ORMSwareTM is a proprietary quantitative modeling and programming system developed by US Army veteran Reginald Joules at Ushar Enterprises Inc, Littleton, Colorado, USA, with keen interest in national defense problems

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ORMSwaretm Discrete Event Simulator And Discrete-Event-Simulation-Powered Programming

When off-the-shelf solutions cannot meet your organization's urgent ad-hoc analytical needs, Ushar Enterprises Inc (UEI) is ready to...

  • Solve your quantitative business decision problems using Operations Research/Management Science/Decision Science (OR/MS/DS) modeling

  • Compress problem-definition-through-solution-implementation time by using proprietary in-house discrete event simulator (ORMSware, not for sale)

  • Develop off-the-cuff programs using ORMSware's hierarchical-queuing-networks-structured discrete event simulation paradigm (a model-based design paradigm for programming)

All your q
uantitative systems have hierarchical queuing networks structure. ORMSware enables expression of software logic in terms of...

  • Storage, retrieval, transportation, and utilization of man (person), machine, materials, money, and minutes (time) through easily digestible hierarchical queuing networks

  • Dynamic events and status changes in queuing networks for expressing calculations of all desired results, whether models are descriptive (what-if) or prescriptive/normative (if-what)

  • Regardless of industry, quantitative business decision problems tend to be mixes of eight core problem types, easily identifiable by their nature/signature/characteristics

Feel free to send us a problem if you would like to see how that can be. Example below is a trucking industry problem. You need not be in trucking industry to have a problem of similar nature.
Spreadsheets are hierarchical queuing networks (see Income Statement, 3rd image down). UEI can convert convoluted, black box spreadsheets into crystal clear ORMSware programs.
Programs in virtually any language are also process networks. ORMSware can be leveraged to convert legacy programs to hierarchical queuing networks powered by any target language.

UEI's highest interest is in helping solve national defense problems. UEI's nearest competitor in pure modeling muscle, flexibility and speed to answer is headquartered in Russia!

Full page 1 in KeepOnTrucking PDF

UEI's ORMSware approach leverages Microsoft Visio to visually express problem/solution using hierarchical queuing networks.

ORMSware engine then translates and adds and allows supplemental logic to execute the model's hierarchical networks laid out in Visio, cranking numbers and producing results.

In the example problem shown on the left the blue nodes (boxes) and arcs (arrows) indicate that there are sub-networks under them. In this example all blue nodes and arcs refer to same subnet, viz., [TimeKeeper].

ORMSware nodes and arcs have much in common. The Subnet property, for easy nesting of networks into nodes and arcs, is part of that commonality.

UEI uses hierarchical queuing networks in this fashion to formulate problems and implement solutions quickly, clearly and cleanly regardless of application area or industry.

UEI can easily specify Resource requests and releases (see {+Driver} at greenish node [3] and {-Driver} at beige node [16] in the top diagram at left); also statistics collection at any node or arc as needed for fast development of transparent discrete event simulations (see bullets and dialog boxes below left).

UEI can attach multiple resources to a probe, surrogate, agent, entity, etc. in a simulation (e.g. attaching to a trainee  in a fighter pilot training program a plane, an instructor pilot, a chase plane, and a chase plane pilot...).

Full page 2 in KeepOnTrucking PDF
Full page 2 in KeepOnTrucking PDF

Node Dialog Box
Benefits of ORMSware (ormsware) modeling approach
  • Lucid, visual quantitative models that inspire, involve and connect all stakeholders of a problem, shortening cycle time from problem definition to solution implementation through advantages in technical, psychological and social domains

  • Monte Carlo Simulation for uncertainty analysis
  • Parameter-driven probabilistic discrete event simulation (simulator) for dynamic analysis of resource-dependent systems and processes (see dialog boxes at left)
  • Totally flexible modeling perspectives for making problems easier to solve (e.g. are planes waiting for parts or parts waiting for planes? Do we want to model it as a queuing problem or an inventory problem?)
  • All problems (whether Discrete Event, Agent Based or System Dynamics type) are modeled as generalized, hierarchical, queuing-networks-structured performance systems, providing total flexibility and ease of formulation
  • Multiple resources can be attached to and released from any probe/surrogate/entity in a discrete event simulation

  • Optimization and goal-seeking supervisory functions

  • Powerful array operations

  • Aids in transforming complex spreadsheets into visual quantitative network models that are easy to understand, modify and maintain

  • Allows hybrid models, retaining in spreadsheets easy parts while moving complex parts to ORMSware

  • Automatic variable time dimension to easily express problem dynamics

ORMSware is a rapid visual quantitative modeling system built by Reginald Joules at Ushar Enterprises Inc. Reginald is a US Army veteran with 20 years of experience in Defense sector, keenly interested in helping solve national defense problems.

Spreadsheet Battles

ORMSware enables engineers and analysts to focus on quickly developing quantitative models that are too large and/or complex for spreadsheets, while avoiding the undesirable distractions of heavy admin burdens of programming.

ORMSware has also turned out to be an ideal tool for systematic, fast, risk-less transformation of legacy spreadsheets into visual quantitative networks.

Many organizations have spreadsheets that have evolved over time into constraining spaghetti structures in black boxes that are too risky to modify and hard to maintain.

Perhaps the most fundamental drawback of spreadsheets is the difficulty in using one of the three basic constructs in programming, viz. iteration/recursion/looping. While it is possible to override default circular logic protections or use Visual Basic for Applications (VBA) to implement iterations they are clumsy ways to implement inevitable recursion needs in virtually any consequential quantitative decision model.

Networks Everywhere One Looks

The general ORMSware paradigm is anchored in the undeniable fact that logical networks are pervasive structures which can be used to visually express quantitative solutions in any discipline, in any application arena.

ORMSware network objects in combination with easy ORMSware notations make it possible to model virtually any quantitative problem in any setting.

Target Customers
  • Organizations in both government and commercial sectors who are continually engaged in developing and maintaining complex and large-scale scientific and engineering models and advanced quantitative business models
  • Organizations eager to migrate complex production spreadsheets partially or totally to an environment where content logic is easy to see and understand
  • Organizations seeking rapid-response off-the-cuff disposable automation similar to spreadsheets, with the power and advantage of more easily verifiable visual logic
Reverse Links


Consumer product that funded ORMSware R & D

Contact information

Ushar Entperprises
Attn: ORMSware Project
8624 Palermo Way
McKinney, TX 75071, USA

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Arc Dialog Box
Think. Condense. Model. Code. Document.
Click image at left to see an example of how ORMSware helps analysts think through problems systematically while also documenting, and implementing models, and creating presentations, virtually simultaneously.

This link takes you to an example showing the approach one is likely to use when working with ORMSware to devise a simple algorithm for generating all possible ways a given number of items can be sequenced. It then demonstrates how large networks are logically condensed (to three ORMSware nodes in the case of this algorithm for sequencing any given number of items). The model is then extended to solve a special Asymmetric Traveling Salesman problem (ATSP) with nontrivial functions of distance, time and cost (a trucking problem).


Example of using Excel for input data