Abstract
Many studies have used different optimisation methods to find a near-optimal solution by optimising the disassembly operations sequence. These studies have used disassembly operation time as the main optimisation parameter, and other parameters such as direction change or tool change are converted to time scale. In order to determine accurate operation time, a product needs to be completely disassembled, noting that the same EOL products can be in a different condition and result in different operation time. In this work, new optimisation parameters based on the disassemblability and components demand are defined. These include Disassembly Handling Index (DHI), Disassembly Operation Index (DOI) and Disassembly Demand Index (DDI). In order to consider the operation time and other costs, Disassembly Cost Index (DCI) is further defined. Genetic algorithm optimisation method was employed to optimise the process sequence. Here, the most demanded components with the easiest disassembly operations are disassembled first without requiring to disassemble the unwanted components and avoid complicated operations. Two case studies were analysed to determine the effectiveness and compatibility of this method. The result shows 13% and 10% improvement in overall disassembly time for the case studies.
Highlights
High environmental pollution and low renewable material resources are just a few disadvantages of the traditional manufacturing industry [1]
The main advantage of the proposed method in this research is to avoid initial time estimation and generate more realistic sequences of disassembly operations based on the disassemblability of the products, it can be seen that the overall disassembly time was improved by 13%
The majority of studies have focused on time as the main parameter to find an optimum solution and other parameters such as tool change requirement and disposal casts are converted in a time unit
Summary
High environmental pollution and low renewable material resources are just a few disadvantages of the traditional manufacturing industry [1]. Parsa and Saadat [13] investigated automated disassembly using the genetic algorithm and proposed a model for robotic disassembly sequence optimisation. Other optimisation methods such as particle swarm optimisation algorithm were used to solve multi-objective optimisation problems [14]. A selective disassembly planning method for waste electrical and electronic equipment (WEEE) was proposed by Li et al They develop a selective disassembly planning method based on particle swarm optimisation with customisable decision-making They applied this model on WEEE to maximise the economic profit and reduce environmental problems [26]. The proposed method is tested on an automotive case study to verify its effectiveness and results are discussed
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