In the simulation model the development of the drinking water supply system of South Holland is simulated for the next thirty years given a policy strategy, a certain demand of drinking and subpotable water and some scenario assumptions like discount rate, water quality standards, increase of energy prices. An alternative policy strategy generates an alternative development over time of the supply system.
Input-output analysis for an “open” system relates production rates for various sectors of an economy to stipulated final demands. However, it is well known that the conventional dynamic analysis usually does not yield results which approach smoothly to those of the static analysis. In this work, the dynamic analysis is cast into the form of a system dynamics model. A modification of the rule which governs sector production rates is introduced so that stable results are obtained which do approach those of the usual static input-output analysis. The system equations are further modified to incorporate time-lagged stock indices and damping in the production rate rule. Prices are handled throughout as in conventional input-output analysis.
A study of the multiple-use task produces a method for integrating quantitative and subjective information to enhance decision-making about the multiple use of renewable resources. Methods of resolving conflicts and applying system dynamics methods are given.
The model system is an approach to solve the dynamic multilocation warehouse (or plant) sizing problem by integrating different models and methods: (1) A simulation model of System Dynamics type for analyzing effects of different locations and capacities on demand, cost, and operating results; (2) An Integer Programming model for determining warehouse configurations, i.e. effective locations, and capacities. The model system has been applied to an important German wholesale distributor of pharmaceuticals. It has been used for analyzing the firm’s distribution system, and working out proposals in order to improve it.
This paper describes a System Dynamics approach to the problem of linking national and regional transportation to other components of national development plans. A framework of interactions among social, economic, and transportation variables is constructed based on the proposed approach. Such a framework facilitates the analysis of the reciprocal impacts of transportation infrastructure and the socio-economic environment, thus providing an important input to the process of transportation policy making for development. Specific references are made to Venezuela, where a serious effort is being made to explicitly incorporate a transportation strategy into the national development plan.
About five years ago a semi-governmental firm, the VAM (Vuil Afovoer Maatschappij) formulated plans to extend their efforts to convert domestic waste to compost to a real recycling industry. The idea was to install equipment to extract secondary paper pulp from domestic waste and sell it to paper and board industry. The Union of Old Paper Merchants opposed strongly: abundancy of low grade secondary paper pulp could ruin the old paper market and times were bad just after the oil crisis. A study was started to investigate possible consequences of the VAM plans. In the four following years a System Dynamics model was built to show the most important mechanisms of the problem.
The central premise of this study is that complex models of social processes often fail to provide direct and useful evidence for policy makers because, of necessity, complex models are based upon five distinct classes of assumptions. At least two of these five classes of assumptions are based upon a priori or theoretical arguments rather than strict empirical arguments. Because of their inherent speculative nature (at least in part), complex models produce forecasts that are not admissible as evidence in an essentially political debate.
This paper introduces and discusses the concept of verbally formulated simulation models. Such models can operate with linguistic values as ‘high’, ‘rather high’, ‘low’ and ‘not low’, etc. as inputs. The output will be similarly verbally formulated. The stimulation procedure is based on a fuzzy set-theoretical semantical model of a fragment of English language, which converts verbal expressions into numerical quantities. The paper applies one particular semantical model in a simulation example. Verbal models may be more believable, or significant, than conventional system dynamic models, in that they adequately represent the fuzzy knowledge of the system which is modeled. The cost of this significance is loss of precision in model output. Verbal models are also easier to test for sensitivity to parameter-, state- and input values than traditional models. Therefore, a comprehensive understanding of the model’s behavior patterns is more readily obtained. The realm of successful applications of verbal models seems, however, to be restricted to systems with variables which are not physically measurable, but whose values are only available through human intuition. Finally, verbal models may successfully be incorporated in conventional system dynamic models if technically feasible. Such a prosedure would allow for an adequate handling of non-quantifiable data.
This paper documents a series of lessons that the author and his colleagues have learned about how to achieve implemented results from system dynamics projects. Through a series of three case studies, the paper illustrates the evolution of their approach to implementation over the period of 1966 to 1975. These case studies focus on: client involvement in projects; the process of model development; the nature of the models developed; and the end of the projects. The paper draws upon the case studies and earlier writing on the subject by Roberts to generalize about the factors that are most critical in achieving successful implementation. These factors include: the sharpness of the project’s problem focus; the urgency of the problem addressed; the organizational position of the clients; the degree and nature of client involvement; the size of the model developed; the demonstrable validity of the model and the nature of the project’s end-products.
Starting from the aims and difficulties of social systems modeling this paper argues that a good understanding of dynamic mathematical models is indispensible. The author’s background, and its relation to System Dynamics is elucidated, and a number of definitions are given of concepts and terms that will be employed. A set of general guidelines, and a list of strategies and tools for understanding follow. Most of the methods presented have been applied successfully in an extensive study of the World Models by Forrester and Meadows et al., and are commonly used in systems and control engineering. The main emphasis is on techniques are points of view that are generally unknown to researchers and practicians in the non-technical disciplines.