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Title: Modelling and solving mixed-model parallel two-sided assembly line problems
Author: Kucukkoc, Ibrahim
ISNI:       0000 0004 5372 8986
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2015
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The global competitive environment and the growing demand for personalised products have increased the interest of companies in producing similar product models on the same assembly line. Companies are forced to make significant structural changes to rapidly respond to diversified demands and convert their existing single-model lines into mixed-model lines in order to avoid unnecessary new line construction cost for each new product model. Mixed-model assembly lines play a key role in increasing productivity without compromising quality for manufacturing enterprises. The literature is extensive on assembling small-sized products in an intermixed sequence and assembling large-sized products in large volumes on single-model lines. However, a mixed-model parallel two-sided line system, where two or more similar products or similar models of a large-sized product are assembled on each of the parallel two-sided lines in an intermixed sequence, has not been of interest to academia so far. Moreover, taking model sequencing problem into consideration on a mixed-model parallel two-sided line system is a novel research topic in this domain. Within this context, the problem of simultaneous balancing and sequencing of mixed-model parallel two-sided lines is defined and described using illustrative examples for the first time in the literature. The mathematical model of the problem is also developed to exhibit the main characteristics of the problem and to explore the logic underlying the algorithms developed. The benefits of utilising multi-line stations between two adjacent lines are discussed and numerical examples are provided. An agent-based ant colony optimisation algorithm (called ABACO) is developed to obtain a generic solution that conforms to any model sequence and it is enhanced step-by-step to increase the quality of the solutions obtained. Then, the algorithm is modified with the integration of a model sequencing procedure (where the modified version is called ABACO/S) to balance lines by tracking the product model changes on each workstation in a complex production environment where each of the parallel lines may a have different cycle time. Finally, a genetic algorithm based model sequencing mechanism is integrated to the algorithm to increase the robustness of the obtained solutions. Computational tests are performed using test cases to observe the performances of the developed algorithms. Statistical tests are conducted through obtained results and test results establish that balancing mixed-model parallel two-sided lines together has a significant effect on the sought performance measures (a weighted summation of line length and the number of workstations) in comparison with balancing those lines separately. Another important finding of the research is that considering model sequencing problem along with the line balancing problem helps algorithm find better line balances with better performance measures. The results also indicate that the developed ABACO and ABACO/S algorithms outperform other test heuristics commonly used in the literature in solving various line balancing problems; and integrating a genetic algorithm based model sequencing mechanism into ABACO/S helps the algorithm find better solutions with less amount of computational effort.
Supervisor: Zhang, David Z. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: assembly line balancing ; parallel assembly lines ; two-sided assembly lines ; model sequencing ; agent-based systems ; ant colony optimisation ; genetic algorithm ; artificial intelligence ; production planning ; optimisation