Multiple Item Capacitated Random Yield Systems
Two main contributors to the uncertainty of production systems are capacity and random yield; therefore, strategies are needed that incorporate both random yield and the increased effect due to capacity restrictions. This paper utilizes dynamic programming and linear programming transformation to provides a method to specify an optimal decision for a given inventory state. In doing so, it can be empirically shown that the structure of the optimal policy is not an order-up-to policy. Using this method, one only needs to solve a small subproblem, which using LP transformation can be done using commercially available LP solvers without concern for solution time. These results may improve the decision-making capabilities of real-time complex environments since the emphasis is on developing policy rules that are easy to implement in manufacturing applications, as well as service industries such as airlines, healthcare and education. Additionally, the results may be used as a means of evaluating (bounding) existing approximation methods.
S. E. Grasman, "Multiple Item Capacitated Random Yield Systems," Computers & Industrial Engineering, Elsevier, Aug 2009.
The definitive version is available at https://doi.org/10.1016/j.cie.2008.11.009
Engineering Management and Systems Engineering
University of Missouri Research Board
Keywords and Phrases
Capacity; Random Yield; Inventory control; Production planning
Article - Journal
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