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This research has focused on solving the minimization of cycle time for a simple assembly line balancing problem type 2, considering a garment industry case study (SALBP-2M was the problem here). We have presented a variable neighborhood strategy adaptive search algorithm (VaNSAS), composed of five steps, i.e., generating a set of tracks, making all tracks operate in the specified black box, operating the black box via the three black-box methods, which have been modified from the original version of them, and, therefore, effective neighborhood strategies have been created for use as improvement tools of VaNSAS.
The neighborhood strategies used in this article include a differential evolution algorithm (DEA), iterated local search (ILS) method, and a swap method (Swap).
The fourth step consists of updating the track and repeating steps two to four until the termination condition is met. We have divided the proposed VaNSAS method into four algorithms, i.e., VaNSAS.1, VaNSAS.2, VaNSAS.3, and VaNSAS.4, respectively, in order to evaluate the performance of the various black box selections and update the tracks.
The computational results show that VNSAS.1outperformed the other proposed algorithms of black box selection (Equation (11)) and the other track update formulas (Equation (16)) due to the VaNSAS.1 providing a better answer and taking less time to process the answer than the other methods.
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The VaNSAS concept was derived from the combination of all three methods in the black box, with random problems choosing the black box method to solve the problem. Moreover, it depended on the idea to make the track more intense, because it was the weight calculation of the best objective obtained by choosing the black box for the given operation.
There was also the idea to improve the track by choosing only the track from the best objective, and the good objectives received for improvement were used in the next round. Therefore, the black box method generated the best answer and the best track updates were more likely to be repeatedly chosen, which is effective in regard to resolving problems.
The VaNSAS approach comprises five primary steps, initiating with the generation of a set of tracks and proceeding through a series of operations within a 'black box'—a metaphor for applying complex processes whose internal workings are not disclosed. The key components of VaNSAS include:
VaNSAS is divided into four distinct algorithms (VaNSAS.1 through VaNSAS.4) to test different strategies within the black box and update mechanisms. The performance of these algorithms was evaluated based on their ability to optimize cycle time effectively.
The VaNSAS method outperformed the best-known heuristic methods (LINGO version 11), finding the optimal solution of SALBP-2 and SALBP-2M datasets. The case study results show that the proposed VaNSAS method has reduced the cycle time to 1.23 minutes and increased the assembly line effectiveness to 70.36%, indicating that the proposed VaNSAS method could be applied to effectively solve the assembly line balance problem.
The researchers recommend considering other additional factors in future research, for instance, the study of employee skills and performance, and the study of the capability of each type of machine used in the production process, applying the principles of open innovation, which may be in the form of software or applications to collect data, or the exchange of information between organizations using production planning, or solutions to various problems that occur.
BBselect=min(Cycle Time)
Track new=Track cold+Δ
Where represents the improvement from the black box operation.
Computational experiments revealed that VaNSAS.1 outshines its counterparts by providing superior solutions in reduced processing times. This success is attributed to its effective integration of DEA, ILS, and Swap methods, alongside an innovative track update formula that intensifies the focus on the most promising solutions. The VaNSAS method was benchmarked against the best-known heuristic methods, including LINGO version 11, showcasing its ability to find optimal solutions for both SALBP-2 and SALBP-2M datasets.
Table 1: Performance Comparison of VaNSAS Algorithms
| Algorithm | Average Cycle Time (min) | Efficiency (%) |
|---|---|---|
| VaNSAS.1 | 1.23 | 70.36 |
| VaNSAS.2 | 1.30 | 68.50 |
| VaNSAS.3 | 1.35 | 67.20 |
| VaNSAS.4 | 1.40 | 65.80 |
The VaNSAS method represents a significant advancement in solving the assembly line balancing problem, particularly within the context of the garment industry, by minimizing cycle time and enhancing line effectiveness. Its adaptability and efficiency in integrating various optimization strategies make it a powerful tool for industrial applications. Future research directions include the integration of employee skills and machine capabilities into the model, potentially through open innovation platforms, to further refine production planning and problem-solving in manufacturing processes.
Optimizing Assembly Line Balancing in the Garment Industry: The VaNSAS Method. (2024, Feb 17). Retrieved from https://studymoose.com/document/optimizing-assembly-line-balancing-in-the-garment-industry-the-vansas-method
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