# Team Desk Assignments

I was recently asked a question on the Flips GitHub page which I felt warranted a full blog post. It is an interesting problem that I have seen several variations on so I wanted to provide a more detailed model. The largest example of this problem I have seen is at Rocket Technology (formerly Quicken Loans) where they must assign desks to thousands of people across several buildings in downtown Detroit, MI.

You have a set of People that you need to assign desks to. Each person is a part of a Team, and you would rather that people sat with their Team. Each Desk is a part of a Cluster. A Cluster is a group of desks that are next to each other. Each Person is already assigned to a Desk. You want to come up with a seating assignment that maximizes the number of people who are sitting with their team while also minimizing the number of times that people must move from the desk they are sitting at currently.

## The Domain

Before we start putting our Mathematical Planning Model together, let’s create simple types to describe our domain. Based on the word description of our problem we already have four types: PersonId, TeamId, DeskId, and ClusterId. For the sake of simplicity, we are going to model all of these as single-case DUs with an int as the value.

type TeamId = TeamId of int
type DeskId = DeskId of int
type PersonId = PersonId of int
type ClusterId = ClusterId of int


I don’t have access to the original enquirers data set, so I need to create a synthetic data. I’ll start by creating a new System.Random and give it an initial seed value of 123 so I can reproduce my results and define the number of teams, persons, clusters, and desks in my problem.

let rng = System.Random 123
let teamCount = 5
let personCount = 22
let clusterCount = 8
let deskCount = 30


Now I’ll create some arrays of my data.

let teamIds =
[|1 .. teamCount|]
|> Array.map TeamId

let personIds =
[|1 .. personCount|]
|> Array.map PersonId

let clusterIds =
[|1 .. clusterCount|]
|> Array.map ClusterId

let deskIds =
[|1 .. deskCount|]
|> Array.map DeskId


I need to separate the people into which team they belong to. F# has a handy function in the Array module which makes it easy to divide an array into evenly divided chunks called Array.splitInto. You give the splitInto function the number of chunks you want, and it evenly divides the input array into that many chunks. Let’s now divide the people into which team they belong to.

let teams =
personIds
|> Array.splitInto teamIds.Length
|> Array.mapi (fun idx personIds ->
let teamId = teamIds[idx]
teamId, personIds


I now have a IReadOnlyDictionary<TeamId, array<PersonId>> which allows me to query which PersonId belong to a given TeamId.

I also want to divide the desks into which clusters they belong to. I’ll use the same function as before to evenly divide them.

let clusters =
deskIds
|> Array.splitInto clusterIds.Length
|> Array.mapi (fun idx desks ->
let clusterId = clusterIds[idx % clusterIds.Length]
clusterId, Set desks // Notice I'm creating a Set<DeskId>


Now I have a IReadOnlyDictionary<ClusterId, Set<DeskId>>. I can look up a ClusterId and get a Set which contains all the DeskId that belong to that ClusterId. Notice, I put the DeskId into a Set, not an array. You will see why that matters later.

Later in my problem I will need to lookup which ClusterId that a DeskId belongs to so I go ahead and create an IReadOnlyDictionary<DeskId, ClusterId> from the same data.

let deskToCluster =
clusters
|> Seq.collect (fun (KeyValue (clusterId, deskIds)) ->
deskIds
|> Seq.map (fun deskId ->
deskId, clusterId))


Each PersonId is already assigned to a desk. Since I don’t have access to the real data, I’m just going to assign each PersonId to a random DeskId and store that in a array<PersonId * DeskId>.

let currentPersonDeskAssignment =

let randomDeskOrder =
deskIds
|> Array.sortBy (fun _ -> rng.NextDouble ())

randomDeskOrder[0 .. personIds.Length - 1]
|> Array.zip personIds


## The Model

We now have a tiny domain and synthetic data. Let’s being building our model! We start with creating the decision variables which represent assigning a given PersonId to a particular DeskId. We will use a Boolean decision variable. A value of 1 will represent true. true means that we do assign a given PersonId to a given DeskId. A value of 0 represents false which means we do NOT assign a PersonId to a given DeskId. We store these decision variables in a SMap2<PersonId, DeskId, Decision> for easy slicing later.

let personDeskAssignment =
DecisionBuilder "PersonAssignment" {
for p in personIds do
for d in deskIds ->
Boolean
} |> SMap2


We now create a decision variable which represents assigning a TeamId to a ClusterId. The decision variable will be a Boolean where 1 means we do assign the TeamId to the ClusterId and 0 means we do not. We store these decisions in a SMap2<TeamId, ClusterId, Decision>.

let teamClusterAssignment =
DecisionBuilder "TeamClusterAssignment" {
for t in teamIds do
for c in clusterIds ->
Boolean
} |> SMap2


Alright, now we can start creating constraints. First thing we need to do is ensure that each PersonId is assigned to exactly 1 desk.

let eachPersonHasDeskConstraints =
ConstraintBuilder "EachPersonHasDesk" {
for p in personIds ->
sum personDeskAssignment[p,  All] == 1.0
}


Notice that the comparison is == and not <==. An == means that the sum MUST equal 1.0. If we had put <==, that would mean that the sum MAY equal 1.0 but it doesn’t have to. This would allow the optimization to not ensure that everyone has a spot. That’s not what we want though so we use == to ensure each PersonId will have a DeskId they are assigned to.

Next, we make sure that each DeskId is only assigned once.

let eachDeskOnlyOnceConstraints =
ConstraintBuilder "EachDeskOnlyOnce" {
for d in deskIds ->
sum personDeskAssignment[All, d] <== 1.0
}


Again, please notice the comparison that is being used here. Now we use <== instead of ==. We are saying a DeskId MAY be assigned up to once, but no more. We are not requiring that each DeskId have a PersonId assigned to it. This is an important distinction.

Now we want to make sure that each TeamId is assigned a ClusterId.

let eachTeamHasClusterConstraints =
ConstraintBuilder "EachTeamHasCluster" {
for t in teamIds ->
sum teamClusterAssignment[t, All] == 1.0
}


Because we use == in the comparison we are ensuring that each TeamId is assigned a ClusterId.

We now must ensure that each ClusterId is assigned only once.

let eachClusterOnlyOnceConstraints =
ConstraintBuilder "EachClusterOnlyOnce" {
for c in clusterIds ->
sum teamClusterAssignment[All, c] <== 1.0
}


We are using <== which is saying that we may assign each ClusterId once but we don’t have to.

### Team Co-Location

Now we get to the slightly complex part. This is where the original enquirer was stuck, and I completely understand why. Everything up to this point has been straightforward. Now we need to quantify the success of teams sitting together. To do this we are going to use something called Indicator Variables. Indicator Variables are used to indicate whether a certain condition is being met in a model or not. We are doing to use them to model whether a given PersonId is sitting with their assigned TeamId.

We will need to create a set of indicator variables for each TeamId, ClusterId, and PersonId combination.

let personCoLocated =
DecisionBuilder "TeamCoLocated" {
for t in teamIds do
for c in clusterIds do
for p in teams[t] ->
Boolean
} |> SMap3


We now have an SMap3<TeamId, ClusterId, PersonId, Decision> which we will use to model whether a PersonId is sitting with their TeamId at a given ClusterId.

You may have noticed that our assignment decisions were along different dimensions. PersonId are assigned to DeskId while TeamId are assigned to ClusterId. Now is when we bring together the dimensions of DeskId and ClusterId.

We want to maximize the number of times that a PersonId is assigned to the same ClusterId as their TeamId. Our objective function is going to be the sum of the personCoLocated decisions. To keep the optimizer from just turning all those values to 1.0 though we need to put constraints on them to make sure the necessary conditions are being met.

The first necessary condition is that a PersonId is assigned to a DeskId in the given ClusterId. Let’s code that up.

let personCoLocatedConstraints =
ConstraintBuilder "TeamPersonCoLocated" {
for t in teamIds do
for c in clusterIds do
for p in teams[t] ->
personCoLocated[t, c, p] <== sum personDeskAssignment[p, In clusters[c]]
}


Here we are saying that for the solver to turn a given personLocated decision to 1.0, at least one of the personDeskAssignment variables for the PersonId must be 1.0 as well. We are calculating that using this expression:

sum personDeskAssignment[p, In clusters[c]]


You’ll see that the filter on the second dimensions is an In filter. The In filter takes a Set as an input which is why we had to store the values in the cluster collection as a Set<DeskId>.

Now we need to cover the second condition, the TeamId must also be assigned to the ClusterId.

let teamCoLocatedConstraints =
ConstraintBuilder "TeamCoLocated" {
for t in teamIds do
for c in clusterIds do
for p in teams[t] ->
personCoLocated[t, c, p] <== teamClusterAssignment[t, c]
}


Here we are saying that if you want to turn the value of the personCoLocated decision to 1.0, you must have assigned the TeamId to that ClusterId.

## Putting it together

We can now put together all the components of our model. Let’s create the LinearExpression which is our objective function.

let colocationObjectiveExpr = sum personCoLocated

let coLocationObjective =
Objective.create "MaximizeCoLocation" Maximize colocationObjectiveExpr


We are saying that we want to maximize the number of times that a PersonId is assigned to the same ClusterId as the TeamId they are assigned to.

We have a secondary objective which is to minimize the number of times people are moved from their current seating assignment. To do this we create an expression which is the sum of whether a PersonId is assigned to the DeskId they are currently at.

let maxRetentionExpr =
seq {
for (p, d) in currentPersonDeskAssignment ->
1.0 * personDeskAssignment[p, d]
} |> Seq.sum

let maxRetentionObjective =
Objective.create "MaxRetention" Maximize maxRetentionExpr


With our two objectives and various constraints we can compose our full model.

let model =
Model.create coLocationObjective


We aren’t doing anything fancy so we will just use our default settings and attempt to solve.

let settings = Settings.basic

let result = Solver.solve settings model


Now let’s create a simple function to print out the results if we find a solution. This will just extract which assignments the solver is recommending we use and print that out to a simple table in the console.

match result with
| Optimal sln ->

let personDeskAssignmentValues = Solution.getValues sln personDeskAssignment

let selectedDeskAssignments =
personDeskAssignmentValues
|> Map.toSeq
|> Seq.filter (fun (_, value) -> value = 1.0)
|> Seq.map fst

let teamClusterAssignmentValues = Solution.getValues sln teamClusterAssignment

let selectedTeamClusterAssignment =
teamClusterAssignmentValues
|> Map.toSeq
|> Seq.filter (fun (_, value) -> value = 1.0)
|> Seq.map fst

printfn "Team/Cluster Assignments"
for (teamId, clusterId) in selectedTeamClusterAssignment do
printfn $"{teamId}" printfn$"{clusterId}"
printfn "=== People ==="

for personId in teams.[teamId] do
let deskId = selectedDeskAssignments[personId]
let clusterId = deskToCluster[deskId]
printfn \$"{personId} | {deskId} | {clusterId}"

| _ -> printfn "Uh Oh"


When we run all of this, we get the following result.

Team/Cluster Assignments
TeamId 1
ClusterId 2
=== People ===
PersonId 1 | DeskId 5 | ClusterId 2
PersonId 2 | DeskId 4 | ClusterId 1
PersonId 3 | DeskId 7 | ClusterId 2
PersonId 4 | DeskId 8 | ClusterId 2
PersonId 5 | DeskId 6 | ClusterId 2
TeamId 2
ClusterId 4
=== People ===
PersonId 6 | DeskId 15 | ClusterId 4
PersonId 7 | DeskId 14 | ClusterId 4
PersonId 8 | DeskId 16 | ClusterId 4
PersonId 9 | DeskId 1 | ClusterId 1
PersonId 10 | DeskId 13 | ClusterId 4
TeamId 3
ClusterId 3
=== People ===
PersonId 11 | DeskId 9 | ClusterId 3
PersonId 12 | DeskId 10 | ClusterId 3
PersonId 13 | DeskId 11 | ClusterId 3
PersonId 14 | DeskId 12 | ClusterId 3
TeamId 4
ClusterId 5
=== People ===
PersonId 15 | DeskId 20 | ClusterId 5
PersonId 16 | DeskId 17 | ClusterId 5
PersonId 17 | DeskId 19 | ClusterId 5
PersonId 18 | DeskId 18 | ClusterId 5
TeamId 5
ClusterId 6
=== People ===
PersonId 19 | DeskId 23 | ClusterId 6
PersonId 20 | DeskId 24 | ClusterId 6
PersonId 21 | DeskId 22 | ClusterId 6
PersonId 22 | DeskId 21 | ClusterId 6


You will see that in most cases people are sitting with their teams but in some cases they are not. This is a product of a mismatch between the number of people on each team and the number of desks in each cluster. You can play with this model yourself here. Change the variables and see what results you get!

This was a fun problem to put together and I was grateful for the question. If you have a question about whether Mathematical Planning can help you and your team, please send me an email at matthewcrews@gmail.com if you have any questions and subscribe so you can stay on top new posts and products I am offering.