This thesis investigates the interactions of several characteristics of serial production lines that contribute to production line performance. A full factorial experimental design of computerized simulations is conducted with three levels of downtime, four levels of variability, three levels of line length, three levels of constraint location, three levels of work-in-process, and six levels of protective capacity. This study enlarges upon recent four-workstation investigations and extends the knowledge to longer production lines. Some generalizations for the amount and location of protective capacity are drawn from the results, as a guide for process improvements and new production line design. An approximating regression model is constructed for prediction of cycle time outcomes with various values of contributing factors.

Manufacturing systems have developed both physically and technologically, allowing production of innovative new products in a shorter lead time, to meet the 21st century market demand. Flexible flow lines for instance use flexible entities to generate multiple product variants using the same routing. However, the variability within the flow line is asynchronous and stochastic, causing disruptions to the throughput rate. Current autonomous variability control approaches decentralise the autonomous decision allowing quick response in a dynamic environment. However, they have limitations, e.g., uncertainty that the decision is globally optimal and applicability to limited decisions. This research presents a novel formula-based autonomous control method centered on an empirical study of the effect of stochastic variability on the performance of flexible human-dependent serial flow lines. At the process level, normal distribution was used and generic nonlinear terms were then derived to represent the asynchronous variability at the flow line level. These terms were shortlisted based on their impact on the throughput rate and used to develop the formula using data mining techniques. The developed standalone formulas for the throughput rate of synchronous and asynchronous human-dependent flow lines gave steady and accurate results, higher than closest rivals, across a wide range of test data sets. Validation with continuous data from a real-world case study gave a mean absolute percentage error of 5%. The formula-based autonomous control method quantifies the impact of changes in decision variables, e.g., routing, arrival rate, etc., on the global delivery performance target, i.e., throughput, and recommends the optimal decisions independent of the performance measures of the current state. This approach gives robust decisions using pre-identified relationships and targets a wider range of decision variables. The performance of the developed autonomous control method was successfully validated for process, routing and product decisions using a standard 3x3 flexible flow line model and the real-world case study. The method was able to consistently reach the optimal decisions that improve local and global performance targets, i.e., throughput, queues and utilisation efficiency, for static and dynamic situations. For the case of parallel processing which the formula cannot handle, a hybrid autonomous control method, integrating the formula-based and an existing autonomous control method, i.e., QLE, was developed and validated.