An Interview Question: Using Integer Programming

We can solve the Interview Question using a mathematical technique called Integer Programming. Let d_1, d_2, \ldots, d_N be the variables representing diskette 1, diskette 2, diskette 3, etc. The values of the d_k variables can only be 0 or 1. A 0 means the diskette is not used while a 1 means that it is used.

Each file is saved to a certain diskette. We want to know to what diskette d_i a given file f_j is assigned. To represent this, we assign the variable a_{ij} a value of 1 if file f_j is assigned to diskette d_i.

We will normalize the file sizes so that if s_i is the size of f_i, the s_i \le 1. We do this by simply dividing all file sizes by the size of the diskette. For a given diskette d_i, the following constraint should be satisfied:

d_i - s_1a_{i1} - s_2a_{i2} - \ldots - s_N a_{iN} \ge 0

for diskette i = 1, 2, \ldots, N and s_i are the normalized file sizes of file f_i for i=1,2,\ldots,N.

Since each file f_j can only be assigned to one diskette, we have the following constraint:

a_{1j} + a_{2j} + \ldots + a_{Nj} = 1

where a_{1j} is the variable representing the “file f_j is in diskette d_1“, etc.

Finally, we have to constrain the value of d_i to be either 0 or 1, that is,

d_i \le 1

for all i=1,2,\ldots,N.

Integer Programming Formulation

Given the above information, we can formulate the Integer Programming problem as


d_1 + d_2 + d_3 + \ldots + d_N

subject to

\begin{array}{rl}  d_1 - s_1a_{11} - s_2a_{12} - s_3a_{13} - \ldots - s_Na_{1N} &\ge 0\\  d_2 - s_1a_{21} - s_2a_{22} - s_3a_{23} - \ldots - s_Na_{2N} &\ge 0\\  :\\  d_N - s_1a_{N1} - s_2a_{N2} - s_3a_{N3} - \ldots - s_Na_{NN} &\ge 0\\  a_{11} + a_{21} + a_{31} + \ldots + a_{N1} &= 1\\  a_{12} + a_{22} + a_{32} + \ldots + a_{N2} &= 1\\  :\\  a_{1N} + a_{2N} + a_{3N} + \ldots + a_{NN} &= 1\\  d_1 &\le 1\\  d_2 &\le 1\\  :\\  d_n &\le 1  \end{array}

Solving the Problem

We will use R to solve this Integer Programming Formulation. Please see code below:


# Generate random file sizes between 1 and 10
x = -1*FileSizes/10

# Each files can be in any of the diskettes. Suppose there are N files,
# to determine if a file j is in diskette i, the value of variable x_ij will
# 1 if file j is in diskette i, and 0 otherwise.
# Here we construct the coefficients of variables x_ij which are the
# sizes of the files (normalized to 1)
for(i in 1:(l-1)){

# Construct the coefficients of the indicator variables representing the 
# diskettes d_i
for(i in 1:l){
 ttt[i] = 1
 CoefficientsOfDiskettes= c(CoefficientsOfDiskettes,ttt,zzmatrix[i,])

# Construct the coefficients of x_ij for constant j. These variables 
# satisfy the equation \sum_{i=1}^N x_{ij}
for(i in 1:l){

# Prepend Coefficients of variables d_i. The value of these coefficients is 0. 
for(i in 1:l){

# Construct coefficients of d_i to construct constraint d_i <= 1
for(i in 1:l){

# Construct matrix input of lpSolve

print("Matrix Coefficients:")

# Construct inequalities/equalities

# Construct Right-Hand side

# Construct Objective Function,l),rep(0,l*l))

# Invoke lpSolve

# Print Results
print(paste("Number of Diskettes: ", sum(mylp$solution[1:l])))
print("File Sizes: ")
for(i in 2:(l+1)){
   files = which(tz[i,] == 1)
   if(length(files) > 0){
      print(paste("Files in diskette ", i-1))

Most of the code above is setting up the matrix of coefficients. The line 70 then calls on lpSolve to compute the optimal values of the variables

Program Output

Running this code we get the output

[1] "Matrix Coefficients:"
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20]
 [1,]    1    0    0    0   -1 -0.2 -0.1 -0.1    0   0.0   0.0   0.0     0   0.0   0.0   0.0     0   0.0   0.0   0.0
 [2,]    0    1    0    0    0  0.0  0.0  0.0   -1  -0.2  -0.1  -0.1     0   0.0   0.0   0.0     0   0.0   0.0   0.0
 [3,]    0    0    1    0    0  0.0  0.0  0.0    0   0.0   0.0   0.0    -1  -0.2  -0.1  -0.1     0   0.0   0.0   0.0
 [4,]    0    0    0    1    0  0.0  0.0  0.0    0   0.0   0.0   0.0     0   0.0   0.0   0.0    -1  -0.2  -0.1  -0.1
 [5,]    0    0    0    0    1  0.0  0.0  0.0    1   0.0   0.0   0.0     1   0.0   0.0   0.0     1   0.0   0.0   0.0
 [6,]    0    0    0    0    0  1.0  0.0  0.0    0   1.0   0.0   0.0     0   1.0   0.0   0.0     0   1.0   0.0   0.0
 [7,]    0    0    0    0    0  0.0  1.0  0.0    0   0.0   1.0   0.0     0   0.0   1.0   0.0     0   0.0   1.0   0.0
 [8,]    0    0    0    0    0  0.0  0.0  1.0    0   0.0   0.0   1.0     0   0.0   0.0   1.0     0   0.0   0.0   1.0
 [9,]    1    0    0    0    0  0.0  0.0  0.0    0   0.0   0.0   0.0     0   0.0   0.0   0.0     0   0.0   0.0   0.0
[10,]    0    1    0    0    0  0.0  0.0  0.0    0   0.0   0.0   0.0     0   0.0   0.0   0.0     0   0.0   0.0   0.0
[11,]    0    0    1    0    0  0.0  0.0  0.0    0   0.0   0.0   0.0     0   0.0   0.0   0.0     0   0.0   0.0   0.0
[12,]    0    0    0    1    0  0.0  0.0  0.0    0   0.0   0.0   0.0     0   0.0   0.0   0.0     0   0.0   0.0   0.0
[1] "Number of Diskettes:  2"
[1] "File Sizes: "
[1] 10  2  1  1
[1] "Files in diskette  1"
[1] 2 3 4
[1] "Files in diskette  2"
[1] 1

Interpreting the Result

Lines 2-14 of the output gives you the matrix of coefficients. Line 15 prints the number of diskettes needed to store the files. Line 17 prints the randomly generated file sizes from 1 to 10. Finally lines 18-21 prints which diskettes contain which files.

The space complexity of this solution is quite substantial. Given N files, we need to specify N^2 + N variables by 3\times N equations for a total of (N^2 + N)\times 3N memory space for coefficients.


Basic Portfolio Optimization

Everyone would like to make a profit out of the money they have. Unless the sum of money is small, putting it in the bank is not a wise choice. The rate of interest given by the bank is so small that inflation will just eat up most of the profit. The most profitable investment but riskier one is on the stock market. Investing on the stock market is more of an art than a science. An investor will initially take a look at the fundamental data of a company to see how it is performing. You don’t want to put your money in a company that will close in a month.

After identifying potential companies, the next question is how much of each security should one purchase in order to maximize your profit while at the same time minimizing the risk. There is a risk involved when buying such securities. The price per share of a security changes frequently in a day. As an example, if you purchased 100 shares of security A at a price of $1 per share and suppose that on the tenth day the price rose to $1.50. If you decide to sell all your shares on that day, your profit will be $50 or 50% of your initial investment. If however, the price per share declined to $0.9, you will lose $10 or 10% of your initial capital. In this discussion, we consider the closing prices.

Risk and Expected Return

How then do we calculate the risk involved in holding shares of a security? The risk depends on how long you are going to keep those shares before selling them.

Table 1 lists the price/share of a traded security for the last 10 days. Assuming you bought 1 share at day 1. If you sell it on day 10, your profit will be 0.6%. Selling it on day 6, your return will be 3.7%. The average return is 0.6%. The risk in investing in this security is represented by the standard deviation \sigma = 2.04\%.


Let \bar R_i be the ith return of security i and X_i be the fraction of the investor’s fund invested in asset i, then the expected value of the portfolio P consisting of these assets is give by

\bar R_p = \sum^N_{i=1} X_i\bar R_i

where N is the number of assets in the portfolio. The variance of portfolio P is given by

\sigma_P^2 = \sum_{j=1}^N X_j^2\sigma_j^2 + \sum_{j=1}^N\sum_{k\neq j}^N X_jX_k \sigma_{jk}

where \sigma_{jk} is the covariance between asset j and k. The covariance is the expected value of the product of two deviations: the deviations of the returns on asset j from its mean and the deviations of asset 2 from its mean.

Simple Example

Let us illustrate portfolio optimization using a simple example. Suppose the current interest rates when investing in Treasury Bills is 8%. Treasury Bills are risk free investments in that if you will get 8% more of your initial money at the end of the holding period. The holding period is the length of time your money is in the possesion of the borrower before it matures. Assuming that the inflation rate at the end of the holding period is 3%, the real amount of money you earned in investing in Treasury Bills is 8-3=5\%.

In order to make more money, we decide to invest some of our money in stocks and bonds which are more risky but the returns are high enough to justify the risk. Let A and B be two securities of a portfolio P with the following parameters:


We assume a \sigma_{ab}= 0.20. The average return of portfolio P is

r_p = 0.10w_a + 0.17 w_b

and the average risk is

\sigma_p^2 = (0.12w_a)^2 + (0.25w_b)^2 + 2(0.20)(0.12)(0.25) w_a w_b

where w_a and w_b are the fraction of your money which you invest in security A and B respectively. We require that w_a + w_b = 1, that is , we invest all our allocated money in this two securities.

The table below shows some values of \sigma versus e_r for various weights.


Figure 1 shows a plot of risk versus expected return for various combinations of w_a and w_b. The line shown is called the Capital Allocation Line. The slope of this line is called the reward-to-variability ratio and is give by

\label{reward.variability.ratio}S = \frac{E(r_p) - r_f}{\sigma_p}

The y-intercept of this line is the risk-free rate, which in our example
is 8%. The slope is the amount of return you get per unit increase in the amount of risk. The goal of portfolio optimizatio is to find the combinations of $lalex w_a$ and w_b that maximizes this quantity.


Substituting the given values of the risks and expected returns we get the optimization problem we have to solve:


S_p = \frac{E(r_p) - 0.08}{\sigma_p}

subject to

r_p  = 0.10 w_a + 0.17 w_b
\sigma_p^2 = (0.12w_a)^2 + (0.25w_b)^2 + 2(0.20)(0.12)(0.25) w_a w_b
1 = w_a + w_b

In the next post, we will use genetic algorithms to solve this optimization problem.

Using Linear Programming to Solve the Call Center Problem

Agents cost money. Not only will you pay for their salary but also other benefits in you compensation package. So the less agents you have to do a job, the less overhead, the more profit for you.

In the last article, we solve the call center scheduling using a manual approach. The question now is this: Does our solution give us the least possible cost to operate the call center? We cannot be sure. However, there is a technique which we can use in order to calculate the least possible cost of our schedule subject to the constraints of the required agents per time slot. This technique is called Integer Linear Programming.

Formulation of the Problem

Let x_i, 0 \le i \le 335, be the number of call center agents who comes to work at time slot i. Using this notation, we can calculate the number of call center agents current present in the office at time slot 1:

S_1=x_0 +  0 + \ldots+  0 + x_{320} + x_{321} + \ldots + x_{335}

How did we arrive with this equation? If an agent starts at time slot 320, then he/she will still be in the office at time slot 1. This is however his/her last time slot for the shift. All agents that start after 320 until 336 will also be present at time slot 1. And so is the agent that starts at time slot.

Similarly, the number of agents in time slot 2 is given by

S_2= x_0 + x_1 + 0 + \ldots + 0  + x_{321} + x_{322}  + \ldots + x_{335}

A similar pattern holds for i from 2 to 335.

Let \vec{req} be the vector of required number of agents per time slot. Then we can specify for each time slot i that the number of agents be greater than or equal to req[i].

The linear programming problem can now be specified by:

Minimize \sum_{i=0}^{335} x_i

subject to the following contraints:


We can create programmatically the matrix of these constraints using this algorithm. The code is written in R, which is an open source statistical software.

y=(x+17) %% 336

for( i in 1:336){
z=(y - i) %% 336;
for( j in 1:336){
if(z[j] < 18 ){
tmp[j] = 1;

Notice that we start our index from 1 rather than 0. This is because R is a 1-based programming language.

Using the lpSolve package of R, we can compute the minimum of the objective function using the lp function of the library.


print("computing lp");
mylp=lp ("min", f.obj, f.con,
f.dir, f.rhs, int.vec=1:336)

After executing the code above, we get the following result:


The value of the objective function is given by:

> mylp$objval
[1] 47

If we compare this to the manual method, the value of the objective function is just the sum of the components of z:

> sum(z)
[1] 48

This means that using the Linear Programming technique, we were able to come up with a solution with the least cost.

Manual Approach to Call Center Scheduling

In the last post, we defined the problem of call center scheduling. In this post, we are going to present a manual way of solving this problem.

Let us first define some terminology. A time slot is a 30 minute interval, e.g. 12:00 am – 12:30 am. Each time slot has a required number of agents that must be in the office. Since there are 24 hours in a day, then there are 48 time slots in a day. each time slot is independent from each other. Therefore, the total number of independent time slots is 48*7 = 336. Let us define the following vectors

  • a vector \vec{req} of length 336 whose components define the required number of agents in the ith slot.
  • a vector \vec{v} of length 48*7=336. Each component of this vector represents the number of agents current in the office.
  • a vector \vec{z} of length 336. This vector represents the number of call center agents that will start in that time slot. A value of 0 means that no agent is to begin working on that time slot. A value of n means n call center agents are expected to begin working on that time slot.

Here is how we compute the vector \vec{v}[/tex] and [tex]$\vec{z}$.

for(i in 1:length(req)){
if(v[i]  48*7 = 336

z[i] = z[i] + x;
# since an agent works 9 hours in a day,
# we have 9*2=18 slots
for(j in 0:17){
k=i + j;
l=k %% 337;
v[l] = v[l] + x;

The vector v is initialized to 0. For each component i of v, we compare it with the value req[i], which is the required number of agents for time slot i. If the value of v[i] is less than req[i], then we add the number of agents to start at that time slot, which is x=req[i] - v[i]. We update the vector z with this value, z[i]=z[i]+x. We then update the vector v by adding the value of x to all components of v starting at i until i+17 because an agent starting at timeslotiwill work 9 hours until i+17.

The code above is actually a code snippet from the program I created using R programming language. To know more about R, click this link.

Running this code in R, we get the following result:


The corresponding value of vector v is:


The difference of v from the required staffing req is :


The matrix form is more convenient:


This means that for slot 1, we 2 more agents rather than the required 1 agent at that time slot. These two agents will not be doing anything productive as far as their job is concerned. They are just there because they need to complete the 9 hours of work as stated in their contracts.

Now, is there a way for us to minimize this unproductivity while still meeting the required number of agents in the center? We will answer this in the next post.

Call Center scheduling Problem

This problem I got from someone working in a call center. The call center operation in question operates 24 hours a day, 7 days a week. Every 30 minutes, there is a projected number of agents that need to be present in order to handle calls. Here is an example of such a requirement:


The data shown is truncated. The should be 48 rows corresponding to 48 half-hour intervals in a day. The whole data can be downloaded from here.

Assuming that an agent works 9 hours per day, how do you schedule your staff in such a way that you can meet the required number of agents required per 30 minute interval using a minimum number agents.

Approach to solving this problem

Imagine for a moment that you are the manager of this call center and you want to solve this problem manually. Looking at the first row and first column of the data, you’ll see that at 12:00 am to 12:30 am on Mon you need 1 agent to be in the office. Since an agent works 9 hours per day, this agent’s duty will end at 9:00 am. If n is the total number of agents in the office at 9:00, then by the next interval, there will be n-1 agents left ( Assuming there is no new agent who comes to work).

Looking down the row under Monday, you will see that a new person is required to be in the office at 6:00 am. You then assign a new person to come to the office at this time.

Further down, another person is required at 8:30 am. However, at 9:00 am, the first person’s shift is up but the requirement is still 3 persons. So you need to assign another person to come to office a 9 am to meet the requirement. Continuing the process in this way, you can ultimately come up with a schedule of your call center agents.


You have to notice that people working at 3:30 pm will intersect the next days shift. They will leave the office at 12:30 am the next day. The same is true for all agents who come to the office between 3:30 to 12:30 pm. To make matters worse, those agents who come to work Sunday starting at 3:30 pm will have to intersect monday’s shift. This means that the one person you initially scheduled above will not be alone, but will have a companion coming from the sunday’s shift.

Big Question

The big question therefore is this: Is the schedule you came up with using the manual approach the best schedule in terms of minimizing the number of staff?

We will answer this question in our next article.