Paper Title
Solving Parallel Mixed-Model Assembly Line Balancing Problem Under Uncertainty Considering Resource-Constrained Project Scheduling Techniques

When designing an assembly line, one of the most important and challenging problems is the line balancing problem. While considering the limitations of machinery in parallel mixed model line layouts, and probabilistic uncertainty of task durations, this study seeks to reduce the number of stations required in assembly lines. Being an NP-hard problem, the mathematical model cannot be solved to optimality for the real-world problems. A heuristic algorithm is used to solve the problem that takes advantage of the similarity of the assembly line balancing problem with the resource constraint project scheduling problem. The Particle Swarm Optimization algorithm is used to explore the feasible region. Monte-Carlo simulation method is applied to consider the uncertainties and their effect on the optimum number of stations. The proposed model and the solution method are evaluated by well-known data set available in the literature. Obtained results show the merits of the proposed model and the solution method. Keywords - mixed-model assembly line, balancing, stochastic optimization, parallel line, resource constraint project scheduling, particle swarm optimization algorithm