Albert 1. Klee
Bureau of Solid Waste Management Department of Health, Education, and Welfare Cincinnati, Ohio 45213
Tactics, strategy: the solid waste battle
W
orkers in the field of the management of solid wastes are frequently overwhelmed by a plethora of management terms-systems analysis, operations research, cost-benefit analysis, etc. These concepts and methodologies describe a little understood revolution in the approach to solutions of complex problems brought about by the growth of our population. The purpose of this article is twofold: To provide a basic familiarity with the more widely used of these terms, their objectives, and the concepts upon which they are based. To relate these concepts briefly to the reality of today’s solid waste collection and disposal problems. By consensus, Frederick W. Taylor is the father of scientific management. Between 1895-1904, he introduced three new principles into the management function: Tradition and hit-and-miss methods were to be abandoned. Aotions formerly left to chance were to become the subject of planning. *Planning and doing were to be separated. Taylor’s ideas-as well as those of Wellington, Emerson, Shewhart, and others-marked the beginning of the scientific approach to management problems that culminated in what we have come to know as classical industrial engineering. Operatlons research
The new managers, primarily concerned with production, made valuable contributions toward improving the efficiency of their operations. But they were usually incapable of bringing to bear the sophisticated mathematical ability required to solve S98 Envi-1
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complex problems involving many variables. It remained to those mathematicians and physicists who tackled operational problems during World War I1 to clearly demonstrate that mathematical solutiom could be obrtained for extremely complex mlanagement problems. Operations research, therefore, justifiably can claim a measure of significant activity prior to World War 11. But it emerged as a recognizable entity only when the concepts and techniques of the physical sciences and mathematics were applied systematically to the operating problems of military organizations. The operations research approach was based upon two precepts: First, the objective was to examine a complete system, not a restricted portion of it, and, subsequently, to optimize the system. Second, the method of attaining this goal was to field a mixed team of professionals-the interdisciplinary approach-utilizing quantitative techniques and scientific methods. The successes of this method were somewhat astonishing, and it was natural that operations research was applied to the problems of industrial organizations after World War 11. The immediate effect was to broaden the point of view from which industrial problems were approached. Examination of every pertinent aspect of an operation was one of the new and dominant features of operations research. Research workers then wrestled with the philosophic problem of how big a system should be before it profitably could be considered as such. In the meantime, more and more practitioners formally considered themselves as operations researchers, &IN whittling away at the mixed team
concept. Operations research retained intact a hard core of methodologyh e a r programming, queuing theory, routing algorithms, mathematical modeling, and the like-but, eventually, limited itself to systems in which inputs were treated as constants. Systems analysis
After World War 11, a new a p proach evolved, called systems analysis, in which inputs are considered as variables. Thus, where operations research supplied the tactics, systems analysis supplied the strategy. On a prior but parallel path, the science of economics addressed itself to the trade-off of costs and benefits under conditions of variable inputs, utilizing its own peculiar jargon-marginal rate of substitution, indifference curves, budget lines, and so on. The structures of both benefits and costs are complex and elusive, and do not always reduce to traditional scales of profits and losses. Therefore, the scope of cost-benefit analysis extended beyond classical microeconomic theory and, ultimately, was incorporated as a major technique of systems analysis. In time, the problems which formed the raison d’etre of systems analysis became more complex, and study teams became larger. Since we have alluded to the methodology of operations research and cost-bene& analysis, it would be helpful at this point to illustrate and compare them. Consider first the classical operations research technique known as linear programming. Suppose that in a given solid wastes collection system, A, there are two resources, XIand X,-equipment and manpower, for instance-that reflect unit net costs, c1 and c2, respectively. Total cost,
feature
Without adequate operations research, systems analysis methodology is only an academic exercise
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R, then equals clXl czXz. The technology of the system, however, dictates that it is not possible to operate at combinations of X, and X, other than those points on or above the nonclosed polygon abc:
C
M W J t .
The minimum costs lines are shown for each of four polygons as dotted lines. Let us assume that there exists another collection system, system B, which is a competitor. It is not necessary that systems A and B utilize the same kinds of input resources; it is sufficient that they are competitors. We may now do a cost-benefit analysis of competing systems. The following diagram shows several 4 5 O lines of constant input cost between the two systems:
A number of lines of constant total cost (shown as dotted lines), R, = clXl czX2, have been added to the figure-the higher the line, the greater the cost. We can never obtain R1, however, since there is no point on the R, line where we can operate the system, due to the required minimum combination of X1 and X,. Although we can reach R3 or R4, since there are permissible combinations on them, minimum cost occurs when a cost line just touches the polygon. The line that does this is the Rz line; accordingly, with these fixed system constraints, R, is the minimum cost possible. This is a graphic example, Point M y for example, represents with solution, of the classical linear a total input cost of R,, split equally programming problem of operations between systems A and B; point N researchers. represents a total input cost of R3 Let us reexamine the diagram in a allocated entirely to system B. These different light and assume now that, input costs are assumed to be the although the system configuration is minimum costs derived from the linear such that the basic shape of the polyprogramming technique just outlined. gon must be maintained, it is possible Each input cost produces some beneto alter our resources inputs so as to fit; in the case of system A, it would reduce or enlarge our polygon: be related in some fashion to the
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quantities X1 and Xz. We can, therefore, plot lines of constant benefit, B,, on the diagram. The higher the benefit line, in this case, the greater the benefit. Here benefit functions have been assumed to be linear and are shown as dotted lines. It is now possible for the systems analyst to decide between the competing systems. If, for example, the input cost is held constant at R,, the greatest benefits, B, in the above diagram, are obtained by selecting system A. For the more steeply sloped benefit lines, the greatest benefits are obtained by selecting system B:
Nonlinear
benefit functions can
also be analyzed in the same way:
In this case, greatest benefits are obtained when a combination of the two systems, indicated by the dotted lines, is employed. Volume 3, Number 10. October 1969 899
We have approached this problem from a viewpoint of constant input cost, selecting that system or systems combination which maximizes benefits for a given cost. However, the analysis could have been reversed, by selecting that system or systems combination which minimizes cost for a required level of benefits. In summary, then, this comparison of linear programming and cost-benefit analysis illustrates some salient differences between operations research and systems analysis. The question is now posed: What can be expected of systems analysis as it pertains to problems of solid wastes? It is clear that, unless adequate operations research methodology can be developed, systems analysis can be only partially successful. Without the tactics, the strategy is but an academic exercise. Traditional areas
I
LP Terminate
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Language. Simulation is an important tool of systems analysis, and special computer languages such as the General Purpose Simulation System (GPSS) make the task of programming such simulations much easier. This block diagram is a portion of a simulation of a solid waste collection system, written in GPSS 900 Environmental Science & Technology
Many of the traditional areas of operations research are apropos of solid wastes systems. For convenience, but without pretense that the list is exhaustive, these areas may be considered as follows: Allocation studies arise when there are a number of activities to be performed and there are alternative ways of doing them, and when resources or facilities are not available for performing each activity in the most effective way. The problem is one of combining activities and resources in such a way as to maximize overall effectiveness. In a solid wastes collection system for example, decisions have to be made as to when collection vehicles should be dispatched and the number of men assigned per truck. Another type of allocation problem involves the optimum location and sizing of disposal facilities such as landfills cr incinerators. Linear programming is a widely used operations research technique for allocation problems. Waiting-lime problems involve the arrival of units that require service at one or more service points. Usually, waiting is required of either the units needing service or the service units themselves. There are two kinds of waiting line processes. The first involves arrivals not subject to controls, for example, the queues formed by trucks waiting to unload at a municipal incinerator. The problem of schedding preventative maintenance €or collection trucks or street cleaning vehicles is an example of the second
process, facilities fixed but arrivals subject to some control. Simulation techniques have been especially useful in analyzing such situations, although specialized mathematical models are available when the arrival and/or service times follow certain well-known distributions. Routing problems simply involve optimum routing. They differ from sequencing problems in that order is not specified. Given a set of disposal sites and a set of solid wastes generation sources, the routing problem for a collection vehicle bears some resemblance to the so-called traveling salesman problem of operations research: The traveling salesman must visit each of several cities before returning home, in such a manner as to minimize some quantity such as distance traveled or total travel time. Replacement-renewal processes arise when equipment deteriorates with use, becomes obsolete because of new developments, or because the equipment is subject to failure. These are particularly relevant to collection systems in view of the significant investment in vehicles. But there are other applications as well, such as wear of refractory liners in incinerators. Information collection problems occur because the effectiveness of a decision is often directly related to the accuracy of the information on which the decision is based. Inaccurate information generally arises from inaccurate measurements, sampling errors, and estimating errors. Thus, a balance usually is sought between the costs of decision errors and the costs of collecting and analyzing data. For example, in solid wastes systems, important decisions depend upon knowledge of the quantities of wastes generated. Unfortunately, the use of scales is the exception, not the rule, and quantity estimates are often nothing more than bad guesses. Competitive processes are those in which the efficiency of a decision by one party is subject to being decreased by the decision of another party. There are two types of competitive processes. In one approachgames theory-competitors are assumed to know the payoff values corresponding to the various strategies each may make. Another approach involves bidding, where the number of competitors usually is not known, the possible number of plays is very large, and the payoffs and outcomes of a play can only be estimated. By considering
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(1.0-3.0-12.0)
Achieved event Completed activity
-- -- -
Most critical path Other critical path
Deadlines. Program evaluation and review technique (PERT) is used in s y s t e m analysis to quantify knowledge about the uncertainty faced in completing the sequential activities of a project, One of its features is that it focuses attention on those scheduled events that are vital to the achievement of overall program deadlines. This diagram is part of a PERT schedule f o r the design, installation, and testing of a municipal incinerator
Tactics vs. Strategy Operations research
Characteristic
Explains or predicts operation of existing system
Model
Optimize existing systems Treated as constants
Pulrpose System inputs
Systems analysis Provides tradeoffs of cost and effectiveness of competing systems Select characteristics new systems
of
Accepted as variables
Volume 3, Number 10, October 1969 901
nature as a competitor, the whole of decision and value theory can be considered competitive processes. Thus, the solid wastes planner may he in a game against nature, where nature’s impartial strategies may be to iucrease or decrease population, devise an unforeseen technological breakthrough, or to create or destroy a salvage market. Of course, the competitor can he easily recognized, and partial, such as educational or recreational interests vying for funds. Formidable problems
As successful as operations research has been, however, formidable problems are yet to he solved. The Bureau of Solid Wastes Management of the U S . Department of Health, Education, and Welfare, for example, is investigating the feasibility of applying location and routing algorithms to disposal site lobation and collection problems. These studies have shown what the models do not handily take into consideration-that cities are not flat, regular polygons, devoid of traffic, or immune to weather. Indeed, there are some areas in which no general methods for obtaining solutions to its problems have been discussed as yet, like the traveling salesman problem. The assumptions of linearity required for many operations research algorithms are, more often than not, unrelated to reality. Further, there are the problems of obtaining sufficient and reliable data to test and verify proposed mathematical models, and the quantification of things we call benefits and costs. How much is a lung worth? What’s the price of a rat bite? The problem of constructing a single measure of effectiveness in a situation with conflicting objectives is a basic and redoubtable one. If operations research in solid waste problems has a long way to go, then systems analysis has even farther to travel. An example of a realistic systems analysis application might be a cost-benefit Study of competing waste collection systems-the single vehicles versus the container train, or the metal refuse container versus 902
Environmental Science & Technology
plastic bags. Unfortunately, many spectacular systems analysis programs are receiving the lion’s share of attention in solid waste research, in spite of the fact that many operations researchers are by no means convinced that the required technology is available or even just around the corer. P m e n t status
It would appear that systems analysis is presently more a matter of verbalization than anything else. Flow charts and block diagrams seem to he all that is necessary to fiavor an environmental health project or proposal with the nectar of systems analysis. This is to he regretted. If prospective consumers of systems analysis are led to believe that it is nothing more than a throwback to the office and clerical routines of the previous half century, they will quickly search for greener pastures. At the Engineering Foundatio search Conference (Solid Wast search and Development) he Milwaukee, Wis., on July 24-28, P. H. McGaughey,remarked: “Concerning our research activities, we are going full steam to the systems approach. I am particularly interested in our use of this systems approach and our concern for the whole environment. This is a good thing, and I am in it the same as everyone else. However, I am moved to reflect upon the nature of glamour phrases and areas of activity. Some 15 years ago, no researcher could expect to command any serious respect unless he used radioisotopes in his work. Within five years, both isotopes and computers were required, although the former declined in prestige until some of us old country boys were permitted to go hack to hunting rabbits with a hound dog as long as we programmed the activity in Fortran. There followed other glamour phrases-water resources, euvironment engineering, total euvironment, and so forth, and currently, operations research and systems analysis.” In his humorous rebuke, M o Gaughey has sounded a justifiable warning.
Albert J. Klee is chief, operational analysis, Bureau of Solid Waste Management. Previously, he has been a control chemical engineer, developmental chemical. engineer, process equipment engineer, and statistical and mathematical analyst of chemical engineering data in industry. He is also adjunct associate professor in mathematics and management, Xavier University. Klee received his B.S. from CCNY (1950), M.Ch.E., f r o m NYU (19551, M.B.A., f r o m Xavier University (1959), and M.S. from Xavier (1962). A n amateur ichthyologist, Klee has discwvered two genera of fishes and several new species, one of which, the Apistogramma kleei, is named after him.
