Open Access Dissertation
Production planning--Mathematical models; Production scheduling--Mathematical models;
Production planning and control (PP&C) are among the most critical activities in manufacturing. Proper use of PP&C methods can give organizations a competitive advantage in the global economy. The expected results of this research will allow manufacturing organizations to maximize the effectiveness of PP&C methods, thereby improving their competitive position in the global economy.
This research was an extension of a previous unpublished study, which investigated the PP&C methods being used at a midwestern manufacturer of agricultural equipment (MMAE). The current research study identified the constraints inherent in the production planning and control system and then developed and validated a master production scheduling and sequencing optimization model based on constraints management and utilizing genetic algorithms.
The specific objectives of this research were as follows: (a) identify the system's constraint, (b) develop a scheduling and sequencing model to address the identified constraints, (c) develop and validate the proposed model by simulation, and (d) identify and document improvements attributed to the operational change resulting from the implementation of the optimization model.
The research examined the impact of the master production scheduling and sequencing model based on constraints management and utilizing genetic algorithms on five variables for the final assembly line and four downstream processes at an engine manufacturing plant of a MMAE. The variables were cycle time, queue size, utilization of work centers, flow rate of engines, and total output of engines.
A two-part model, based on constraints management philosophy of production planning and control methods, was developed by the researcher in Excel, one part for scheduling and the other for sequencing. Using data from 100 production days during the fall of 1999 and the spring of 2000, simulations for the current scheduling and sequencing method (the control condition) and for the proposed method (the experimental condition) were compared. Output from the simulations for the experimental and control conditions was statistically analyzed.
The results of this research indicated (a) cycle time for the experimental condition was reduced, but the reduction was not statistically significant; (b) queue size for the experimental condition was also reduced, as expected, but once again, the reduction was not statistically significant; (c) total utilization of work centers was increased, as expected, and the increase was statistically significant; (d) the experimental condition's simulation results indicated very minimal improvements for the even flow of engines; and (e) the average total number of engines processed for the experimental condition was increased, as expected, and the increase was statistically significant.
Year of Submission
Doctor of Industrial Technology
Department of Industrial Technology
Mohammad F. Fahmy, Faculty Advisor
MD. Salim, Co-Advisor
1 PDF file (xii, 202 pages)
©2000 Ahmad Nadeem Choudhry
Choudhry, Ahmad Nadeem, "A model for production scheduling and sequencing using constraints management and genetic algorithm" (2000). Dissertations and Theses @ UNI. 743.