Management Science Techniques 

Linear programming 

• A problem-solving approach developed for situations involving maximizing or minimizing a linear function subject to linear constraints that limit the degree to which the objective can be pursued. 

• Easier terms: min-max a situation using constraints 

Integer linear programming 

• An approach used for problems that can be set up as linear programs with the additional requirement that some or all of the decision recommendations be integer values (whole numbers). 

Network models 

• Solution procedures for problems in transportation system design, information system design, project scheduling, etc. 

Project scheduling 

• PERT (Program Evaluation and Review Technique)  

• CPM (Critical Path Method)  

• These help managers who are responsible for planning, scheduling, and controlling projects that consist of numerous separate jobs or tasks performed by various departments or individuals. .

Inventory models  

• Used by managers faced with the dual problems of maintaining sufficient inventories to meet demand for goods and, at the same time, incurring the lowest possible inventory holding costs. Note that it is assumed that demand is known and constant

Waiting line (or queuing) models 

• Helps managers understand and make better decisions concerning the operation of systems involving waiting lines. 

Simulation 

• A technique used to model the operation of a system. This technique employs a computer program to model the operation and perform simulation computations 

Decision analysis 

• Can be used to determine optimal strategies in situations involving several decision alternatives and an uncertain pattern of future events. 

Goal programming 

• A technique for solving multi-criteria decision problems, usually within the framework of linear programming.  

Analytic hierarchy process 

• A multi-criteria decision-making technique that permits the inclusion of subjective factors in arriving at a recommended decision. 

Forecasting methods 

• Techniques that can be used to predict future aspects of a business operation. 

Markov-process models 

• These are useful in studying the evolution of certain systems over repeated trials (such as describing the probability that a machine, functioning in one period, will function or break down in another). 

Methods Used Most Frequently 

• Linear programming 

• Integer programming 

• Network models (such as transportation and transshipment models) 

• Simulation 

Outline

1. Linear Programming

• Definition: Problem-solving approach for maximizing/minimizing a linear function under linear constraints.

• Simplified Concept: Min-max a situation using constraints.

2. Integer Linear Programming

• Definition: Linear programming with the requirement that some or all decision variables are integers (whole numbers).

3. Network Models

• Application: Solutions for transportation system design, information system design, project scheduling, etc.

4. Project Scheduling

• Techniques:

  • PERT (Program Evaluation and Review Technique): Planning and scheduling tool for projects.

  • CPM (Critical Path Method): Method for determining the longest stretch of dependent activities.

5. Inventory Models

• Purpose: Balancing sufficient inventory to meet demand while minimizing holding costs.

6. Waiting Line (Queuing) Models

• Function: Aids in decision-making for systems involving waiting lines.

7. Simulation

• Definition: Technique to model system operations using computer programs for simulation computations.

8. Decision Analysis

• Use: Determines optimal strategies amidst multiple decision alternatives and uncertain future events.

9. Goal Programming

• Definition: Technique for solving multi-criteria decision problems within linear programming frameworks.

10. Analytic Hierarchy Process

• Description: Multi-criteria decision-making technique incorporating subjective factors for recommendations.

11. Forecasting Methods

• Purpose: Techniques to predict future aspects of business operations.

12. Markov-Process Models

• Application: Studying system evolution over repeated trials, e.g., machine functioning probabilities.

13. Most Frequently Used Methods

• Linear Programming

• Integer Programming

• Network Models (Transportation and Transshipment)

• Simulation

Mindmap

Linear Programming

• Problem-solving approach

  • Maximizing or minimizing a linear function

  • Subject to linear constraints

• Simplified: Min-max a situation using constraints

Integer Linear Programming

• Linear programs with integer requirements

• Decision recommendations must be whole numbers

Network Models

• Solution procedures for:

  • Transportation system design

  • Information system design

  • Project scheduling

Project Scheduling

• Techniques:

  • PERT (Program Evaluation and Review Technique)

  • CPM (Critical Path Method)

• Purpose: Assist managers in planning, scheduling, and controlling projects

Inventory Models

• Address dual problems:

  • Maintaining sufficient inventories to meet demand

  • Minimizing inventory holding costs

Waiting Line (Queuing) Models

• Aid in decision-making for systems with waiting lines

Simulation

• Technique to model system operations

• Utilizes computer programs for computations

Decision Analysis

• Determines optimal strategies

• Involves multiple decision alternatives and uncertain future events

Goal Programming

• Solves multi-criteria decision problems

• Operates within linear programming framework

Analytic Hierarchy Process

• Multi-criteria decision-making technique

• Incorporates subjective factors in decision recommendations

Forecasting Methods

• Techniques for predicting future business operations

Markov-Process Models

• Studies system evolution over repeated trials

• Describes probabilities of machine functioning or breakdown

Methods Used Most Frequently

• Linear Programming

• Integer Programming

• Network Models (Transportation and Transshipment)

• Simulation