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A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system
Author(s) -
Herbon Avi,
Khmelnitsky Eugene
Publication year - 2010
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.2009.00720.x
Subject(s) - time horizon , interval (graph theory) , aggregate (composite) , mathematical optimization , computer science , production (economics) , optimal control , horizon , control (management) , stochastic control , production planning , process (computing) , stochastic modelling , production control , aggregate planning , stochastic process , operations research , mathematics , economics , statistics , artificial intelligence , materials science , geometry , combinatorics , composite material , macroeconomics , operating system
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 part‐type 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.