Balancing stochastic type-II assembly lines: chance-constrained mixed integer and constraint programming models

Abstract

In the literature, line balancing is mostly investigated in deterministic environments. But production systems inevitably contain stochastic situations. In this study, the stochastic type-II assembly line balancing problem (ALBP) is considered. Firstly, a chance-constrained nonlinear mixed integer programming (MIP) formulation is developed from the well-known deterministic form. Then, a new linearized stochastic model is proposed by using some transformation approaches to reduce model complexity, and the model is solved. Finally, constraint programming (CP) models for deterministic ALBPs, nonlinear chance-constrained stochastic ALBPs and linearized chance-constrained stochastic ALBPs are developed, respectively. Problems from the literature are utilized to test the effectiveness of the proposed models and the results are compared with a bidirectional heuristic algorithm. The numerical results show that the CP models are more effective and successful for solving the stochastic ALBP. Some managerial implications are also suggested for industrial environments that consistently face ALBPs.