Enggineering Technology



Now that the steps of OR have been identified, the tools to solve the problem must be described. These models or algorithms are utilized in phase 3-5 to produce results and data collection. Models can be broken into two categories.

They are deterministic and stochastic. Each model is created to specify a certain problem or application needed to be solved. Deterministic models are the simplest types. There are no uncertain or probabilistic variables and no optimization.

They are straightforward and utilize formulas and graphs to represent the data. Within the deterministic category are:

  • · Linear Programming:

  1. Problems involving the allocation of scarce resources such as materials, manpower, machine time or space.
  2. Extensively used in problems of blending ingredients, scheduling, manpower planning and economic planning.
  • · Transportation:

  1. This is a special case of Linear Programming.
  2. This involves problems where one resource is stored or made in several locations and needed in several other locations, e.g. warehousing and distribution.
  • · Assignment:

This is a special case of Linear Programming. This involves problems such as assigning n drivers to n cars in order to minimize cost or n operators to n tasks to minimize time.

  • · Integer Programming: :

This is a special case of Linear Programming. This involves problems of resource allocation with the restriction that certain resources are only available in fixed-size units, which can’t be split up. Also used in problems of route selection and other problems involving 0-1 variables.

  • · Goal Programming:

Problems of resource allocation. Stochastic models incorporate uncontrolled variation in some way.

The widest use of these models is to the use of statistical forecasting. Within the stochastic category are:

  • · Statistical Forecasting Methods:

Making short to medium forecasts of

future values of a time series of data. Typically used for predicting sales,

spare parts demands, etc.

  • · Simulation:

Problems that involve uncertainty in the system – e.g. in production lines, shops, transport services, manpower modeling.

This model is to mimic real-life systems and used to evaluate the real system.

  • · Game and Hypergame Theory:

Competitive problems where there is some kind of opponent. This tends to be more theoretical than practical.

  • · Decision Analysis:

Problems involving a decision or a series with a small number of options and a small number of outcomes. Examples include decisions on whether or not to test market new products, bidding strategies for contracts and diagnosis of the medical condition.

  • · Replacement Theory:

Problems involving the failure of components and/or machines.

  • · Search Theory:

Problems involving something with the use of limited resources.

  • · Queuing Theory:

Problems where customers are queuing for service of some sort. Examples include shops, banks, telephone exchange, repair of machines, and job-shop scheduling.

The following methods can be either used in deterministic or stochastic


· Dynamic Programming:

Problems involving a series of similar linked decisions, differing only in time or space, such as the choice of the shortest route between two points or decisions involving monthly production or storage.

· Project Network Analysis:

Problems involving sequencing and scheduling a collection of identifiably separate jobs subject to various logical constraints. Used almost everywhere in the construction industry.

· Stock Control:

Problems involving the holding of stocks, raw materials, spare parts, finished goods or stationary.


As a mechanical enggineer iam writing here, what i learnt in my life,
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