NCJ Number
163893
Journal
IIE Transactions Volume: 28 Dated: (1996) Pages: 735-744
Date Published
1996
Length
10 pages
Annotation
This paper proposes a Bayesian acceptance sampling approach for the problem of random drug testing in the transportation industry.
Abstract
The recent tightening of industry standards by the Department of Transportation requires that firms operating in the transportation industry test 25 percent of their employees for drug use each year. This provides an incentive to evaluate the effectiveness of ad hoc random approaches to drug testing, particularly since the testing is estimated to cost the industry $200 million annually. The proposed model recognizes the dependence of a Bayesian acceptance sampling approach on the prior distribution of users in the population and on the outcome of the test itself. The approach offers a minimum expected total cost solution and decision rule for testing, based on the optimal sampling plan derived, which may then be used to determine future testing schedules and outcomes. The comparative cost of sampling plans derived with the Bayesian approach are compared with that obtained with a random, non-economic approach. The results show that use of an economic approach can generate savings from 8 percent to 90 percent. The approach is applied to the Los Angeles County Metropolitan Transit Authority as a method of monitoring and randomly testing a population of 4,000 bus drivers. Compared with its existing approach and using cost inputs provided by the Authority, acceptance sampling would allow a significant increase in the amount of testing possible and provide a more proactive drug-testing policy toward drivers who use drugs. 4 tables and 12 references