that are totally man-made, e.g., communication networks ... Finally, we feel DEDS is primarily a man-made system versus a ... Apollo moon landing problem.
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Editorial Basic Research, Manufacturing Automation, and Putting the Cart Before the Horse* ODERN day technology has increasingly created systems that are totally man-made, e.g., communication networks and computer integrated manufacturing systems. Such systems clearly are dynamic in nature as they evolve in time; yet they cannot in general be easily modeled by the familiar system of ordinary or partial differentialldifference equations. We call such systems discrete event dynamic systems (DEDS). The recent position paper “Challenges to control” [ l ] specifically identifies DEDS as an important future research topic. The point here is the pervasive nature of such systems in the modern world and the relative lack of a good analytical and dynamical oriented model for their description. One cannot overemphasize the importance of a well-established modeling framework or paradigm such as the one found in continuous variable dynamic systems (CVDS) using differential equations. It permeates the entire scientific culture, facilitates cross disciplinary communications, and influences our approach to problems. I submit it is precisely this lack of commonly agreed upon paradigm that underlies the relatively primitive analysis and synthesis effort we have with DEDS. While there has been no lack of attempts at constructing general models for DEDS, one thing everyone agrees on is that no consensus has developed as to which one of the models has the potential to eventually serve as the analog of the differential equation paradigm for CVDS. Among these modeling efforts we count: Markov chainslautomation models-to this group we also assign Pem nets, extended state machines; queueing network models; min-max algebraic models; and generalized semi-Markov process models (this category includes all efforts related to general discrete event simulation languages). Thereare several reasons for the lack of a good modeling framework for DEDS. They can be understood by an examination of the desiderata for a DEDS model:
M
i) ii) iii) iv) v) vi)
the discontinuop nature of discrete events; the continuous nature of most performance measares; the importance of probabilistic formulation; the need for hierarchical analysis; the presence of dynamics; the feasibility of the computational burden.
Space does not permit a detailed discussion of all of these items. Suffice it to say that no model to date satisfies all six criteria. Finally, we feel DEDS is primarily a man-made system versus a physicallnatural system. As such, it interacts more with humans than with nature. A controlled but unmanned spacecraft to Mars is interacting mostly with nature (Newton’s Law of motion and gravitation, the electromagnetic spectrum, etc.). On the other hand, a flexible manufacturing system even under computer control is interacting more with human operators through display screens or actual material handling. This has serious implications in practical implementation of the DEDS theory in two respects. First, AI interface and natural language processing ability may be a much more important factor in DEDS than in CVDS. In
* With due apology to Professor P. Elias and his famous 1956 editorid in the IEEE TRANSACTIONS ON INFORMATION THEORY entitkd “Religion, photosynthesis,andinformation theory.” Anyresemblance between the following and the Elias editorial is unintentional and purely coincidental.
manufacturing automation, for example, DEDS are required to interface with personnel of lower skill levels than with those operating aerospace vehicles. Secondly, since DEDS are totally man made, there are no invariant physical laws to constrain system configurations. Consequently it is doubtful that we can visualize a day when all solutions to all DEDS problems can be reduced to an algorithm. Some form of rule-based solutions approach will probably be a necessary part of a general DEDS tool chest. If physics is the science of nature and things, then operations research is properly concerned with the science of event and operations. This, in fact, was how OR was originally conceived. As a scientific discipline itmust have both experimental and theoretical components. DEDS research is no different. Carefully designed experiments accumulate evidence upon which a relevant theory can be built. Conversely, analytical reasoning pinpoints further experiments to conduct for validation. Without this “observation-conjecture-experiment-theory-validation” cycle to inspire and to guide research, I submit a totally axiomatic mathematical development of DEDS is not likelyto yield practical results at this stage of DEDS development [2]. However, before everyone rushes headlong into research in DEDS, itis instructive to review the growth of control and systems theory since the late 1950’s. At that time a confluence of events gave birth to the explosive growth of the discipline. The nation was jolted by Sputnik and resolved to excel in aerospace. Digital computation has progressed to such a degree that a problem is considered to have been solved when it can be reduced to another problem that can be routinely solved on a digital computer, e.g., solving a system of nonstiff ordinary differential equations or inverting a well conditioned matrix of “moderate” dimension. Lastly, the time domain concepts of state space and the tools of linear algebra and probability turned out to beideally suited to attack the then outstanding aerospace problem, i.e., the Apollo moon landing problem. The Kalman-Bucy filter, the LQG solution, the Kelley-Bryson variational optimization procedure solved some REAL engineering problems in the early 1960’s. The rest, as the saying goes, is history. To give another example from a related discipline, the field of mathematical programming owes its continuing existence to the early successes of linear programming (in the 1940’s and 1950’s) and the simplex algorithm as applied to resource allocation problems in the real world. On the other side, look at the subject of game theory or, more close to home, differential games. Although inspired by real world problems, the development of the subject matter quickly turned mathematical. The result is that one cannot seriously claim to have solved any real problem of importance in either subject. I am not minimizing the intellectual and conceptual contributions in game or differential game theory; nor am I hegrudging the need for society to support a certain amount of such theoretical work as “art for art’s sake” or for “serendipity” reasons. What we need to recognize is this. If we as a group of professionals profess to do basic research in engineering or applied work, then itis a necessary precondition for substantial support that we first commit ourselves to solve some pressing problems of the society. After that is done, the profession can then be funded on a large scale to do basic or applied research. Otherwise, support will naturally wither away to a minimum as it has happened in game or
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differential game theory. Note that this isnot a debate about problems in manufacturing automation. The situation is in fact theory versus application but about what is a fruitful way to build worse in some sense because manufacturing problems will be a scientific versus a mathematical theory. much harder than that of landing on the moon. Without a serious This lesson of history is stressed here because of the emergence commitment to solve the problems first, it is unlikely that of the subject of DEDS and its application to manufacturing serendipity will allow us to produce an applicable theory in a automation. In our need to seek funding and find greener pastures, vacuum. It is then all too likely that the young discipline of DEDS it is all too easy to repeat the history of differential games. A few will atrophy. A few good people will always be supported pseudo-real problems will be addressed and lip service to regardless, but a majority of our able young researchers will have manufacturing application will be given in every opening para- been led down the primrosepath. Not only will we have not graph of papers on DEDS. In actuality, we rush headlong into the helped in solving problems of national importance, but also we process of formalization and mathematization of DEDS models will have wasted human resources. Thus, I sincerely urge anyone without any regard to what the real problems are. By itself, who is contemplating committing to the DEDS field: Make a this is not necessarily bad. As mentioned before, the society will commitment to solve some realproblemsfirst.Afterthat, support a certain amount of highly creative work regardless of its relevant theory will emerge naturally. Then andonly then you practical utility. After all, civilization cannot just be to feed, cloth, can do your theoretical research to your heart’s content. and sustain the masses. But by the same token, someone must REFERENCES sweep the streets, run the power stations, manufacture the goods and otherwise keep the life sustaining machineries and bureaucra[I] A. Levis et ai., “Challenges to control-A collective view,” IEEE cies of the society running. Support of a few highly creative Trans. Automat. Contr., vol. AC-32, pp. 274-285, Apr. 1987. individuals does not mean support of everyone to do the same. [Z] Y. C. Ho, “Editorial-Is it application or is it experimental science?,” IEEE Trans. Automat. Contr., vol. AC-27, p. 1142, Dec. 1982. The control systems field enjoyed a whole generation of support only after we successfully solved the pressing aerospace problems of the 1960’s. To expect the same kind of support for research in Yu-CHI Ho, DEDS we must first commit ourselves to solve some real pressing Associate Editor at Large
