Design of a Resource Allocation Planning System

This paper proposes a generic human-computer software user interface design, called the Resource Allocation Planning System (RAPS), designed to support a person making resource allocation decisions. Although there are many algorithms for automatically solving resource allocation problems, it is often the case that human judgment is also required. Also, while there are software user interfaces to support decision-making for specific resource allocation problems, most of them serve more as organizational charts than as decision-support systems, and most of them become increasingly difficult to use as the size of the resource allocation problem increases. This paper discusses the design and rationale for RAPS and gives an example of how RAPS can be adapted to a specific resource allocation problem. Rationale and Objectives Resource allocation is a common type of task that requires complex cognitive reasoning; it requires assigning assets to demands given a set of constraints. A decision-support system (DSS) can reduce cognitive demands by organizing information effectively and making aspects of a task that are always true automatically executable, therefore allowing a decision maker to focus cognitive resources on those aspects of the task that require human input and judgment. For example, when a user is trying to make a decision based on a set of options, the cognitive workload on the decision-maker can be reduced by limiting the options presented to only those options that are feasible. Furthermore, the difficulty of a cognitive task can be decreased (or increased) depending on the tools and representations available to the decision maker for tracking information and task completion. Effective representations provide decision makers with an “external memory” (e.g., information is represented directly “in the world”), thereby eliminating the need to remember that information “in the head”. Ineffective representations, on the other hand, require the operator to remember important constraints, relations, procedures, etc. “in the head’ while working on a problem, adding cognitive burden and increasing the likelihood of errors. This is known as the Representational Effect (Zhang and Norman, 1994).