2022年2月28日月曜日

Benchmaks by First International Nurse Rostering Competition Instances

inrc1.md

First International Nurse Rostering Competition Instances

References

  1. First Nurse Scheduling Competition 2010

  2. Nurse Rostering Problem

Medium Instances

Speed Comparison

Optimality Proven Instances
Instance Name Cplex Gurobi ScheduleNurse3
medium-early01 44/3=14.7 3/3=1 47/3=15.7
medium-early02 24/6.8=3.5 45/6.8=6.6 6.8/6.8=1
medium-early03 20/6=3.3 6/6=1 9.3/6=1.6
medium-early04 8/8=1 15/8=1.9 47/8=5.9
medium-early05 21/9.9=2.1 15/9.9=1.5 9.9/9.9=1
medium-hidden01 - - -
medium-hidden02 - - -
medium-hidden03 - - 38/38=1
medium-hidden04 - - 78/78=1
medium-hidden05 - - 3390/3390=1
medium-late01 - 2682/175=15.3 175/175=1
medium-late02 3152=851.9 211/3.7=57.0 3.7/3.7=1
medium-late03 - 2503/13=192.5 13/13=1
medium-late04 12350/5=2470 165/5=33 5/5=1
medium-late05 - 790/139=5.7 139/139=1
Optimal Objective Reached Instances
Instance Name Cplex Gurobi ScheduleNurse3
medium-early01 44/3=14.7 3/3=1 47/3=15.7
medium-early02 24/6.8=3.5 45/6.8=6.6 6.8/6.8=1
medium-early03 20/6=3.3 6/6=1 9.3/6=1.6
medium-early04 8/8=1 15/8=1.9 47/8=5.9
medium-early05 21/9.9=2.1 15/9.9=1.5 9.9/9.9=1
medium-hidden01 - - -
medium-hidden02 - - -
medium-hidden03 - - 38/38=1
medium-hidden04 - - 78/78=1
medium-hidden05 - - 3390/3390=1
medium-late01 - 918/175=5.2 175/175=1
medium-late02 3152/3.3=955.2 211/3.3=63.9 3.3/3.3=1
medium-late03 - 508/13=39.1 13/13=1
medium-late04 2393/5=478.6 165/5=33 5/5=1
medium-late05 - 790/139=5.7 139/139=1

Time - Number of Instances proven optimality

Time - Number of Instances reached optimal objective

Detail Data

Long Instances

Optimality Proven Instances
Instance Name Cplex Gurobi ScheduleNurse3
long-early01 3/2=1.5 2/2=1 11/2=5.5
long-early02 15/14=1.1 14/14=1 82/14=6
long-early03 3/1=3 1/1=1 44/1=44
long-early04 4/2=2 2/2=1 71/2=35.5
long-early05 4/2=2 2/2=1 81/2=40.5
long-hidden01 - - 168/168=1
long-hidden02 - - 98/98=1
long-hidden03 - 27097/49=553 49/49=1
long-hidden04 - 9775/13=751.9 13/13=1
long-hidden05 - 2223/35=63.5 35/35=1
long-late01 - 3585/130=27.6 130/130=1
long-late02 - 5481/141=38.9 141/141=1
long-late03 - - 3740/3740=1
long-late04 - 6550/146=44.9 146/146=1
long-late05 - 550/75=7.3 75/75=1
Optimal Objective Reached Instances
Instance Name Cplex Gurobi ScheduleNurse3
long-early01 3/2=1.5 2/2=1 10/2=5
long-early02 15/14=1.1 14/14=1 82/14=6
long-early03 3/1=3 1/1=1 44/1=44
long-early04 4/2=2 2/2=1 71/2=35.5
long-early05 4/2=2 2/2=1 81/2=40.5
long-hidden01 - 3737/168=22.2 168/168=1
long-hidden02 - 586/98=6.0 98/98=1
long-hidden03 - 3392/48=70.7 48/48=1
long-hidden04 2662/13=204.8 488/13=37.5 13/13=1
long-hidden05 6979/35=199.4 518/35=14.8 35/35=1
long-late01 - 1529/130=11.8 130/130=1
long-late02 - 5481/141=38.9 141/141=1
long-late03 12668/135=93.8 6154/135=45.6 135/135=1
long-late04 - 1277/146=8.7 146/146=1
long-late05 1592/75=21.2 550/75=7.3 75/75=1

Time - Number of Instances proven optimality

Time - Number of Instances reached optimal objective

Detail Data

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2022年2月27日日曜日

Benchmarks by Classical Instances

classical.md

Speed Comparison

Optimality Proven Instances

Instance Name Cplex Gurobi AutoRoster ScheduleNurse3
QMC-1 2.95/2.25=1.3 2.25/2.25=1 - 8.5/2.25=3.8
SINTEF 1.89/0.78=2.4 0.78/0.78=1 9/0.78=11.5 1.15/0.78=1.5
ikegami-3Shift-DATA1.2 - 695/5.66=122.8 - 5.66/5.66=1
ikegami-3Shift-DATA1.1 6606/7.155=923.3 416/7.155=58.1 - 7.155/7.155=1
ikegami-3Shift-DATA1 1838/4=459.5 285/4=71.3 - 4/4=1
ikegami-2Shift-DATA1 9.23/0.14=65.9 0.14/0.14=1 11/0.14=78.6 1.94/0.14=13.9
GPOST-B 227/34=6.7 161/34=4.7 40/34=1.2 34/34=1
GPOST 124/2.8=44.3 22/2.8=7.9 17/2.8=6.1 2.8/2.8=1
Valouxis-1 - - - 37/37=1
WHPP - 4853/4=1213.3 17/4=4.3 4/4=1
BCDT-Sep - - - 140/140=1

Optimal Objective Reached Instances

Instance Name Cplex Gurobi AutoRoster ScheduleNurse3
QMC-1 2.95/2.25=1.3 2.25/2.25=1 140/2.25=62.2 8.5/2.25=3.8
SINTEF 1.89/0.78=2.4 0.78/0.78=1 9/0.78=11.5 1.146/0.78=1.5
ikegami-3Shift-DATA1.2 2573/4=643.3 184/4=46 - 4/4=1
ikegami-3Shift-DATA1.1 6606/3.94=1676.6 175/3.94=44.4 - 3.94/3.94=1
ikegami-3Shift-DATA1 1200/4=300 285/4=71.3 300/4=75 4/4=1
ikegami-2Shift-DATA1 9.23/0.14=65.9 0.14/0.14=1 11/0.14=78.6 1.94/0.14=13.9
GPOST-B 130/2.5=52 61/2.5=24.4 40/2.5=16 2.5/2.5=1
GPOST 124/2.3=53.9 22/2.3=9.6 17/2.3=7.4 2.3/2.3=1
Valouxis-1 663/3.91=170 224/3.91=57.3 9/3.91=2.3 3.91/3.91=1
WHPP - 4853/4=1213.3 17/4=4.3 4/4=1
BCDT-Sep - - - 140/140=1

Time - Number of Instances proven optimality

No. Instance Name
1 Millar-2Shift-DATA1.1
2 Millar-2Shift-DATA1
3 Ozkarahan
4 Musa
5 Azaiez
6 QMC-1
7 LLR
8 SINTEF
9 ikegami-3Shift-DATA1.2
10 ikegami-3Shift-DATA1.1
11 ikegami-3Shift-DATA1
12 ikegami-2Shift-DATA1
13 GPOST-B
14 BCV-4.13.1
15 GPOST
16 Valouxis-1
17 WHPP
18 BCDT-Sep

Time - Number of Instances reached optimal objective

No. Instance Name
1 Millar-2Shift-DATA1.1
2 Millar-2Shift-DATA1
3 Ozkarahan
4 Musa
5 Azaiez
6 QMC-1
7 LLR
8 SINTEF
9 ikegami-3Shift-DATA1.2
10 ikegami-3Shift-DATA1.1
11 ikegami-3Shift-DATA1
12 ikegami-2Shift-DATA1
13 GPOST-B
14 BCV-4.13.1
15 GPOST
16 Valouxis-1
17 WHPP
18 BCDT-Sep

Environment

Solver Version Machine
Gurobi Gurobi Optimizer version 9.5.0 build v9.5.0rc5 NEOS SERVER
AutoRoster RosterViewerDemo4.3.5 Branch and Price Ryzen 5800X 64GB
Cplex IBM(R) ILOG(R) CPLEX(R) Interactive Optimizer 20.1.0.0 NEOS SERVER
Schedule Nurse3 Algorithm3 Ryzen 5800X 64GB

References

  1. Asta, S., Özcan, E., and Curtois, T. A tensor based hyper-heuristic for nurse rostering. Knowledge-based systems, 2016. 98: p. 185-199.

  2. Burke E.K. and T. Curtois. New Approaches to Nurse Rostering Benchmark Instances. European Journal of Operational Research, 2014. 237(1): p. 71-81. pdf.

  3. Solos, Ioannis P., Ioannis X. Tassopoulos and Grigorios N. Beligiannis. A Generic Two-Phase Stochastic Variable Neighborhood Approach for Effectively Solving the Nurse Rostering Problem. Algorithms, 2013. 6: p. 278-308.

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2022年2月26日土曜日

Benchmaks by Second International Nurse Rostering Competition Instances

Untitled-4.md

Environment

Solver Version Machine
Gurobi Gurobi Optimizer version 9.5.0 build v9.5.0rc5 NEOS SERVER
AutoRoster RosterViewerDemo4.3.5 Branch and Price Ryzen 5800X 64GB
Cplex IBM(R) ILOG(R) CPLEX(R) Interactive Optimizer 20.1.0.0 NEOS SERVER
Schedule Nurse3 Algorithm3 Ryzen 5800X 64GB

References

  1. Second Nurse Scheduling Competition

  2. Second International Nurse Rostering Competition (INRC-II) — Problem Description and Rules —

  3. A rotation-based branch-and-price approach for the nurse scheduling problem

4weeks

Instance Weeks Employees Best known LB Best known UB Known Best Gap Schedule NurseⅢ LB Schedule NurseⅢ　UB Schedule Nurse Ⅲ　Gap Note
n030w4 1 6-2-9-1 4 30 1615 1685 4.33% 1670 1670 0.00%
n030w4 1 6-7-5-3 4 30 1740 1840 5.75% 1815 1815 0.00%
n035w4 0 1-7-1-8 4 35 1250 1415 13.20% 1360 1360 0.00%
n035w4 2 8-8-7-5 4 35 1045 1145 9.57% 1080 1080 0.00%
n040w4 0 2-0-6-1 4 40 1335 1640 22.85% 1565 1565 0.00%
n040w4 2 6-1-0-6 4 40 1570 1865 18.79% 1750 1750 0.00%
n050w4 0 0-4-8-7 4 50 1195 1445 20.92% 1320 1320 0.00%
n050w4 0 7-2-7-2 4 50 1200 1405 17.08% 1315 1315 0.00%
n060w4 1 6-1-1-5 4 60 2380 2465 3.57% 2455 2455 0.00%
n060w4 1 9-6-3-8 4 60 2615 2730 4.40% 2675 2675 0.00%
n070w4 0 3-6-5-1 4 70 2280 2430 6.58% 2380 2380 0.00%
n070w4 0 4-9-6-7 4 70 1990 2125 6.78% 2115 2115 0.00%
n080w4 2 4-3-3-3 4 80 3140 3320 5.73% 3300 3300 0.00%
n080w4 2 6-0-4-8 4 80 3045 3240 6.40% 3180 3190 0.31%
n100w4 0 1-1-0-8 4 100 1055 1230 16.59% 1170 1170 0.00%
n100w4 2 0-6-4-6 4 100 1470 1855 26.19% 1790 1790 0.00%
n110w4 0 1-4-2-8 4 110 2210 2390 8.14% 2330 2330 0.00%
n110w4 0 1-9-3-5 4 110 2255 2525 11.97% 2455 2455 0.00%
n120w4 1 4-6-2-6 4 120 1790 2165 20.95% 2020 2020 0.00% SC3 shows UB=2040, while Verilator shows UB=2020
n120w4 1 5-6-9-8 4 120 1820 2220 21.98% 2050 2050 0.00% SC3 shows UB=2090, while Verilator shows UB=2050.
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