Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm

Kalaycı, Can Berk; Hancilar, Arif; Güngör, Aşkıner; Gupta, Surendra M.

doi:10.1016/j.jmsy.2014.11.015

Cited by 110 publications

(45 citation statements)

References 40 publications

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“…The researchers in [38,39] proposed schemes through a bacterial foraging algorithm ant sColony algorithm Solving multi-objective DLBP. Li Ming et al [40] established a multi-objective U-shaped DLBP model based on lean production and solved it by using an artificial bee colony algorithm. Considering the uncertainty of disassembly time, Kalayci et al [41] introduced triangular fuzzy membership degree 1.…”

Section: Background Of Abcmentioning

confidence: 99%

An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

et al. 2019

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Featured Application: Supplier chain management, Industry production, Efficient industry management, global business management, smart manufacturing. Abstract: Modern day industries strive to obtain long-term supplier integrations (SI) with potentially stronger supplier groups, to achieve fast and reliable production. This paper studies the process of selecting vendors, while simultaneously considering the aspects of random factors, multiple criteria, and efficiently reaching optimal solutions to improve the SI. A framework was developed that consists of three layers of expert opinions, supplier requirements, and multi-objective bee colony optimization. The model factors affecting the SI decision were explored from the comprehensive relevant literature, and these factors were shortlisted and prioritized. Routines for the modeled framework were coded by using the proposed algorithms which were implemented for a real-world problem from a manufacturing small and medium enterprise (SME) in Pakistan. Optimization of SI was carried out on an archived artificial bee colony (AABC). Its effectiveness was also evaluated by comparison with simple artificial bee colony (ABC) and particle swarm algorithms. The methodologically calculated results, obtained from simulation of a mathematically reinforced optimization framework, are highly beneficial for the industry, as well as local and international suppliers. A detailed and in-depth evaluation of suppliers was provided by the sensitivity analysis, which presented a more rigorous authentication and elaboration of the results. The presented framework is the first of its kind for the SMEs of Pakistan and can be applied with little modification to other industries. competitive business climate, companies cannot afford undue delays in supply orders. From small industries to mega projects, the supply chain plays an important role in achieving production goals [2].A sound number of these challenges can be successfully handled if enterprises can develop long-term win-win relationships with well-matched international and national prospective suppliers. Therefore, the concept of supplier integration (SI) has been recently proposed by different researchers to enable enterprises to conveniently cope with these dynamic supply chain business issues [3][4][5][6]. SI is the concept for improving the part of the supply chain between manufacturers and their respective suppliers of ingredients, raw materials, packaging, etc. By sharing information between parties, the suppliers and buyers are able to exercise judgment on costs, quantities, timing of deliveries, production streamline product flow, and to create a collaborative relationship [7]. However, this strategy is coupled with a number of problems. The demographic location of countries, their economic status, product categories, and the capabilities of suppliers are some of the main factors affecting the decision regarding selection of suppliers and long-term integration with the manufacturer [8].The literature shows that different factors...

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Section: Background Of Abcmentioning

confidence: 99%

An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

et al. 2019

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“…Genetic algorithm was utilized for solving fuzzy assembly line balancing problem with multiple objectives type-2 and fuzzy assembly line balancing problem type-E. To the extent this has been explored, only one study has been published by Paksoy et al (2013) about fuzzification of hybrid model of DLBP by means of fuzzy binary target programming. Kalayci et al (2015) have presented Hybrid Discrete Artificial Bee Colony (HDABC) new algorithm to solve multiple objective FDLBP problem. In their study, they have assessed the fuzzy effect on computational complexity of HDABC and compared the quality of solution of their own suggested algorithm with discrete and traditional artificial bee colony algorithm.…”

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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“…A new approach based on PSO with a neighborhood-based mutation operator was proposed in [25] to solve sequence-dependent disassembly line balancing problem (SDDLBP). The artificial bee colony (ABC) algorithm was used to make decisions regarding energy-savings for DLBP [26], and its fuzzy extension, the hybrid discrete ABC algorithm, was proposed to deal with the uncertainty of the real-world disassembly systems [27].…”

Section: Introductionmentioning

confidence: 99%

Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm

Feng

Zheng

et al. 2018

Energies

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With environmental pollution and the shortage of resources becoming increasingly serious, the disassembly of certain component in mechanical products for reuse and recycling has received more attention. However, how to model a complex mechanical product accurately and simply, and minimize the number of components involved in the disassembly process remain unsolved problems. The identification of subassembly can reduce energy consumption, but the process is recursive and may change the number of components to be disassembled. In this paper, a method aiming at reducing the energy consumption based on the constraints relation graph (CRG) and the improved ant colony optimization algorithm (IACO) is proposed to find the optimal disassembly sequence. Using the CRG, the subassembly is identified and the number of components that need to be disassembled is minimized. Subsequently, the optimal disassembly sequence can be planned using IACO where a new pheromone factor is proposed to improve the convergence performance of the ant colony algorithm. Furthermore, a case study is presented to illustrate the effectiveness of the proposed method.

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Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm

Cited by 110 publications

References 40 publications

An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

An Artificial Bee Colony Algorithm Based on a Multi-Objective Framework for Supplier Integration

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

Sequence Planning for Selective Disassembly Aiming at Reducing Energy Consumption Using a Constraints Relation Graph and Improved Ant Colony Optimization Algorithm

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