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Master production scheduling in uncertain environments
In the third model, we extend the second model by considering a problem with several bases. There are no repair facilities at the bases. However, there is a depot repair facility which consists of repairmen that are able to repair any type of module. Our objective throughout is to calculate the steady-state operating characteristics of the system. The usual Markovian approach leads to multidimensional state spaces that are large and difficult to solve even for relatively small problems. Consequently, we propose an approximation technique that allows us to solve large problems relatively quickly. Although the resulting solution is only approximate, a variety of test problems indicates that the algorithm is quite accurate. (Order Number DA9012165, June 1990.)
Master Production
Scheduling
in Uncertain Environments
NENG-PAI LIN, PH.D. THE OHIO STATEUNIVERSITY, 1989, 182 PP. ADVISOR: LEE J. KRAJEWSKI
In uncertain environments, the master production schedule (MPS) is usually developed using a rolling schedule. When utilizing a rolling schedule, the MPS is replanned periodically and a portion of the MPS is frozen in each planning cycle. The cost performance of a rolling schedule depends on two decisions: the choice of the replanning interval (R), which determines how often the MPS should be replanned, and the choice of thefrozen internal (F), which determines how many periods the MPS should be frozen in each planning cycle. This dissertation uses an analytical approach to study the master production scheduling process in uncertain environments without capacity constraints, where the MPS is developed using a rolling schedule. It focuses on the choices of F and R for the MPS. In Chapter 1, a conceptual framework which includes all important MPS time intervals is described. The effects of F and R on system costs are also explored. In Chapter 3, a heuristic model for the MPS is presented. This model approximates the expected average system cost as a function of F, R, and other environmental factors. The system costs include the forecast error, MPS change, setup, and inventory holding costs. This model can be used to estimate the associated system costs for any combination of F and R. In Chapter 4, the performance of this model is evaluated. The effects of the choices of F and R are also investigated. Guidelines for managing the MPS in uncertain environments are developed. In Chapter 5, the research is extended to the environments with multiple items. The procedure for finding the best common F and R for all items is described. The consequences on system costs due to using common F and R for all items are also examined. (Order Number DA901 1216, June 1990.)
432
Vol. 9, No. 3
Master Production
Scheduling
in Uncertain Environments
NENG-PAI LIN, PH.D. THE OHIO STATEUNIVERSITY, 1989, 182 PP. ADVISOR: LEE J. KRAJEWSKI
In uncertain environments, the master production schedule (MPS) is usually developed using a rolling schedule. When utilizing a rolling schedule, the MPS is replanned periodically and a portion of the MPS is frozen in each planning cycle. The cost performance of a rolling schedule depends on two decisions: the choice of the replanning interval (R), which determines how often the MPS should be replanned, and the choice of thefrozen internal (F), which determines how many periods the MPS should be frozen in each planning cycle. This dissertation uses an analytical approach to study the master production scheduling process in uncertain environments without capacity constraints, where the MPS is developed using a rolling schedule. It focuses on the choices of F and R for the MPS. In Chapter 1, a conceptual framework which includes all important MPS time intervals is described. The effects of F and R on system costs are also explored. In Chapter 3, a heuristic model for the MPS is presented. This model approximates the expected average system cost as a function of F, R, and other environmental factors. The system costs include the forecast error, MPS change, setup, and inventory holding costs. This model can be used to estimate the associated system costs for any combination of F and R. In Chapter 4, the performance of this model is evaluated. The effects of the choices of F and R are also investigated. Guidelines for managing the MPS in uncertain environments are developed. In Chapter 5, the research is extended to the environments with multiple items. The procedure for finding the best common F and R for all items is described. The consequences on system costs due to using common F and R for all items are also examined. (Order Number DA901 1216, June 1990.)
432
Vol. 9, No. 3