A nonlinear multiple criteria model base for the problem of balancing caseload risk in probation departments

This paper presents a combined statistical model for probationer risk classification and nonlinear multi-criteria programming resource allocation model for workload balancing. A statistical model of individual probationer failure was used to classify individuals as high, medium or low risk for probation purposes. The proportion of probationers in each risk category was used as an input into the integer, nonlinear resource allocation model where the decision variables were the number of visits to probationers by type of visit and risk classification.