An Enhanced Artificial Immune System Approach For Assembly Line Balancing Problem Through Shifting Bottleneck Identification - Khalid, Mohd Nor Akmal (2018)
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.