A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system

Abstract

In this study, which is both analytical and numerical, we compute the effective information horizon (EIH), i.e., the minimal time interval over which future information is relevant for optimal control and for measuring the performance of a single part-type production system. Optimal control modeling and process solving, which consider aspects of decision making with limited forecast, are exemplified by a single parttype production system. Specifically, the analysis reveals practical situations in which there is both a performance loss as well as feasibility violation when only information expected within the planning horizon is considered. The analysis is carried out by developing a pseudo-stochastic model. We follow previous ''pseudo-stochastic'' approaches that solve stochastic control problems by using deterministic, optimal control methods. However, we model the expected influences of all future events, including those that are beyond the planning horizon, as encapsulated by their density functions and not only by their mean values.

The critical points of production rate under vendor–buyer coordination: explicit solutions

“…Moon and Cha (2005) developed an inventory model under which, at the time of entering into a contract with the manufacturer, the retailer can negotiate the lead time by considering the production rate of the manufacturer who usually has the option of increasing his regular production rate up to the maximum (designed) production capacity. Herbon and Khmelnitsky (2009) developed a model that seeks an optimal control function (i.e., transient production rate), which minimizes a performance measure along the planning horizon. Optimal control modeling and process solving, which consider aspects of decision making with a limited forecast, are exemplified by a single-part type production system.…”