An energy and time prediction model for remanufacturing process using graphical evaluation and review technique (GERT) with multivariant uncertainties.

The rising energy price and stringent energy efficiency-related legislations encourage decision makers to concern more about energy efficiency in current manufacturing competition. In this regard, a quick and accurate prediction of the energy consumption and makespan in the manufacturing process has been a prerequisite for energy optimization. Given the various types of uncertainties in the remanufacturing system such as stochastic, fuzzy, and grey factors, the present study developed a prediction model that forecasts the energy consumption, completion time, and probability of processing routes. It adopted the graphical evaluation and review technique (GERT) to convert remanufacturing process into an uncertain network, considering multivariant uncertainties instead of merely stochastic uncertainty in prior works. We provided a generic seven steps to implement this approach. The energy consumption and completion time of remanufacturing process were determined in conjunction with Mason's rule and chance-constrained programming. Connecting rod reprocessing was presented as a numerical example. Based on the GERT network, we conducted an Arena simulation to validate the feasibility and effectiveness of this approach. In addition, we adopted the concept of criticality index to conduct sensitivity analysis and examine the predominant factors affecting the concerned indicators. This study would enable remanufacturers to perform a quick prediction of energy use and makespan in remanufacturing process.