A New Self-Balancing Assembly Line Based on Collaborative Ant Behavior

Koo, Pyung-Hoi

doi:10.3390/app10196845

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…The last step to design a line is the assignment of tasks to workstations [46]. Each model (k) can be considered as a separate SMALBP because the time devoted to each product (TP k ) is now known (as well as the minimum number of workstations).…”

Section: Balancing the Multi-model Linementioning

confidence: 99%

Balancing Workload and Workforce Capacity in Lean Management: Application to Multi-Model Assembly Lines

et al. 2020

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While multi-model assembly lines are used by advanced lean companies because of their flexibility (different models of a product are produced in small lots and reach the customers in a short lead time), most of the extant literature on how to staff assembly lines focuses either on single-model lines or on mixed-model lines. The literature on multi-model lines is scarce and results given by current methods may be of limited applicability. In consequence, we develop a procedure to staff multi-model assembly lines while taking into account the principles of lean manufacturing. As a first approach, we replace the concepts of operation time and desired cycle time by their reciprocal magnitudes workload and capacity, and we define the dimensionless term of unit workload (load/capacity ratio) in order to avoid magnitudes related to time such as cycle time because, in practice, they might not be known. Next, we develop the necessary equations to apply this framework to a multi-model line. Finally, a piece of software in Python is developed, taking advantage of Google’s OR-Tools solver, to achieve an optimal multi-model line with a constant workforce and with each workstation performing the same tasks across all models. Several instances are tested to ensure the performance of this method.

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Section: Balancing the Multi-model Linementioning

confidence: 99%

Balancing Workload and Workforce Capacity in Lean Management: Application to Multi-Model Assembly Lines

et al. 2020

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“…Assembly line balancing (ALB) is a crucial part of production/operations management [2]. On most mass-production assembly lines, workers repetitively perform a set of tasks predefined by using assembly line balancing techniques [3], which assign a number of work elements to various workstations to maximize the assembly line's balancing efficiency [4]. A balancing of tasks across the workstations results in the optimization of a given objective function without violating preceding constraints [5].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Arranging Operators to Balance Assembly Lines and Reduce Operator Training Time

2021

Applied Sciences

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Industry 4.0 is transforming how costs, including labor costs, are managed in manufacturing and remanufacturing systems. Managers must balance assembly lines and reduce the training time of workstation operators to achieve sustainable operations. This study’s originality lies in its use of an algorithm to balance an assembly line by matching operators to workstations so that the line’s workstations achieve the same targeted output rates. First, the maximum output rate of the assembly line is found, and then the number of operators needed at each workstation is determined. Training time is reduced by matching operators’ training and skills to workstations’ skill requirements. The study obtains a robust, cluster algorithm based on the concept of group technology, then forms operator skill cells and determines operator families. Four numerical examples are presented to demonstrate the algorithm’s implementation. The proposed algorithm can solve the problem of arranging operators to balance assembly lines. Managers can also solve the problem of worker absences by assigning more than one operator with the required skillset to each workstation and rearranging them as needed.

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“…The topics covered span many diverse areas associated with the design and management of production lines, such as: production improvement in uncertain environments [1], design of production lines to introduce switch-off policies [2], new technologies [3], statistical data analysis [4,5].…”

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confidence: 99%