lguerdan / dynamic-matching-solver Goto Github PK

To run the code, install numpy, pandas, matplotlib, seaborn, and gurobi. You will need to download the gurobi optimizer locally, then configure an academic license.

You will see several common variables in the code related to the problem formulation. These are:

• I: the number of resources (i=0 is for no match)
• J: the number of arriving agents
• d: the number of rounds each agent will leave before exit
• T: the total time rounds in the simulation (T=J+d)
• c: an array specifying the number of each copies for each resource
• k: an array specifying the wait time of each resource
• valid_matches: an (I,J,T) array specifying which agents will be present at each time given arrivals and the agent wait period.
• pairing_weights: an (I,J,T) array specifying the utility of matching an agent and resource at a given time

Included files:

• Linear program solvers and utility functions are defined in dynamic_matching_solvers.py: there is a primal solver, dual solver, greedy solver, and online-dual solver.
• dynamic_matching_experiments.ipynb runs experiments based on the solvers implemented in the main file.
• dynamic_matching_solver_sim_forward.ipynb contains an experimental implementation of simulating future arrivals, and making allocations based on these results. This implementation has not been extensively tested, and needs to be modified such that:
  1. The primal solver runs a comparison for each i,t in the simulation interval to decide whether an allocation should occur.
  2. A dual solver creates an average alpha vector from many runs, and allocates based on this vector.

To create a scenario and run the solvers, run:

J=6
I=3
d=2
T = J+d 

k = np.array([1,3,3])
c = np.array([J,1, 1])

valid_matches, pairing_weights = create_simulation_scenario(I,J,T)


objp, allocs = primal_solutions(valid_matches, pairing_weights, I, J, T, k, c)
objd, alphas, betas = dual_solutions(valid_matches,pairing_weights, I, J, T, k, c)
objo, online_allocations = online_matching(I,J,T,k,c,alphas,betas,allocs,valid_matches,pairing_weights)
objg, greedy_allocs = greedy_matching(I,J,T,k,c,d,valid_matches,pairing_weights)

You can then visualize allocations using display_3D.