A Tunable Generic Meta-Heuristic Framework for Balancing Assembly Line Systems in Manufacturing

Cyber-Physical Systems controlling assembly line operations are central to manufacturing processes. Assembly line systems have diversified over time, depending on multiple factors, including the products being manufactured, the workstations and resources used, factory layouts, and so on. This diversity in assembly line configurations has added layers of complexity to the Assembly Line Balancing Problem (ALBP). While many powerful meta-heuristic techniques exist, their performance can vary significantly depending on the specific characteristics of the ALBP instance, such as the structure of the precedence graph, the distribution of task times, and the number of workstations. Recognizing the need for a more versatile solution, this article introduces a generic local search strategy called Flexible Meta-Heuristic (FMH), which includes a set of adjustable tuning parameters for adapting to specific scenarios. FMH combines and extends the strengths of Hill Climbing (HC), Simulated Annealing (SA), and Genetic Algorithm (GA) to provide effective solutions across a wide range of problems. Through extensive experiments using standard benchmarks and randomly generated datasets, FMH demonstrates high accuracy, deviating by at most 0.9% from best-known benchmark values. Additionally, FMH is significantly less resource-intensive, solving problems with up to 150 tasks in minutes where exact solvers can take hours, making it more scalable and applicable to large industrial scenarios. Our findings suggest that the algorithm’s flexibility and strategic hyper-parameter tuning contribute significantly to its effectiveness in solving diverse ALBPs.