A multi-objective decision making approach for dealing with uncertainty in EOL product recovery

To deal with this limitation, taxonomy of uncertainty metrics is developed through the whole EOL product recovery. The quantifications of uncertainty measures of EOL product recovery are formulated by different dimensions of quality condition, disassembly complexity and EOL recovery. A multi-objective decision making approach for dealing with uncertainty in EOL product recovery is proposed. Artificial bee colony (ABC) algorithm is employed to find the best EOL options of components with maximum feasibility and profit for EOL product recovery. A typical motor is used as a case study to illustrate the methodology. This paper addresses the uncertainty involved in determining the EOL options of components for EOL product recovery. The proposed approach closes a gap in the current EOL product recovery assessment criteria. By comparing to those selections of EOL options without considering uncertainty, the results show that considering uncertainty turns EOL product recovery more realistic and can give several good alternatives to decision makers.