Khalid, Mohd Nor Akmal
(2018)
An Enhanced Artificial Immune System
Approach For Assembly Line Balancing
Problem Through Shifting Bottleneck
Identification.
PhD thesis, Universiti Sains Malaysia.
Abstract
The manufacturing industry has evolved rapidly in the past few years, due to the
global competitive economy, high-quality market demands, and customized products
with the lowest possible costs. This is achieved by partitioning the workloads among
the available resource to obtain an equal amount of workloads in the assembly line
system, which defines the assembly line balancing (ALB) problem. The most
prominent ALB problem is the simple assembly line balancing (SALB) problem
which has been utilized for decades to provide a basis for testing different approaches.
Despite varieties of computational techniques have addressed the ALB problem,
which can be categorized as exact, heuristic, and meta-heuristic approaches, little
work had been done on SALB-E problem due to its difficulty of obtaining the optimal
solutions. Additionally, bottlenecks can still occur during the assembly operations
that affect the production quality and induce unnecessary costs. Identifying and
optimizing machines with the likelihood of the next operation bottleneck had been
rarely addressed in the assembly line especially when it shifts from one machine to
another (called shifting bottleneck). This study propose an effective computational
approach to address the SALB-E problem through the shifting bottleneck
identification. A bio-inspired approach had been frequently adopted for handling
complex and combinatorial optimization problem through a simple yet effective
manner. As such, a computational method, known as artificial immune system (AIS)
approach, had been proposed.
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