Optimal allocation of work in assembly lines for lots with homogenous learning
This paper deals with the problem of allocating work to the stations of an assembly line to minimize the makespan of a lot of products with a low overall demand. There is no buffer permitted in between the stations, and the line operates under homogeneous learning (i.e., under the same learning rate for all stations). We show that in the presence of learning, the optimal makespan requires imbalanced allocation of work to stations. The level of savings in the optimal makespan value due to the imbalanced loading of work over the balanced loading case are demonstrated as a function of the value of the learning constant, number of stations on the line as well as lot size. These savings can be quite significant under the case of low overall demand.
learning assembly line balancing nlp optimization