A simulation based decision support system for resouce planning Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/w6634583t

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  • The problem of resource planning has long been a topic of interest for operations managers in both production and service industries. Inputs such as raw materials, energy, and capital are limited resources; therefore, managers must make informed decisions regarding the use of these inputs. This thesis addresses the area of man-power resource planning by developing a simulation based decision support tool to aid in the evaluation of alternatives during the decision making process. The problem of resource planning was first brought to my attention through the Motor Vehicle Division (DMV) Systems and Planning Section in Salem, Oregon. They had conducted a study of field offices in the summer of 1987 in a continuing effort to improve customer satisfaction. One area that was identified as in need of further study was the receptionist function. A simulation program was developed using the SLAM simulation language to model customer activities in DMV field offices. The program has a front end that allows model variables to be input by the user. The user specified variables include customer arrival rates, number of available servers, number of available receptionists and definition of the receptionist function. The program also executes the simulation for the user and includes a back end that displays relevant statistics. The program was applied to DMV field offices comparing the effects of a receptionist versus another worker serving customers. It was found that at busy times (high customer arrival rates) the use of a receptionist significantly lowers waiting times. Sensitivity analysis was also conducted on three of the more uncertain variables relating to the receptionist function: service time, percent turn away and percent MVR service time reduction. It was found that waiting times and queue lengths were very sensitive to these variables.
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  • File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0.82 on a Canon DR-9080C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
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