This package implements functions suitable to solve problems of deterministic inventory with and without quantity discounts.

install.packages("devtools") # If needed
devtools::install_github("sidoruvigo/EOQdis")

Load the library with: library("EOQdis")

The package includes the following functions:

• EOQ()
• EOQd()
• EOQpd()
• plot.EOQ()

This function implements the basic deterministic EOQ (Economic Order Quantity) model.

• Order arrives instantly.
• No stockout.
• Constant rate of demand.

• l: Demand (per unit of time).
• k: Preparation cost (per order).
• I: Storage cost (per article).
• C: Cost of goods (per item).

A list containing:

• Q: Optimal order quantity.
• Z: Total cost.
• T: Cycle length.
• N: Number of orders.

library(EOQdis)

l <- 520  # Demand
k <- 10   # Preparation cost
I <- 0.2  # Storage cost per article
C <- 5    # Cost of goods per item

res1 <- EOQ(l = l, k = k, I = I, C = C)
res1

This function provides an EOQ with discounts where the units purchased have the same reduction in price.

• l: Demand (per unit of time).
• k: Preparation cost (per order).
• I: Storage cost (per article).
• q: Product quantities where the price changes (Vector of quantities within the discounts given in 'dis' are applied).
• dis: Vector of discounts.
• c: Orginal price of the product. (Price of the product without any discount)

A list containing:

• Q: Optimal order quantity.
• Z: Total cost.
• T: Cycle length.
• N: Number of orders.

dis <- c(0, 0.05, 0.1) # Disccounts: 5% and 10%
l <- 520 # Demand (per unit of time).
k <- 10  # Preparation cost (per order).
I <- 0.2 # Storage cost (per article).
q <- c(0, 110, 150) # 5% when the quantity is greater than 110 and less than 150. For more than 150 units the discoun is 10%
c <- 5  # Original price of the product
res2 <- EOQd(dis = dis, l = l, k = k, I = I, q = q, c = c)
res2

A company needs 500 chairs every month to sell in their online store that costs 15€ each. The supplier negotiates with the company that if they buy more than 50 chairs they offer them a 25% disccount and if they buy more than 100 they offer them a 50% disccount. Making an order costs 10€ and the storage cost is estimated to be 2€ per chair.

dis <- c(0, 0.25, 0.5)
q <- c(0, 50, 100)
l <- 500
k <- 10
I <- 2
c <- 15

opt <- EOQd(dis = dis, l = l, k = k, I = I, q = q, c = c)
opt

This function provides an EOQ with discounts where the discount occurs for units purchased when a certain amount is reached.When the amount of order increases, the cost price decreases in the additional units ordered, not in all units.

• l: Demand (per unit of time).
• k: Preparation cost (per order).
• I: Storage cost (per article).
• q: Quantites where the discounts are applied.
• c: Vector of cost of goods.

A list containing:

• Q: Optimal order quantity.
• Z: Total cost.
• T: Cycle length.
• N: Number of orders.

l <- 50000
k <- 10
I <- 0.25
c <- c(0.6, 0.55)
q <- c(0, 1000)

res3 <- EOQpd(l = 50000, k = 10, I = 0.25, c = c, q = q)
res3

An university needs to buy 5000 markers every year, with a cost per order of 15€ and there is an estimated storage cost of 0.2€. The provider has the following policy: if the customer buys less than 500 markers, the cost per item is 0.75€, if the customer buys more than 500 units, the cost per item is 0.5€. We need to determine the optimal policy

l <- 5000
k <- 15
I <- 0.2
c <- c(0.75, 0.5)
q <- c(0, 500)

res3 <- EOQpd(l = l, k = k, I = I, c = c, q = q)
res3

S3 method to plot a EOQ object by using the generic plot function.

• x: Object of class EOQ.

A plot with classical EOQ representation.

plot(res1)
plot(res2)
plot(res3)

• Soage González, José Carlos.

Maintainer: José Carlos Soage González ([email protected])