Density Stochastic Approximation of Initial Buffer Decision Problem in Continuous Production Lines

In this paper, we study the initial butter decision (IBD) problem for a class of periodic production lines and the stochastic approximation (SA) algorithm for the problem. We establish the generalized semi-Markov process (GSMP) model of production line and the formulation of the IBD problem that is a bi-objective stochastic optimization problem. Considering the high computation cost of objective functions under middle scale stations, we employ SA algorithm to solve the optimization problem. To improve the performance to search the solutions on the boundary of feasible decision set, we propose an modified SA algorithm, called density stochastic approximation (DSA). Numerical results show SA algorithm is effective for the problem and DSA outperform SA and other two well-known variants of SA on searching the solutions on the boundary.