Resource-allocation behavior in complex but commonplace tasks

We all face resource-allocation tasks, yet little is known about how people make resource-allocation decisions. Three earlier papers (Langholtz, Gettys, & Foote, 1993, 1994, 1995) established that people can intuitively solve technical two-dimensional resource-allocation problems, where allocations are made on a continuous scale, reaching solutions that approach the optimum solution determined with Linear Programming (LP). In this research we expand our understanding of how people make resource-allocation decisions by examining how people perform complex but commonplace problems using a discrete scale where optimal solutions are determined with Integer Programming. We also contrast performance under conditions where progressively unequal scheduling is required to reach the optimum solution. The data show people can achieve solutions that provide most of the payoffs found with LP, even when they are faced with a complicated three-dimensional problem, but there is a tendency toward equal scheduling under all conditions. (C) 1997 Academic Press.