A new scheme of case-based decision support systems by using DEA and GA techniques

We propose a new scheme for case-based decision support systems (DSS) using data envelopment analysis (DEA) and genetic algorithms (GA). The application field for our scheme is cases of multiple input-output activity in which the efficiency of the outputs is evaluated. The case-based DSS offer activity-policy which reflects any level, both of the efficiency and features in the activity, referring to many past cases in the same area. Our scheme is based on two procedures, an analysis procedure and an estimation procedure. In the analysis procedure, all the cases are recursively evaluated by solving the modified model of DEA, i.e., the generalized BCC model. The remaining cases, except for cases belonging to the efficiency frontier, are also evaluated by the generalized BCC model, and the processing is repeated. After the analysis procedure, it is possible to classify the cases into multiple hierarchies by level of efficiency, and also into the groups with common features between inputs and output, which cover multiple hierarchies. In the estimation procedure, according to past cases, features, frontier levels, and required conditions, any solution set of future activity plans is searched by a GA with a fitness function using these factors. After the estimation procedure, the user controls the variety of required conditions about past cases of activity, and finally chooses the future plan.