A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

Küçükkoç, İbrahim; Buyukozkan, Kadir; Satoğlu, Şule Itır; Zhang, Zhengwen

doi:10.1007/s10845-015-1150-5

Cited by 30 publications

(10 citation statements)

References 63 publications

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“…Pastor et al [ 48 ], proposed a new heuristic method to solve the mentioned lexicographic bottleneck ALBP by using the local search algorithm, where the initial solution was generated by another heuristic procedure. In recent years, Kucukkoc et al [ 49 ] proposed an artificial bee colony method for the lexicographic bottleneck mixed-model ALBP. Considering the mentioned lexicographic methods, in this study, the potentially feasible solutions are enumerated in the lexicographic order, which is different from the approach utilized in the mentioned studies.…”

Section: Introductionmentioning

confidence: 99%

A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors

Shavarani

Nejad

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors

Shavarani

Nejad

et al. 2023

Heliyon

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“…While there has been a great deal of research done on many aspects of CNC machine control and productivity interruptions, none have addressed the down time in product return issues, they have not addressed a quantitative, prognostic, predictive method to improve the service provider's performance. Previous research verifies Adaptive Control (AC) has been introduced as an effective method of optimizing machining parameters in the past [11,12]. More recently, the implementation of fuzzy logic models for predicting and controlling part condition has been investigated and implemented as a method of improving the machined part quality of key components of the machine.…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components

Kevin¹

2021

AJCST

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The nucleus of this concept and system is directly focused on a 'computer numerical control' (CNC) turret lathe and milling machine tool systems. These concepts focus specifically to this category of engineered systems. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. A quantitative system identifies Key Predictive Attributes (KPA's), which identifies a new concept application technique and applies quantitative methods to these attributes to develop a systemic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines). The specific key predictive attributes are the elements being utilized in the newly created modular function in this research, to assess the potential impact of discrete elements of these attributes as it affects the occurrence of equipment down time for a system which will work to quantify the service quality of the maintenance process. This project is unique in that currently there is no system which utilizes methods or tools, that proactively gather, analyze, assess, and project outcomes of equipment "Down Time" of the Service Quality process. The innovative position of this analysis is one of actual variable tolerances, versus a more traditional nominal referenced variable reference. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimate of service outcomes. Another unique aspect of this method is that it will establish tangible tolerances to assess the performance of the Design Review and Service Quality process and not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) will be programmatically developed based upon the system data. This system tool will develop programming algorithms which will propel this current process from a subjective qualitative process to become a robust quantitative projection tool. The novelty in this research is the development of a quality index through the creation of the new Moriarty/Ranky Transform approach.

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“…Significant research effort has been made in modelling and developing heuristic solution approaches to solve the realistic problems. See for example, Erel and Gokcen (1999), Yuan et al (2008), Emde et al (2010), Xu and Li (2013), Kucukkoc et al (2015), Li et al (2016), Delice et al (2017) and Saif et al (2017) for recently studies published on diverse mixed modes. The accuracy of algorithm in ALBP will affect the production efficiency of assembly line, so it has become the focus of most scholars.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al (2016) introduced an improved particle swarm optimization algorithm (PSO) aiming at the current assembly line balancing problem type II (ALBP-II). PSO mentioned by Delice et al (2017) and artificial bee colony algorithm (ABC) proposed by Kucukkoc et al (2015) and Saif et al (2017) are effective in view of the different assembly line balancing problems.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable assembly line balancing for cloud manufacturing

Yuan

Huang

et al. 2018

J Intell Manuf

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In an attempt to react to the increasing imbalance of assembly line due to the high uncertainty of assembly resources in the cloud manufacturing environment, this study investigates the reconfigurable assembly line balancing problem (ALBP) in a cloud manufacturing environment based on the actual production process. We designed the assembly precedence relation model on the basis of analyzing the characteristics and categories of the reconfigurable ALBP. Thereafter, an optimization model of ALBP under traditional mode is established. Combined with the dynamic and collaborative operation of cloud manufacturing, a workstation information sharing framework for cloud manufacturing is designed, and an equilibrium optimization model of ALBP in cloud manufacturing environment is developed to obtain the maximum productivity and the minimum the load smoothness. Moreover, an improved memetic algorithm is proposed to solve the optimization model, which has strong global and local search capabilities compared with the general algorithm. Finally, performance of the proposed approach is tested on a set of examples, and distinguished results can be acquired by comparing with particle swarm optimization algorithm, simulated annealing and genetic algorithm.

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A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem

Cited by 30 publications

References 63 publications

A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors

A novel competitive exact approach to solve assembly line balancing problems based on lexicographic order of vectors

Life Cycle Analysis of Computer Numerical Control (CNC) Machine Components

Reconfigurable assembly line balancing for cloud manufacturing

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