New chance-constrained models for U-type stochastic assembly line balancing problem

Abstract

U-shaped assembly lines are widely encountered in contemporary JIT systems. Unlike presumptions of deterministic studies, task times may vary according to a probability distribution. In this study, a stochastic U-type assembly line balancing problem (ALBP) is considered. For this purpose, two new chance-constrained nonlinear models are proposed. While the first model belongs to the mixed-integer programming (MIP) category, the other is constraint programming (CP). The linearized chance-constrained counterparts are developed using a transformation approach to reduce the model complexity and solve the models linearly. Several numerical experiments are performed to test the effectiveness of the proposed models. The results are compared with the results of modified ant colony optimization and a piecewise-linear programming model. The numerical results demonstrate that the proposed CP and MIP models are more effective and successful in solving stochastic U-type ALBP.