The optimal assignment of facilities to locations is a combinatorial problem that remains unsolved. None of the several optimal-producing procedures is computationally feasible for any but small problems. Three previously proposed heuristic techniques (craft, one by Hillier, one by Hillier and Connors) and one new one (Biased Sampling) are examined and experimentally compared for problems of from five departments to 30 departments. The new Biased Sampling procedure is seen to produce the best solutions but at relatively high computational cost. The Hillier-Connors procedure is especially interesting because it is considerably faster than craft and Biased Sampling and its solutions are only 2–6 per cent worse than Biased Sampling and only slightly worse than craft.

This article postulates that the ability of an information system to respond to the evolving needs of its users is a necessary ingredient for its success. The concept is exemplified through INF*ACT, an evolving and heavily utilized marketing information system.

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Much discussion apd writing about "systems" is often superficial, glib, and of a non-operational nature. This paper presents a cohesive framework for the process of system design which is felt to be of operational value in the design and management of systems.The framework's central tenet is that all systems are made up of entities and the relationships among the attributes of those entities; and that in designing systems, two processes are used, inclusion and structuring. Inclusion is concerned with what entities to include in a system and structuring with how their attributes are to be related. The system design process is directed or driven by a set (system) of criteria. Systems must be viewed with a time dimension: evolution will and should take place, both in the systems themselves and, more importantly, in the criteria that drive the design and evolution of those systems. In this framework, all problem solvers can usefully regard themselves as system designers.Presentation of the framework is followed by examples, discussion of the criteria set, system design tools, and some implications for teachers and practitioners.

Evolution in models for problem solving is inevitable and desirable; as goals are achieved (or problems solved) new, more fundamental or telling, problems are identified leading to model revisions and better problem solutions. The speed of the evolution is dependent on two major factors: the inherent flexibility of the model and relationship of the model user to the model designer. This paper focuses on these two factors in the modeling environment. It is critical to note that the direction or course of the evolution can only be determined by users since they have the relevant goals, objectives, or criteria; expert model builders are much more capable of problem solution than of problem formulation or identification.