Multi-objective partial disassembly optimization based on sequence feasibility

Abstract

A sustainable manufacturing system integrates production systems, consumer usage behavior, and End-of-Life (EoL) product value recovery activities. Facilitating multi-objective disassembly planning can be a step toward analyzing the tradeoffs between the environmental impact and profitability of value recovery. In this paper, a Genetic Algorithm (GA) heuristic is developed to optimize partial disassembly sequences based on disassembly operation costs, recovery reprocessing costs, revenues, and environmental impacts. EoL products may not warrant disassembly past a unique disassembly level due to limited recovered component market demand, minimal material recovery value, or minimal functional recovery value. The effectiveness of the proposed GA is first verified and tested using a simple disassembly problem and then applied to the traditional coffee maker disassembly case study. Analyses are disaggregated into multiple disassembly network optimization problems, one for each product subassembly, resulting in a bottom-up approach to EoL product partial disassembly sequence optimization.