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Allocation problem optimization on Monte Carlo simulated allocation datasets

0.2295Per-Capita ValueOPT (Exact MILP solution)
0.1940360.2032430.212450.221657May 12, 2026

Evaluation results

MethodLinks
OPT (Exact MILP solution)sample size (n)=1502026.050.2295———
Greedy–Lagrangian (GLC)sample size (n)=1502026.050.22720.0023——
OPT (Exact MILP solution)sample size (n)=3002026.050.2138———
OPT (Exact MILP solution)sample size (n)=2002026.050.212———
Greedy–Lagrangian (GLC)sample size (n)=3002026.050.2120.0017——
OPT (Exact MILP solution)sample size (n)=2502026.050.2113———
OPT (Exact MILP solution)sample size (n)=5002026.050.2112———
Greedy–Lagrangian (GLC)sample size (n)=2002026.050.21090.001——
Greedy–Lagrangian (GLC)sample size (n)=5002026.050.20990.0013——
Greedy–Lagrangian (GLC)sample size (n)=2502026.050.20980.0015——
OPT (Exact MILP solution)sample size (n)=3502026.050.2091———
Greedy–Lagrangian (GLC)sample size (n)=3502026.050.20810.001——
OPT (Exact MILP solution)sample size (n)=4002026.050.207———
OPT (Exact MILP solution)sample size (n)=4502026.050.2061———
Greedy–Lagrangian (GLC)sample size (n)=4002026.050.20560.0014——
Greedy–Lagrangian (GLC)sample size (n)=4502026.050.20510.0011——
OPT (Exact MILP solution)sample size (n)=1002026.050.2045———
OPT (Exact MILP solution)sample size (n)=502026.050.2001———
Greedy–Lagrangian (GLC)sample size (n)=1002026.050.19860.0059——
Greedy–Lagrangian (GLC)sample size (n)=502026.050.19540.0047——
LP relaxation (LP)sample size (n)=502026.05——0.00441.6
LP relaxation (LP)sample size (n)=1002026.05——0.0011.3
LP relaxation (LP)sample size (n)=1502026.05——0.00061.3
LP relaxation (LP)sample size (n)=2002026.05——0.00041.3
LP relaxation (LP)sample size (n)=2502026.05——0.00021
LP relaxation (LP)sample size (n)=3002026.05——0.00021.2
LP relaxation (LP)sample size (n)=3502026.05——0.00011.3
LP relaxation (LP)sample size (n)=4002026.05——0.00011.6
LP relaxation (LP)sample size (n)=4502026.05——0.00011.4
LP relaxation (LP)sample size (n)=5002026.05——0.00011.2