Multi-criteria decision-making for optimization of product disassembly under multiple situations

Area of Science:

• Industrial Ecology
• Sustainable Manufacturing
• Operations Research

Background:

• Growing interest in material recovery from consumer products at end-of-life (EOL).
• Need for decision-making methodologies to balance environmental benefits and costs in EOL processing.
• Variability in EOL situations necessitates adaptive strategies.

Purpose of the Study:

• To develop a methodology for analyzing the impact of product design and situational variables on optimal EOL strategies.
• To identify Pareto sets of EOL strategies that maximize environmental benefits for a given economic cost or profit.
• To rapidly approximate Pareto sets critical for understanding EOL decision-making under diverse scenarios.

Main Methods:

• Utilized multi-objective genetic algorithms (GA) to approximate the Pareto set of optimal EOL trade-offs.
• Developed a methodology to analyze product design and situational variables' impact on EOL strategies.
• Applied the methodology to a case study of coffee maker EOL treatment.

Main Results:

• Demonstrated the capability of GA to rapidly calculate Pareto sets for complex EOL decision-making.
• Illustrated the impact of situational variables on energy recovery versus cost trade-offs in different locations (Aachen, Germany; Ann Arbor, MI).
• Showcased the influence of the EU Waste Electric and Electronic Equipment (WEEE) Directive on EOL trade-offs.

Conclusions:

• The methodology provides critical insights into optimizing EOL strategies for environmental and economic goals.
• Rapid Pareto set calculations are essential for informed decision-making in dynamic EOL environments.
• Understanding situational variable impacts enables more effective product design and EOL management.