch-Off Policies Reducing Energy Consumption

“…As described in the literature [23], switching off policies based on buffer level information can lead to significant energy savings without reducing productivity. The proposed policies are upstream (UP), downstream (DP), and upstream and downstream (UDP).…”

“…The states 1 and 2 are called inactive states. According to Reference [23], the upstream and downstream combines the UP and DP policies as follows (Equation 13):…”

“…To determine the best switch off control parameters, a full factorial design has been developed. According to Reference [23], the levels of buffer to switch off or switch on machines respect the following constraints (Equations (14)- (16)): Figure 5 reports on the experiment results for model 1. The design with the lowest inactive time has a value of 0, the design with the highest inactive time has a value of 1.…”

“…Therefore, the simulation scenarios and the evaluation of the performances have been obtained by considering the best control parameters. According to Reference [23], the discrete event simulation length is 10 7 s, and the initial transient is 10 5 s. Figure 6 shows the setting of simulation scenarios and performance evaluations. Using mathematical optimization, the flow line design and the operations assignment to the stations have been achieved.…”

“…Su et al [23] and Frigerio and Matta [16] studied upstream, downstream, and mixed policies in a single machine, and the extension to a production line, assuming stochastic arrivals, and constant warm-up under to stock control. These models did not consider how the control of the machines may reduce the production performance of the manufacturing system.…”