ESTIMATING EMPLOYMENT DISCRIMINATION LIABILITY WITH A DETERMINISTIC SIMULATION*

This paper presents an employment discrimination liability model that estimates back-pay liability for an employer's discriminatory employment practices by simulating employment practices under assumptions of fairness. Comparison of the racial/sexual composition of the simulated fair work force with the racial/sexual composition of the employer's actual work force permits identification of cases of discrimination. The model suggests that the choice of a stock or flow approach will have a significant effect on the amount of employer liability.     

An Experimental Comparison of Dispatching Rules for Field Service Support*

Field service repair management is increasing in importance as “high tech” machines such as computers, communication systems, and copy machines are becoming more popular and more widely disbursed geographically. Complicating the management of these systems are response time performance guarantees (e.g., 8-hour response time), which are a popular marketing tool for many field service businesses [21]. These response time), guarantees especially complicate the field service dispatching problem. With response time guarantees, dispatchers must consider travel times and other issues to determine which call should be assigned to an available technician. This research proposes several dispatching rules that might be applied for dispatching sequential-trip technicians in this environment and compares these rules in a simulation experiment to determine which rule is best in terms of average tardiness. The proposed composite travel time-expiration time rule was found to be the best on average tardiness performance and on all other tardiness-related measures. Given the increased importance of tardiness performance in field service organizations, the proposed composite rule appears to have promise.     

The Use of Stochastic Simulation in Knowledge-Based Systems

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems.