Operation Research Models & Principles of Modeling

Operation Research Models

The OR models are

  1. Allocation models
  2. Replacement models
  3. Waiting line models
  4. Network models
  5. Game theory
  6. Inventory models
  7. Markovian models
  8. Job sequencing models
  9. Simulation models

Allocation models (Distribution models)

These models are related with the allocation of available resources so as to make the most of profit or minimize loss subject to existing and predicted limitations. Methods which are used for solving allocation models are

  • Linear programming problems
  • Assignment problems
  • Transportation problems

 Waiting line models (Queueing)

This model is an attempt made to forecast

  • How much average time will be used up by the customer waiting in a queue?
  • What will be a standard length of the queue?
  • What can be the utilization factor of a queue system?

This model provides to reduce the sum of costs of service providing and cost of getting service, linked with the value of time used up by the customer in a queue.

Game theory (Competitive strategy models)

These models are generally used to decide the behavior of decision-making under conflict or competition. Methods for solving such models are not found suitable for industrial practices mainly because they are meant for an idealistic world neglecting many necessary characteristic of reality.

Inventory (Production) models

These models are related with the finding of the best order quantity and ordering production intervals considering the factors like cost of placing orders, demand per unit time, costs related with goods held up in the inventory and the cost due to scarcity of goods, etc.

 Replacement models

These models deals with finding of best time to compensate an equipment in situations which arise when some items or machinery require replacement by a scientific advance or new one or deterioration due to wear and tear, accidents etc. Individual and group replacement principles can be used in case of such equipments that completely fail instantaneously.

 Job sequencing models

These models include the selection of a sequence of performing a series of jobs to be done on machines that maximizes the efficiency measure of working of the system.

Network models

These models are pertinent in big projects involving interdependencies and intricacies of activities. CPM (Critical Path Method) and PERT (Project Evaluation and Review Technique) are used for planning, arranging or scheduling and controlling activities of intricate project which can be described through network diagram.

Simulation models

This model is used mostly for solving problems when there are large number of variables and constrained relationships.

Markovian models

These models are applicable in the situations where the state of the system can be stated by some explanatory measure of numerical value and where the system changes from one state to another on a probability basis.

Principles of Modeling

The model building and their applications both should be willfully aware of the following ten principles

  1. Do not go for a complicated model when simple one will be sufficient
  2. Models never substitute decision makers
  3. The deduction phase of modeling must be carry out carefully
  4. Models should be authenticate prior to implementation
  5. A model should never be taken excessively literally
  6. Be careful while molding the problem to fit the technique
  7. A model should neither be pushed to do nor condemned for failing to do that for which it was never assumed
  8. Some of the major benefits of modeling are linked with the procedure of developing the model
  9. Be careful of over-selling a model
  10. A model cannot be better than the information that goes into it

Simplifications of OR Models

While developing a model, two contradictory objectives generally strike in our mind

  1. The model should be as precise as possible
  2. It should be as simple as possible in solving

Besides, the management must be able to comprehend the solution of the model and must be able of using it. So the truth of the problem under study should be simplified to the extent when there is no loss of accurateness. The model can be simplified through

  • Skipping certain variable
  • Aggregating the variables
  • Altering the nature of variables
  • Altering the relationship between variables
  • Changing the constraints, etc

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