An Evolutionary Algorithm for Selective Disassembly of End-of-Life Products

This paper addresses the problem of creating intelligent, green, and financially-beneficial disassembly sequences for end-of-life (EOL) electronic products. These complex EOL products contain a broad spectrum of materials including precious metals. Therefore, one would have to process these products to retrieve the value buried in them. EOL processing options include, reuse, remanufacturing, recycling or proper disposal. Each of this option requires a certain level of disassembly. Hence, obtaining an optimal or near optimal disassembly sequence is crucial to increasing the efficiency of EOL processing. Since the complexity of determining the best disassembly sequence increases as the number of parts in a product grows, an efficient methodology is required for disassembly sequencing. In this paper, we present an evolutionary algorithm for generating near-optimal and/or optimal sequences for selective disassembly of EOL products. A numerical example is provided to demonstrate the functionality of the algorithm.