A simple heuristic for computing nonstationary (s, S) policies

Nonstationary inventory problems with set-up costs, proportional ordering c osts, and stochastic demands occur in a large number of industrial distribu tion, and service contexts. It is well known that nonstationary (s, S) poli cies are optimal for such problems. In this paper, we propose a simple, myo pic heuristic for computing the policies. The heuristic involves approximat ing the future problem at each period by a stationary one and obtaining the solution to the corresponding stationary problem. We numerically compare o ur heuristic with an earlier myopic heuristic and the optimal dynamic progr amming solution procedure. Over all problems tested, the new heuristic aver aged 1.7% error, compared with 2.0% error for the old procedure, and is on average 399 times as fast as the D.P. and 2062 as fast as the old heuristic . Moreover, our heuristic, awing to its myopic nature, requires the demand data only a few periods into the future, while the dynamic programming solu tion needs the demand data for the entire time horizon-which are typically not available in most practical situations.