
Explore simulation as a forecast tool in operations research, using Monte Carlo methods, probability distributions, and random numbers to predict outcomes, time metrics, return on investment, and inventory costs.
Explore how simulation models forecast queue length and demand using probability distributions and random numbers, guiding production planning and distribution center decisions.
Explore queuing problems in simulation by analyzing patient waiting times and service start times in a clinic. Learn to calculate the average waiting time and ideal doctor start times.
Explore how to model real-life systems with simulation, using probability distributions, preliminary worksheets, and cumulative probabilities to forecast waiting lines, machine breakdowns, and maintenance costs.
Explore how to model waiting line problems through simulation, compute average waiting times, and assess reliability under varying arrival and service times.
Explore an inventory problem through simulation, analyzing demand, stock levels, opening and closing stock, and how lead time and outstanding orders shape ordering decisions.
Identify strategies for managing inventory under uncertain demand. Examine opening and closing stock, orders, and shortage costs through probabilistic demand and stock levels.
Explore an inventory problem in operations research, analyzing stock levels, opening and closing stock, demand, and ordering decisions under lead time in a simulation context.
Investigate a maintenance replacement problem using simulation to model replacement costs, downtime costs, life expectancy, and frequency and cumulative probability distributions.
Explore a simulation model of the purchase and demand problem, estimating profit across six days by analyzing cost boundaries, shortages, supply and demand dynamics, and probability outcomes.
Simulation is a technique used for evaluating alternative course of action based upon facts and assumption with a computerized mathematical model in order to represent actual decision making under conditions of uncertainty. Simulation is a method of solving decision making problem by designing ,constructing and manipulating a model of the real system. It is a useful technique for solving a business problem where many values of the variables are not known or partly known in advance and there is no easy way to find these values. Simulation may be defined as a “quantitative technique that uses a computerized symbolic model in order to represent actual decision making under uncertainty for determining alternative alternative courses of action based upon facts and assumptions.”
Simulation is probably the most important technique used in analyzing a number of complex systems where analytical methods are either difficult or not known .Basically it is a technique of manipulating a model through a process of imitation .It normally involves a large number of computations .Whenever mathematical models for getting an analytical solution does not exist , simulation method enables us to obtain a numerical solution to the problem. A simulation model may be considered to be to be consisting of two basic phases , namely , Preliminary Worksheet and Final Worksheet. In this course , the students will learn:
Concept of Simulation
Mechanism of Monte Carlo Method
Solution of Queuing Problems using Simulation
Solution of Inventory Problems using Simulation
Solution of Financial Problems using Simulation
Solution of Maintenance Problems using Simulation