Energy consumption and profit-oriented disassembly line balancing for waste electrical and electronic equipment
Introduction
With the rapid development of science and technology, various kinds of electronic and electrical equipment (EEE) have emerged in an endless stream, and the speed of replacement is breakneck. Due to the maturity of EEE technology and the progress of the economic level, EEE quickly entered the peak period of scrapping (Guo et al., 2018). Currently, the recycling rate of waste electrical and electronic equipment (WEEE) is very low. WEEE has dual attributes of economic benefits and environmental pollution (Cucchiella et al., 2015). The conventional treatment methods of WEEE include recycling, reuse, and remanufacturing, which aims to utilize the value of the products as much as possible and reduce the amount of waste landfill (Harivardhini et al., 2017). Regardless of which treatment method is adopted, disassembly is an essential part. Especially for large-scale recycling of WEEE, disassembly enterprises adopt disassembly lines to improve the disposal efficiency (Mete et al., 2019). To meet environmental protection policies and improve disassembly efficiency, disassembly line balancing (DLB) is a hot and challenging issue (Kazancoglu and Ozturkoglu, 2018).
The traditional disassembly method does not take into account the uncertainty factors, such as corrosion and deformation of the connectors (Kongar and Gupta, 2006). The constraint relationship between the connector and the component cannot be removed according to the normal disassembly method. Destructive disassembly is often used to address this situation in the actual disassembly process (Song et al., 2014). To the best of the authors’ knowledge, there is no research on destructive disassembly lines. Moreover, the economic benefits and environmental impact of the disassembly line are often ignored, and the performance of the disassembly line cannot be comprehensively evaluated in the existing literature. In addition, traditional optimization methods cannot take into account the feasibility of the results and the efficiency of large-scale problems (McGovern and Gupta, 2007b; Avikal et al., 2014), so it is essential to design a new optimization method to obtain high-quality feasible disassembly schemes.
The focus of this paper is to establish a comprehensive evaluation model of the disassembly line with uncertain conditions, and propose a new heuristic method to solve the problem. Compared with existing studies, this paper has the following differences or contributions: 1) To address the uncertainty between parts, the destructive disassembly mode is introduced into the disassembly line for the first time; 2) A partial destructive disassembly line balancing model is proposed to evaluate the economic benefits and environmental impacts of the disassembly line; 3) A new improved genetic algorithm based on the problem characteristics is developed to optimize the problem.
The rest of the paper is organized as follows. Section 2 provides a review of the disassembly line balancing. Section 3 describes the problem characteristics and evaluation indicators of the disassembly line. Section 4 introduces the proposed method. Section 5 is a case application analysis. Finally, Section 6 draws conclusions and future research directions.
Section snippets
Literature review
The disassembly line balancing has been well investigated in the existing literature, mainly from three aspects: disassembly mode, optimization objective, and optimization method. This section will review the three aspects.
Problem statement
The disassembly line balancing assigns disassembly tasks to each station while satisfying the cycle time and precedence constraints, to optimize the number of stations, smoothness index, energy consumption and disassembly profit of the disassembly line. The partial destructive disassembly line needs to determine whether the task is disassembled and determine the disassembly mode of each task. Partial destructive disassembly line balancing (PDDLB) is more complicated than the general DLB.
Proposed method
As a classical swarm intelligence algorithm, the genetic algorithm shows better performance in many discrete combinatorial optimization problems, such as flexible job shop scheduling (Wu and Sun, 2018) and green vehicle routing and scheduling (Xiao and Konak, 2017). To obtain high-quality feasible disassembly schemes quickly, this section will design an improved genetic algorithm to solve the PDDLB problem.
The proposed method mainly includes the initialization of the population, genetic
Case study
In August 2018, the authors investigated a television disassembly line in a disassembly enterprise in Southwest China. The investigation results showed that the disassembly mode in the production line was chaotic, and the disassembly task assignment was unreasonable. To verify the performance of the proposed model and method, this section will take the television disassembly line as an example and use the proposed model and method to optimize the performance of the disassembly line.
Conclusions
Given the uncertain factors in the actual disassembly process, such as parts corrosion and deformation, this paper introduces the destructive disassembly mode into the disassembly line for the first time and constructs a partial destructive disassembly line balancing model. The model focuses on the number of stations, smoothness index, disassembly profit, and energy consumption, which can comprehensively evaluate the efficiency, economic benefit and environmental impact of the disassembly line.
CRediT authorship contribution statement
Kaipu Wang: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft, Visualization. Xinyu Li: Methodology, Formal analysis, Data curation, Supervision, Writing - review & editing. Liang Gao: Conceptualization, Funding acquisition, Resources, Project administration, Writing - review & editing. Peigen Li: Methodology, Formal analysis, Supervision, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Key Research and Development Project of China (grant number 2019YFB1704603), the National Natural Science Foundation of China (grant number 51721092), and Program for HUST Academic Frontier Youth Team (grant number 2017QYTD04).
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