A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Lian, Kunlei; Zhang, Chaoyong; Gao, Liang; Shao, Xinyu

doi:10.1080/00207543.2011.653453

Cited by 39 publications

(9 citation statements)

References 26 publications

(26 reference statements)

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“…Mamun et al (2012) consider some particular features, such as the use of parallel workstations, zoning constraints and resource limitations. Kara and Tekin (2009), Hwang and Katayama (2010) and Lian et al (2012) balance U-shaped MALs. Chutima and Chimklai (2012) and Kucukkoc and Zhang (2014) consider twosided MALBPs.…”

Section: Literature Reviewmentioning

confidence: 96%

“…Hence, the simultaneous consideration of both decisions is only viable under very special conditions (Hwang and Katayama 2010;Kucukkoc and Zhang 2014;Lian et al 2012;Mosadegh, Ghomi, and Zandieh 2012). When a MAL is balanced, it is difficult to evaluate the system throughput because the line may encounter blockage and starvation at the subsequent sequencing stage due to station variability.…”

Section: Literature Reviewmentioning

confidence: 99%

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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: 96%

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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“…Another paper is that of a hybrid imperialist competitive algorithm for minimizing make understand its operation [15]. In the field of metaheuristics, ICA is based on political and human social progress, unlike other evolutionary metaheuristic algorithms, based on behaviors of animals or physical phenomena [16,17]. Figure 1 shows the flowchart of the imperialist competitive algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…All countries will eventually become a state in which there is only one empire in the entire world and all remaining countries are colonies of that empire [19,20]. social progress, unlike other evolutionary metaheuristic algorithms, based on behaviors of animals or physical phenomena [16,17]. Figure 1 shows the flowchart of the imperialist competitive algorithm.…”

Section: Introductionmentioning

confidence: 99%

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

et al. 2017

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Abstract:In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

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“…Imperialist Competitive Algorithm (ICA) is a new evolutionary algorithm based on the meta-heuristic of human's socio-political evolution. It was proposed by Atashpaz-Gargari and Lucas in 2007 [10], and since its inception, it has been applied to solve many optimization problems in scheduling [11][12][13][14][15], classification [16,17] and machinery design [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Interaction Enhanced Imperialist Competitive Algorithms

Lin

Tsai

et al. 2012

Algorithms

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Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. It divides its population of solutions into several sub-populations, and then searches for the optimal solution through two operations: assimilation and competition. The assimilation operation moves each non-best solution (called colony) in a sub-population toward the best solution (called imperialist) in the same sub-population. The competition operation removes a colony from the weakest sub-population and adds it to another sub-population. Previous work on ICA focuses mostly on improving the assimilation operation or replacing the assimilation operation with more powerful meta-heuristics, but none focuses on the improvement of the competition operation. Since the competition operation simply moves a colony (i.e., an inferior solution) from one sub-population to another sub-population, it incurs weak interaction among these sub-populations. This work proposes Interaction Enhanced ICA that strengthens the interaction among the imperialists of all sub-populations. The performance of Interaction Enhanced ICA is validated on a set of benchmark functions for global optimization. The results indicate that the performance of Interaction Enhanced ICA is superior to that of ICA and its existing variants.

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A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Cited by 39 publications

References 26 publications

Balancing mixed-model assembly lines with adjacent task duplication

Balancing mixed-model assembly lines with adjacent task duplication

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Interaction Enhanced Imperialist Competitive Algorithms

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