This paper summarizes information about a method, called sample-path optimization, for optimizing performance functions in certain stochastic systems that can be modeled by simulation. We explain the method, give conditions under which it converges, and display some sample calculations that indicate how it performs. We also describe briefly some more extensive numerical experiments on large systems (PERT networks with up to 110 stochastic arcs, and tandem production lines with up to 50 machines). Details of these experiments are reported elsewhere; we give references to this and other related work. We conclude with some currently unanswered questions
