Modeling Product Variety Induced Manufacturing Complexity for Assembly System Design.

Mixed-model assembly systems have been recognized as a major enabler to handle product variety. However, the assembly process can become quite complex as the variety increases. The complexity may impact the system performance in terms of quality and productivity. This dissertation considers the variety induced manufacturing complexity in manual, mixed-model assembly lines, develops models for the propagation of complexity, and applies the models to assembly system design. A complexity measure called “Operator Choice Complexity” (OCC) is proposed to quantify human performance in making choices. The OCC is an entropy measure of the average randomness in a choice process. Meanwhile, empirical evidences support the proposed complexity measure. Based on the OCC, models are developed to evaluate the complexity at each station, and for the entire assembly system. The system level model, termed “Stream of Complexity” (SoC) model, addresses complexity propagation in multi-stage systems. By applying the SoC model, complexity can be mitigated with system design and operation decisions. The models are then applied to assembly sequence planning and build sequence scheduling. Assembly sequence planning is to determine the order of assembly tasks. According to the SoC model, assembly sequence determines the directions of complexity flows. Thus proper sequence planning can reduce complexity. However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming. Build sequence scheduling is to determine the order of products being built. It also determines the sequential dependencies between the choices. According to the complexity model, the knowledge of the sequential dependencies can help operators make choices, and scheduling proper build sequences can reduce complexity. However, deterministic models are not sufficient to study such sequence scheduling problems. A probabilistic model based on hidden Markov chains is proposed to formulate the scheduling problem with constraints. Analytical solutions are obtained, and suggest that proportional production attains the maximum complexity while batch production attains the minimum. The results of the research are highly applicable to all manufacturers who are interested in economically offering product variety without loss of quality and productivity.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/62265/1/xwzhu_1.pd