Wedding Table Assignment Problem using Julia

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This is a port from Python of the Wedding Table Assignment example from PuLP.

This is an example of a set partitioning problem. We have a universe of guests and subsets of tables that have to exactly cover our universe, i.e. the guests.

using JuMP, Cbc
using Combinatorics

We start with a little helper function to calculate “happiness” of a table, determined by how close the guests are (by their letter):

function happiness(table):
    """
    Find the happiness of the table
    - by calculating the maximum distance between the letters
    """
    return abs(Int(table[1][1]) - Int(table[end][1]))
end

We set up the number of table, table size, and an array of our guests:

max_tables = 5
max_table_size = 4
guests = ["A" "B" "C" "D" "E" "F" "G" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R"]
1×17 Array{String,2}:
 "A"  "B"  "C"  "D"  "E"  "F"  "G"  "I"  …  "L"  "M"  "N"  "O"  "P"  "Q"  "R"

Then we generate the possible combinations of guests to tables up the maximum table size:

table_combos = [collect(combinations(guests, t)) for t in 1:max_table_size];
table_combos[end][end]    
4-element Array{String,1}:
 "O"
 "P"
 "Q"
 "R"

And we flatten that out:

possible_tables = []
for i in 1:length(table_combos)
    append!(possible_tables, table_combos[i])
end

We end up with 3,213 possible tables. Time to run an optimizer to pick the best combos!

length(possible_tables)

3213

Our Model:

m = Model(solver=CbcSolver())
num_possible_tables = length(possible_tables)
idx_possible_tables = 1:num_possible_tables

@variable(m, table_assignment[idx_possible_tables], Bin)

# Objective: maximize happiness = minimize happiness value
@objective(m, Min, sum([happiness(possible_tables[t]) * table_assignment[t]
    for t in idx_possible_tables]))

@constraint(m, sum([table_assignment[t]
    for t in idx_possible_tables]) <= max_tables)


# because this is a set partitioning problem, we enforce a strict equality constraint
# - i.e. every guest has to be seated
for guest in guests
    @constraint(m, sum([table_assignment[t] for t in idx_possible_tables
        if guest in possible_tables[t]]) == 1)
end

;

(This is a fairly large model, so we won’t print it)

status = solve(m)

println("Objective value: ", getobjectivevalue(m))
Objective value: 12.0

Collective happiness is 12, apparently. :)

table_assignment

We end up with the following table assignments, optimized by guest happiness:

[("Table: ", possible_tables[i], "Happiness: ", happiness(possible_tables[i]))
    for i=1:length(table_assignment) if getvalue(table_assignment[i]) == 1 ]
5-element Array{Tuple{String,Array{String,1},String,Int64},1}:
 ("Table: ", String["Q", "R"], "Happiness: ", 1)          
 ("Table: ", String["E", "F", "G"], "Happiness: ", 2)     
 ("Table: ", String["A", "B", "C", "D"], "Happiness: ", 3)
 ("Table: ", String["I", "J", "K", "L"], "Happiness: ", 3)
 ("Table: ", String["M", "N", "O", "P"], "Happiness: ", 3)

Updated:

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