Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

Kalaycı, Can Berk; Gupta, Surendra M.

doi:10.1016/j.eswa.2013.06.067

Cited by 158 publications

(53 citation statements)

References 20 publications

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“…The idle power P 0i was set to 0.10 kW·h, the disassembling power P dsi was set to 0.25 kW·h, the direction changing power P dci was set to 0.15 kW·h, the tool changing power P tci was set to 0.15 kW·h, and the cycle time CT was 60 s. In addition, standby time was short compared with the whole disassembling process time; standby is not considered in this paper. Due to the combinatorial nature of the optimization model of the DLBP with an energy saving consideration, it was solved based on the artificial bee colony algorithm [18]. The proposed approach was implemented in MATLAB R2014b and was run on a laptop computer (Intel/Core i5 CPU, 3.10 GHz and 8 GB RAM with a Windows 7 operating system).…”

Section: Case Studymentioning

confidence: 99%

“…Energies 2018, 11, x FOR PEER REVIEW 10 of 18 with an energy saving consideration, it was solved based on the artificial bee colony algorithm [18]. The proposed approach was implemented in MATLAB R2014b and was run on a laptop computer (Intel/Core i5 CPU, 3.10 GHz and 8 GB RAM with a Windows 7 operating system).…”

Section: Case Studymentioning

confidence: 99%

“…Kalaycilar et al [17] presented a model of a disassembly line with a fixed number of workstations considering a specified finite supply and specified release quantities. In order to solve the DLBP, Kalayci and Gupta [18] proposed a methodology based on the particle swarm algorithm to solve the DLBP while satisfying the disassembly precedence constraints and optimizing the effectiveness. Later, Kalayci et al [19] proposed a hybrid algorithm that combined a genetic algorithm with a variable neighborhood search method to solve the sequence-dependent DLBP.…”

Section: Disassembly Line Evaluation and Optimizationmentioning

confidence: 99%

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An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

et al. 2018

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Abstract:Concerns have been increasing regarding the environmental sustainability of disassembly activities that take place in various recovery operations at end of life stage of products, and subsequently, disassembly activities have been gaining increased exposure. The disassembly line is a good choice for automated disassembly of end-of-life products. Although interest in addressing energy saving in manufacturing is rising, the study of incorporating energy consumption reduction and disassembly efficiency improvement into disassembly line balancing is still limited. The analysis, design, and balanced decision-making for disassembly lines are urgently needed in order to make the disassembly line as energy-saving as possible. In this paper, an energy-saving optimization method, which was used in scheduling workstation selection and the disassembly sequence for the disassembly line, is proposed. Energy consumption of each stage of the disassembling process was analyzed and modeled. Then, a mathematical model of the disassembly line balancing problem with energy saving considerations was formulated and the objective of energy consumption was integrated into the objectives of cost and working load in order to balance a disassembly line. Optimal solutions for the disassembly line balancing problem with an energy saving consideration were obtained by using an artificial bee colony algorithm, which was a feasible way of minimizing workstations, and ensuring similar working load, as well as minimizing energy consumption. Finally, a case study of disassembly line balancing for a typical driving system is presented to illustrate the proposed method.

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Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Disassembly Line Evaluation and Optimizationmentioning

confidence: 99%

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An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

et al. 2018

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“…Go et al (2012) posited a model to determine optimal sequence of disassembly using genetic algorithm. In their article, Kalayci and Gupta (2013) have explored Sequence-Dependent Disassembly Line Balancing Problem (SDDLBP) with multiple objectives that required allocation of disassembly tasks to an ordered group of twine disassembly work-stations through satisfying disassembly preference constraints and optimization of efficiency in several tools. They proposed Artificial Bee Colony (ABC) algorithm to solve the given problem dealt with solving profit-oriented disassembly line balancing problem by considering partial disassembly and presence of risky elements and uncertainty of task times.…”

Section: A Review On Subject Literaturementioning

confidence: 99%

The Balancing of Disassembly Line of Automobile Engine Using Genetic Algorithm (GA) in Fuzzy Environment

Seidi¹,

Saghari²

2016

Industrial Engineering and Management Systems

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Disassembly is one of the important activities in treating with the product at the End of Life time (EOL). Disassembly is defined as a systematic technique in dividing the products into its constituent elements, segments, sub-assemblies, and other groups. We concern with a Fuzzy Disassembly Line Balancing Problem (FDLBP) with multiple objectives in this article that it needs to allocation of disassembly tasks to the ordered group of disassembly Work Stations. Tasks-processing times are fuzzy numbers with triangular membership functions. Four objectives are acquired that include: (1) Minimization of number of disassembly work stations; (2) Minimization of sum of idle time periods from all work stations by ensuring from similar idle time at any work-station; (3) Maximization of preference in removal the hazardous parts at the shortest possible time; and (4) Maximization of preference in removal the high-demand parts before low-demand parts. This suggested model was initially solved by GAMS software and then using Genetic Algorithm (GA) in MATLAB software. This model has been utilized to balance automotive engine disassembly line in fuzzy environment. The fuzzy results derived from two software programs have been compared by ranking technique using mean and fuzzy dispersion with each other. The result of this comparison shows that genetic algorithm and solving it by MATLAB may be assumed as an efficient solution and effective algorithm to solve FDLBP in terms of quality of solution and determination of optimal sequence.

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“…Therefore, the parts selection of non-destructive or destructive disassembly should be optimized in terms of the recycling rate and profit. On the other hand, since the precedence relationships among disassembly tasks of the product also change with the parts selection, it is required to optimize allocation of the tasks in designing a disassembly line (Avikal et al, 2013;Aydemir-Karadag and Turkbey, 2013;Kalayci and Gupta, 2013;McGovern and Gupta, 2003). In addition, because information is required for such a design, the recycling rate, profit of each part, and disassembly task times take precedence among the disassembly tasks.…”

Section: Introductionmentioning

confidence: 99%

2-Stage Optimal Design and Analysis for Disassembly System with Environmental and Economic Parts Selection Using the Recyclability Evaluation Method

Igarashi¹,

Yamada²,

Inoue³

2014

Industrial Engineering and Management Systems

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Promotion of a closed-loop supply chain requires disassembly systems that recycle end-of-life (EOL) assembled products. To operate the recycling disassembly system, parts selection is environmentally and economically carried out with non-destructive or destructive disassembly, and the recycling rate of the whole EOL product is determined. As the number of disassembled parts increases, the recycling rate basically increases. However, the labor cost also increases and brings lower profit, which is the difference between the recovered material prices and the disassembly costs. On the other hand, since the precedence relationships among disassembly tasks of the product also change with the parts selections, it is also required to optimize allocation of the tasks in designing a disassembly line. In addition, because information is required for such a design, the recycling rate, profit of each part and disassembly task times take precedence among the disassembly tasks. However, it is difficult to obtain that information in advance before collecting the actual EOL product. This study proposes and analyzes an optimal disassembly system design using integer programming with the environmental and economic parts selection (Igarashi et al., 2013), which harmonizes the recycling rate and profit using recyclability evaluation method (REM) developed by Hitachi, Ltd. The first stage involves optimization of environmental and economic parts selection with integer programming with ε constraint, and the second stage involves optimization of the line balancing with integer programming in terms of minimizing the number of stations. The first and second stages are generally and mathematically formulized, and the relationships between them are analyzed in the cases of cell phones, computers and cleaners.

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Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

Cited by 158 publications

References 20 publications

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

An Energy-Saving Optimization Method of Dynamic Scheduling for Disassembly Line

The Balancing of Disassembly Line of Automobile Engine Using Genetic Algorithm (GA) in Fuzzy Environment

2-Stage Optimal Design and Analysis for Disassembly System with Environmental and Economic Parts Selection Using the Recyclability Evaluation Method

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