Intelligent real time optimization system for line-balancing control in apparel manufacture

The assembly line configuration and balancing control in apparel manufacture relies heavily on human judgment, although with the advanced systems installed. Consistent decisions and optimal solutions are difficult to obtain and/or maintain under dynamic and uncertain manufacturing environments. The decision-making process is further complicated by the human factors of operatives. An Intelligent Real-time Optimization Decision Support System (IRODSS) is thus developed to assist the supervisors for assembly line balancing control by providing optimal resources re-allocation solutions considering the impact of operator efficiency variance and other dynamic factors. A general assembly line balancing (GALB) problem is addressed in this thesis for an automatic unit production system (AUPS) which produces single products in stochastic processing time with a hybrid line structure. The optimization aims to obtain the following objectives: "to maximize the line efficiency, to minimize the standard deviation of operation efficiency and to minimize the operation efficiency waste". Two stages of optimization, namely, pre-line balancing prior to production and real time line balancing during production, are proposed for fulfilling the above objectives. Pre-line balancing control is designed for system initialization via an optimal line configuration through an in-depth study of the impact of systems factors on the production process. But even initialized to the optimal balanced status, the assembly line will turn into imbalanced over time due to uncertain factors. Real-time balancing control, thus, is brought forward for solving the continuous line reconfiguration and/or rebalancing problem in real time basis production through sequential operator allocation optimization based on the prediction of real-time operator efficiency. The developed IRODSS can represent the complexity of real situation because it considers the relationship of different types of uncertainties (e.g. the variance of operator efficiency) that may exist in a dynamic environment and their impact on line efficiency and line status. It is an autonomous temporal intelligent system as it is capable of monitoring the change of work environment (i.e. the change of flow line status) and providing time-based decisions to improve the line balance status sequentially and automatically. These features make it different from other expert systems.