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The Distribution System Simulator


  • Michael M. Connors

    (IBM, White Plains, New York)

  • Claude Coray

    (IBM, Los Angeles, California)

  • Carol J. Cuccaro

    (IBM, Los Angeles, California)

  • William K. Green

    (IBM, Los Angeles, California)

  • David W. Low

    (IBM, Los Angeles, California)

  • Harry M. Markowitz

    (Arbitrage Management Co., Inc.)


The Distribution System Simulator (DSS) is a software system designed to overcome the difficulties inherent in the construction and use of simulation models for large-scale, physical distribution systems. The central difficulties associated with the simulation of a distribution system are: (1) defining a suitable model, (2) programming the model for a computer, (3) obtaining appropriately indicative output reports from the distribution simulation, (4) ordering the model repetitively to respond to the implications of the output reports so that the practical consequences of theoretical changes can be determined. The Distribution Systems Simulator provides a means for accomplishing these ends without programming effort on the part of the user. The Distribution System Simulator is a modelling tool which produces a mathematical representation of a firm's distribution system. The user of DSS responds to a questionnaire which contains the options that he can use to develop a model of his distribution system. The user specifies the characteristics of the desired model by answering true or false to questions expressed in English. The options allow the analyst to take into account each of the major factors involved in the operation of a distribution system: the characteristics of customers' demand for products, buying patterns of customers, order filling policies, replenishment policies, emergency replenishment policies, redistribution policies, transportation policies, distribution channels, factory locations, production capabilities, and other significant elements. These options are essentially inventory and product movement oriented-beyond this, DSS provides the capability, through user functions, to incorporate other vehicle scheduling algorithms, forecasting techniques, production schedules, and pricing mechanisms which are outside the scope of the options. Since DSS emulates the essential parts of the actual distribution system, it permits the distribution system to be modelled in such a way that a "total system approach" can be taken to the problem by OR personnel as well as by executives. DSS is more than a distribution system model generator. It also generates the computer program for running the model on a computer. As indicated above, the user of DSS specifies the characteristics of the desired model by answering a questionnaire consisting of a set of true or false questions. These answers are submitted to a computer, along with the DSS program. The result is: (1) the generation of a PL/I program whose logic is described by the options chosen on the questionnaire, (2) a complete specification of the data required by the simulation program generated, and (3) a complete specification of the information required for the output analysis available for the simulation generated. The user need not be familiar with any elements of computer programming in order to obtain the simulator, instructions for its use, and output analyses; however, the requirements of a thorough understanding of distribution systems, modelling, and management science are imposed on the user.

Suggested Citation

  • Michael M. Connors & Claude Coray & Carol J. Cuccaro & William K. Green & David W. Low & Harry M. Markowitz, 1972. "The Distribution System Simulator," Management Science, INFORMS, vol. 18(8), pages 425-453, April.
  • Handle: RePEc:inm:ormnsc:v:18:y:1972:i:8:p:b425-b453

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    Cited by:

    1. Magnanti, Thomas L., 1983. "Networks as an aid in transportation and contingency planning," Energy, Elsevier, vol. 8(8), pages 703-723.
    2. Drexl, Andreas & Klose, Andreas, 2001. "Facility location models for distribution system design," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 546, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    3. Hax, Arnoldo C. & Meal, Harlan C., 1973. "Hierarchical integration of production planning and scheduling," Working papers 656-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Klose, Andreas & Drexl, Andreas, 2005. "Facility location models for distribution system design," European Journal of Operational Research, Elsevier, vol. 162(1), pages 4-29, April.

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