Operation Research Techniques

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Operation Research Techniques

Operation Research (OR) involves a range of techniques aimed at solving complex decision-making problems. These techniques help optimize various business operations, ensuring efficient use of resources and improving overall performance.

(i) Inventory Control Models

These models help balance various inventory costs, such as:

  • Shortage Costs: Costs incurred when inventory is insufficient to meet demand.
  • Ordering Costs: Costs associated with placing and receiving orders.
  • Storage Costs: Costs of storing inventory, including warehousing and handling.
  • Interest Costs: Costs of capital tied up in inventory.

Decisions Made Using Inventory Control Models:

  • How Much to Purchase: Determine the optimal order quantity.
  • When to Order: Decide the timing of orders to avoid stockouts.
  • Make or Buy Decisions: Decide whether to produce in-house or purchase from suppliers.

The most well-known inventory control model is the Economic Order Quantity (EOQ) equation, which calculates the ideal order quantity that minimizes total inventory costs.

(ii) Waiting Line Models

These models aim to minimize the total cost of waiting time and idle time. There are two main types:

  • Queuing Theory: Used to determine the optimal number of service facilities and the timing of arrivals for servicing.
  • Sequencing Theory: Focuses on determining the optimal order in which jobs should be processed to minimize total time or costs.

(iii) Replacement Models

These models determine the optimal time to replace or maintain items, considering:

  • Obsolescence: Items becoming outdated.
  • Inefficiency: Items losing efficiency over time.
  • Economic Viability: Items becoming too costly to repair or maintain.

(iv) Allocation Models

These models optimize resource allocation when:

  • There are multiple activities to be performed with several alternative methods.
  • Resources or facilities are limited.

These models help combine activities and resources to achieve the best possible outcome.

(v) Competitive Strategies

These strategies are used in situations where the efficiency of one entity's decisions depends on another's decisions. Examples include:

  • Game Theory: Used in competitive market scenarios, like pricing strategies in a market with multiple competitors.

(vi) Linear Programming Technique

This technique solves problems with multiple variables and certain restrictions to optimize objectives such as profit or costs. Restrictions might include government policies, plant capacity, product demand, availability of raw materials, and storage capacity.

(vii) Sequencing Models

These models determine the optimal sequence for performing a series of jobs on a service facility or machine, aiming to optimize performance measures like total processing time or cost.

(viii) Simulation Models

Simulation models use experimental methods to study system behavior over time. They are particularly useful for understanding complex systems and testing different scenarios.

(ix) Network Models

Network models are used for planning, scheduling, and controlling complex projects. Examples include PERT (Program Evaluation Review Technique) and CPM (Critical Path Method), which help in managing project timelines and resources.

Applications of Operation Research

  • Distribution or Transportation Problems: Optimize the distribution of products from warehouses to various centers using linear programming to minimize costs.
  • Product Mix: Determine the optimal mix of products to maximize profit or minimize production costs with available resources.
  • Production Planning: Allocate jobs to machines to maximize production efficiency or minimize total production time.
  • Assignment of Personnel: Assign personnel to tasks based on aptitude to complete tasks in minimum time.
  • Agricultural Production: Maximize profit by optimizing crop cultivation with varying returns and cropping times on lands with different fertility levels.
  • Financial Applications: Solve financial decision-making problems using linear programming.
    • Portfolio Selection: Maximize return on investment by selecting the best portfolio from alternatives like bonds and stocks.
    • Financial Mix Strategies: Select optimal financing methods for firm projects, inventories, etc.

Limitations of Operations Research

  • Qualitative and Emotional Factors: OR techniques primarily focus on quantitative data, often overlooking qualitative and emotional factors.
  • Specific Categories: OR techniques are applicable to specific types of decision-making problems, not universally applicable.
  • Interpretation: Correct interpretation of OR models is crucial for effective implementation.
  • Resistance to Change: Conventional thinking and resistance from workers and employers can hinder the adoption of OR techniques.
  • Idealized Representation: OR models are idealized representations of reality and should not be considered absolute. They provide a framework for analysis rather than definitive answers.