A multi-objective genetic algorithm for mixed-model assembly line rebalancing

Yang, Caijun; Gao, Jie; Sun, Lei

doi:10.1016/j.cie.2011.11.033

Cited by 68 publications

(37 citation statements)

References 31 publications

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“…Boysen, Fliedner, and Scholl (2009a) list three capacity adjustment methods: reconfiguration, time adjustment and subcontracting. Altemeier et al (2010) and Yang, Gao, and Sun (2013) consider the MAL reconfiguration as a rebalancing problem. Simaria, Zanella de Sá, and Vilarinho (2009) and Li and Gao (2014) assume that the unstable demand during the planning horizon can be characterised by several representative scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Balancing mixed-model assembly lines with adjacent task duplication

Yang

Gao

2014

International Journal of Production Research

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When a mixed-model assembly line (MAL) is balanced, it is generally assumed that the variants of a task over different models should be assigned to an identical station. In this study, this restriction is relaxed and the variants of a task over different models can be duplicated on two adjacent stations (referred to as adjacent task duplication) to improve the MAL's efficiency. The adjacent task duplication incurs few additional training and tool duplication costs as each task is duplicated on at most two stations. Moreover, for each task, the assembly part storage is not duplicated as it can be shared by the two adjacent stations. The mathematical model of this problem is formulated and some important properties are characterised. A branch, bound and remember algorithm is then developed to solve the problem. The performance of the proposed algorithm is tested on 900 representative instances, of which 889 instances are optimally solved. The experimental results show that the use of the adjacent task duplication policy effectively reduces the number of stations, especially when the WEST ratios are small. IntroductionAssembly lines are flow-oriented production systems that are typical in the industrial production of high-quantity standardised commodities (Becker and Scholl 2006). The labour productivity of assembly lines is largely driven by how the tasks (indivisible elements of work) are assigned to stations subject to technological constraints, commonly referred to as assembly line balancing (ALB) (Battaïa and Dolgui 2013). The simple assembly line balancing problem (SALBP) aims to minimise the number of stations given the cycle time (type I) or the cycle time given the number of stations (type II) (Baybars 1986).The mixed-model assembly line (MAL) is a more complex environment in which several product models are assembled simultaneously in intermixed sequences (Thomopoulos 1967). MALs are widely used in many industries such as automobiles, white goods and consumer electronics, where demand is characterised by high variability and relatively small volume for each model (Becker and Scholl 2006). A MAL generally assembles different versions of one basic product rather than completely different items. Hence, many studies assume that a single precedence diagram can be drawn to resume the precedence relationships among different tasks over each model (Merengo, Nava, and Pozzetti 1999). However, the processing time of a task may differ over the various models, and each variant of the task over these models should be regarded as a separate work element (Boysen, Fliedner, and Scholl 2008). The MAL balancing problem (MALBP) involves assigning all the task variants to stations.A straightforward approach for modelling and solving MALBPs is to restrict all the variants of a task over different models to an identical station. This restriction is reasonable, considering that these task variants may require the same skills, tools or assembly parts (Thomopoulos 1967). With this restriction, MALBPs are reduced to some version of...

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Section: Literature Reviewmentioning

confidence: 99%

Balancing mixed-model assembly lines with adjacent task duplication

Yang

Gao

2014

International Journal of Production Research

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“…Bunlar; üretim, işçilik, envanter ve kurulum maliyetleri gibi maliyet fonksiyonları [21]- [23], hat etkinliğinin maksimizasyonu [22], [23], belirlenen süre içinde tamamlanan işlerin maksimizasyonu veya tamamlanamayan işlerin minimizasyonu [22]- [25], işlem gören ürün sayısı, çıktı hızı, sistem akış zamanı ve kullanım oranı gibi sistemin performans değerleri [26]- [28]. Bunlara ek olarak, Manavizadeh ve diğerleri [29] Sezgisel çalışmalar içinde meta sezgisel kullanan çalışmalara bakıldığında ise genetik algoritma kullanan çalışmaların daha fazla olduğu görülmektedir [6], [10], [12], [15], [17], [18], [29], [30]. Bunun yanında tavlama benzetimi [22], [13], karınca kolonisi optimizasyonu [23], [16], tabu arama [14] ve demet arama (Beam search) [9] algoritmaları kullanılmıştır.…”

Section: Literatür Araştırmasıunclassified

A constraint programming model for mixed model type 2 assembly line balancing problem

Alakaş¹,

Pınarbaşı²,

Yüzükırmızı³

et al. 2016

Pamukkale J Eng Sci

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“…Doostparast et al (2014) planned a reliability based periodic preventive maintenance (PM) for a system with deteriorating components. Yang et al (2013) addressed a rebalancing problem for a mixed-model assembly line (MAL) with seasonal demands. Ardakan and Hamadani (2014) considered the mixed-integer non-linear optimization-redundancy allocation problem to determine simultaneous reliability and redundancy level of components.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis and performance optimization of a butter oil production system: a case study

Aggarwal

Singh

Kumar

2014

Int J Syst Assur Eng Manag

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This paper proposes a method for time dependent system availability (TDSA) analysis of butter oil production system and the performance of the system is optimized using genetic algorithm. The mathematical formulation of the system based on Markov birth-death process is carried out and first order Chapman-Kolmogorov differential equations are developed based on the assumption that the failure and repair rate of each subsystem follows exponential distribution. The proposed method i.e. an adaptive step-size control RungeKutta method is used to compute TDSA and mean time between failures for butter oil production system of a dairy plant. The butter oil production system is a complex system and comprises of seven repairable subsystems. The results are presented and discussed with the plant personnel to adopt and practice suitable maintenance policies/strategies to enhance the productivity and quality of butter oil.

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A multi-objective genetic algorithm for mixed-model assembly line rebalancing

Cited by 68 publications

References 31 publications

Balancing mixed-model assembly lines with adjacent task duplication

Balancing mixed-model assembly lines with adjacent task duplication

A constraint programming model for mixed model type 2 assembly line balancing problem

Availability analysis and performance optimization of a butter oil production system: a case study

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