Utilizing variant differentiation to mitigate manufacturing complexity in mixed-model assembly systems

• The current complexity research in mixed-model assembly systems makes a restrict assumption about identical parallel stations.
• We extend the existing complexity model to general mixed-model assembly systems with non-identical parallel stations in the presence of product variety.
• We develop a mathematical formulation to minimize the complexity of a mixed-model assembly system.
• We solve the formulated problem, a non-linear programming problem, by genetic algorithm.
• We apply the developed complexity mitigation model to the configuration selection of assembly systems.

A measure of product variety induced complexity has been proposed for mixed-model assembly systems with serial, parallel and hybrid configurations. The complexity model was built based on the assumption of identical parallel stations, i.e., same product variants are produced at all parallel stations in the same volume and with the same mix ratio. In this paper, the existing complexity model is extended to general mixed-model assembly systems with non-identical parallel stations in the presence of product variety. Then it is discussed that how to reduce the system complexity using the variant differentiation, based on which a mathematical formulation is developed to minimize the complexity of a mixed-model assembly system. The formulated problem is a non-linear programming problem and then solved by genetic algorithm. Last the developed complexity mitigation model is applied to the configuration selection of assembly systems, i.e., to identify the system configuration with the minimum complexity.