A SCHEDULING SEQUENCING ALGORITHM FOR MIXED-MODEL ASSEMBLY LINES

Today many industries used mixed-model assembly lines to cope with consumers demand for a large variety of products. As orders arrive at the factory they are assigned a production due date. If meeting these due dates were the only scheduling goal First In First Out scheduling would suffice. However, the scheduling algorithm presented in this thesis is aimed at smoothing inventory requirements as well as meeting due dates. This algorithm can also be used for mixed-model assembly lines sequencing.;The work elements, tools, parts, and sub-assemblies required by an assembly operation may vary from model to model. The cost of such multitask operations will be an uneven demand for labor, parts, and sub-assemblies. To keep inventory queues to a minimum, it is necessary that demand be as smooth and predictable. Also, by dampening perturbation in demand the feeder operation will operate more efficiently.;The existing technique such as line balancing, and sequencing are based on line efficiency. They are adequate for what they bring to their particular pieces of an assembly system, but they in no way integrate operations (i.e. from purchasing to delivery of finished goods) so that a factory can run like an assembly line. The Just-In-Time (JIT) philosophy popularized by the Japanese concentrates on the integration of operations as essential for manufacturing efficiency. To make JIT production a reality it is necessary that production flow as smoothly as possible. The Japanese begin with uniform plant loading (UPL).;The goal of this thesis is to promote UPL while maintaining the integrity of due dates. Smoothing the daily production schedule will benefit the manufacturing process in many ways. First, the parts and components required by the assembly process will be more uniform on a day-to-day basis. This will help eliminate waste by reducing inventories required. Secondly, the average model-mix used in the line balancing process will more closely represent the daily schedule, resulting in a more accurate and efficient line balance. Lastly, the daily schedule is more likely to avoid the overloading of high or low work content models.