The type-II assembly line rebalancing problem considering stochastic task learning

Li, Yuchen

doi:10.1080/00207543.2017.1346316

Cited by 39 publications

(13 citation statements)

References 24 publications

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“…Re-balancing costs comprise sum of task transposition costs, operating costs of workstations and workstation opening/ closing costs. Li (2017) proposed an algorithm that is called ENCORE for type-II assembly line re-balancing problem considering stochastic task learning. Zhang et al (2018) presented two-sided assembly line re-balancing problem with modifications of production demand, line's structure and production process.…”

Section: Previous Researchmentioning

confidence: 99%

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

Seri̇n

Mete

Çelik

2019

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Purpose Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

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Section: Previous Researchmentioning

confidence: 99%

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

Seri̇n

Mete

Çelik

2019

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“…The case studied in the present work is the SALBP-2 in which, given a number of stations, maximising efficiency is achieved by minimising the cycle time. This case matches a line rebalancing problem which has been specifically studied in [6,7] among others, as it considers the number of stations from a previous line design, but technology or production changes alter the duration of the tasks, forcing the rebalancing of the line.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations

et al. 2021

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The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid method to solve the simple version of the problem in which the number of stations is fixed, a problem known as SALBP-2. The hybrid differs from previous approaches by encoding individuals of a genetic algorithm as instances of a modified problem that contains only a subset of the solutions to the original formulation. These individuals are decoded to feasible solutions of the original problem during fitness evaluation in which the resolution of the modified problem is conducted using a dynamic programming based approach that uses new bounds to reduce its state space. Computational experiments show the efficiency of the method as it is able to obtain several new best-known solutions for some of the benchmark instances used in the literature for comparison purposes.

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“…Cil et al (2017) proposed beam search based heuristic algorithm to solve the robotic mixed-model assembly line balancing problem. Li (2017) proposed a new algorithm named ENCORE to address the Type-II assembly line balancing problem with stochastic task time. proposed a hybrid genetic algorithm to solve the Type-II mixed-model assembly line balancing problem with interval task time.…”

Section: Introductionmentioning

confidence: 99%

An improved flower pollination algorithm for solving a Type-II U-shaped assembly line balancing problem with energy consideration

Zhang

2020

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Purpose The increasing energy shortage leads to worldwide attentions. This paper aims to develop a mathematical model and optimization algorithm to solve the energy-oriented U-shaped assembly line balancing problem. Different from most existing works, the energy consumption is set as a major objective. Design/methodology/approach An improved flower pollination algorithm (IFPA) is designed to solve the problem. The random key encoding mechanism is used to map the continuous algorithm into discrete problem. The pollination rules are modified to enhance the information exchange between individuals. Variable neighborhood search (VNS) is used to improve the algorithm performance. Findings The experimental results show that the two objectives are in conflict with each other. The proposed methodology can help manager obtain the counterbalance between them, for the larger size balancing problems, and the reduction in objectives is even more significant. Besides, the experiment results also show the high efficiency of the proposed IFPA and VNS. Originality/value The main contributions of this work are twofold. First, a mathematical model for the U-shaped assembly line balancing problem is developed and the model is dual foci including minimized SI and energy consumption. Second, an IFPA is proposed to solve the problem.

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The type-II assembly line rebalancing problem considering stochastic task learning

Cited by 39 publications

References 24 publications

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times

A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations

An improved flower pollination algorithm for solving a Type-II U-shaped assembly line balancing problem with energy consideration

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