AS 712/644 Simulation
2- 1
2. Static Simulation
Two Types of simulation models:
Static simulation or monte-carlo simulation
Dynamic simulation or system simulation
Monte-Carlo simulation is often used to evaluate the expected impact of
policy change and risk involved in decision-making.
Risk is often referred to the probability of occurrence of an undesirable outcome.
System simulation usually models sequence of events that occur over time.
2.1 Example of Static simulation
ในหัวขอนี้จะกลาวถึงตัวอยางการสรางแบบจําลองเพื่อศกึษาและประเมินพฤติกรรมของระบบ
Example 2.1 Sweet Candies is a small family-owned business that offers gourmet
chocolates and ice cream fountain service. For special occasions, such as Valentine's
Day, the store must place orders for special packaging several weeks in advance from
their supplier. One product, Valentine's Day Chocolate AAA, is bought for $7.50 a
box and sells for $12.00. Any boxes that are not sold by February 14 are discounted
by 50 percent and can always be sold easily. Historically, Sweet Candies has sold
between 40 and 90 boxes each year with no apparent trend (either increasing or
decreasing). Sweet dilemma is deciding how many boxes to order for the Valentine's
Day customers. If demand exceeds the purchase quantity, then the company loses
profit opportunity. On the other hand, if too many boxes are purchased, he will lose
money by discounting them below cost.
If Q boxes are purchased and sales demand is D, profit of this company can be
expressed as
Profit = 12D - 7.50Q + 6(Q - D) if D ≤ Q
(2.1)
= 12Q - 7.5Q
if D > Q
(2.2)
The inputs to a simulation model of this situation would be
1. the order quantity, Q (the decision variable),
2. the various revenue and cost factors (constants), and
3. the demand, D (uncontrollable and probabilistic).
The model output we seek is the net profit. If we know the demand, then we
can use equation 2.1 and 2.2 to compute the profit. Because demand is probabilist