Add PSY-style docs workflow concurrency guard.

[kdayday]

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Performance Results
Main

Network: 10 nodes, 13 edges, 3 cost segments
Generators: 5, Demands: 5
Loss coefficients (a, b, c) per generator:
  n1: a=0.007287  b=0.005968  c=0.003850
  n2: a=0.001094  b=0.009002  c=0.007028
  n3: a=0.007897  b=0.008552  c=0.003001
  n4: a=0.008179  b=0.002530  c=0.009637
  n5: a=0.009778  b=0.009934  c=0.009099

Solver logs: /home/runner/work/InfrastructureOptimizationModels.jl/InfrastructureOptimizationModels.jl/main/test/performance/logs/solver_2026-08-07T23-35-20.log

==============================================================================================================================================
Bilinear Approximation Benchmarks
  Refinement = depth for all methods
==============================================================================================================================================
Method          R   Vars Constrs   Bins    Objective   Gap(%) MIPGap(%)     LowerBnd  rmse δbi   max δbi   rmse δq    max δq  build_t  solve_t
----------------------------------------------------------------------------------------------------------------------------------------------
NLP (Ipopt)     -     40     105      0     0.956760        -         -            -  0.00e+00  0.00e+00  0.00e+00  0.00e+00   0.0322   0.0049

NLP (Uno)       -     40     105      0     0.956760   0.0000         -            -  0.00e+00  0.00e+00  0.00e+00  0.00e+00   0.0014   0.0012

Bin2+sSOS       4    190     535      0     1.180700  23.4061    0.0000     1.180700  8.07e-02  1.41e-01  2.49e+01  5.48e+01   0.0054   1.7657
Bin2+sSOS       6    250     655      0     1.098175  14.7806    0.0000     1.098175  1.26e-01  2.86e-01  5.47e+00  9.07e+00   0.0055   7.3850
Bin2+sSOS       8    310     775      0     1.053069  10.0661    0.0088     1.052976  8.62e-02  2.43e-01  4.18e+01  9.32e+01   0.0055  26.6357

Bin2+mSOS       4    310     805    120     1.180700  23.4061    0.0000     1.180700  8.07e-02  1.41e-01  2.49e+01  5.48e+01   0.0022   1.7621
Bin2+mSOS       6    430    1045    180     1.098175  14.7806    0.0092     1.098074  1.26e-01  2.86e-01  5.47e+00  9.07e+00   0.0024   7.4904
Bin2+mSOS       8    550    1285    240     1.053069  10.0661    0.0045     1.053021  8.62e-02  2.43e-01  4.18e+01  9.32e+01   0.0025  17.9751

Bin2+Saw        4    310    1075    120     0.985823   3.0376    0.0000     0.985823  1.08e-01  2.49e-01  1.11e+02  2.44e+02   0.0032   5.8311
Bin2+Saw        6    430    1495    180     0.958677   0.2004    0.0077     0.958603  7.52e-02  1.68e-01  8.94e+01  1.71e+02   0.0036  13.3584
Bin2+Saw        8    550    1915    240     0.956996   0.0247    0.0072     0.956927  7.52e-02  1.68e-01  1.37e+02  2.09e+02   0.0043  49.9952

HybS+sSOS       4    310    1165      0     0.812261  15.1030    0.0000     0.812261  5.52e-01  1.00e+00  2.04e+02  2.97e+02   0.0067   2.6706
HybS+sSOS       6    410    1545      0     0.891208   6.8515    0.0000     0.891208  5.49e-01  1.00e+00  3.69e+02  7.09e+02   0.0069   4.9232
HybS+sSOS       8    510    1925      0     0.934789   2.2965    0.0087     0.934707  5.48e-01  1.00e+00  3.47e+02  5.22e+02   0.0078  15.5957

HybS+mSOS       4    390    1345     80     0.812261  15.1030    0.0046     0.812224  5.52e-01  1.00e+00  2.04e+02  2.97e+02   0.0034   2.9014
HybS+mSOS       6    530    1805    120     0.891208   6.8515    0.0000     0.891208  5.49e-01  1.00e+00  3.69e+02  7.09e+02   0.0039   6.2471
HybS+mSOS       8    670    2265    160     0.934789   2.2965    0.0047     0.934744  5.48e-01  1.00e+00  3.47e+02  5.22e+02   0.0045  16.1686

This branch

Network: 10 nodes, 13 edges, 3 cost segments
Generators: 5, Demands: 5
Loss coefficients (a, b, c) per generator:
  n1: a=0.007287  b=0.005968  c=0.003850
  n2: a=0.001094  b=0.009002  c=0.007028
  n3: a=0.007897  b=0.008552  c=0.003001
  n4: a=0.008179  b=0.002530  c=0.009637
  n5: a=0.009778  b=0.009934  c=0.009099

Solver logs: /home/runner/work/InfrastructureOptimizationModels.jl/InfrastructureOptimizationModels.jl/branch/test/performance/logs/solver_2026-08-07T23-39-52.log

==============================================================================================================================================
Bilinear Approximation Benchmarks
  Refinement = depth for all methods
==============================================================================================================================================
Method          R   Vars Constrs   Bins    Objective   Gap(%) MIPGap(%)     LowerBnd  rmse δbi   max δbi   rmse δq    max δq  build_t  solve_t
----------------------------------------------------------------------------------------------------------------------------------------------
NLP (Ipopt)     -     40     105      0     0.956760        -         -            -  0.00e+00  0.00e+00  0.00e+00  0.00e+00   0.0317   0.0047

NLP (Uno)       -     40     105      0     0.956760   0.0000         -            -  0.00e+00  0.00e+00  0.00e+00  0.00e+00   0.0013   0.0012

Bin2+sSOS       4    190     535      0     1.180700  23.4061    0.0000     1.180700  8.07e-02  1.41e-01  2.49e+01  5.48e+01   0.0051   1.7939

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Codecov Report

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