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 |
Classical Benchmarks
References
-
Asta, S., Özcan, E., and Curtois, T. A tensor based hyper-heuristic for nurse rostering. Knowledge-based systems, 2016. 98: p. 185-199.
-
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.
-
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.
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 |
Detail Data







Second International Nurse Rostering Competition Instances
References
-
Second International Nurse Rostering Competition (INRC-II) — Problem Description and Rules —
-
A rotation-based branch-and-price approach for the nurse scheduling problem
-
CESCHIA, S.; GUIDO, R.; SCHAERF, A. Solving the static inrc-ii nurse rostering problem by simulated annealing based on large neighborhoods. Annals of Operations Research, Springer, p. 1–19, 2020.
-
GOMES, R. A.; TOFFOLO, T. A.; SANTOS, H. G. Variable neighborhood search accelerated column generation for the nurse rostering problem. Electronic Notes in Discrete Mathematics, Elsevier, v. 58, p. 31–38, 2017.
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% | 1780 | 1780 | 0.00% | SC3 shows UB=1790, while Verilator shows UB=1780 |
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. |
New INRC2 4weeks Data
Schedule Nurse 3 (Ryzen5800X 64GB Win10) | Mathematical Models and a Late Acceptance Fix-and-Optimize Approach for a Nurse Rostering Problem (ufrgs.br) | |||||||||||||||||
Legrain et al. (2019) | Gomes et al. (2017) | Ceschia et al. (2020) | LAFO | |||||||||||||||
LB=A Validator(SC3) | UB Validator(SC3) | Optimality Proven Time(sec) | UB reached time(sec) | GAP( (obj-A)/A*100)[%] | UB | Time | GAP( (obj-A)/A*100)[%] | UB | Time | GAP( (obj-A)/A*100)[%] | UB | Time | GAP( (obj-A)/A*100)[%] | UB | Time | GAP( (obj-A)/A*100)[%] | ||
staff=35 | n035w4_2_8-8-7-5 | 1080 | 1080 | 275 | 275 | 0 | 1,145 | 1,803 | 6.0 | 1,085 | 5,586 | 0.5 | 1,151 | 1,317 | 6.6 | 1,237.00 | 5,160 | 14.5 |
n035w4_0_1-7-1-8 | 1360 | 1360 | 471 | 471 | 0 | 1,415 | 1,803 | 4.0 | 1,425 | 3,269 | 4.8 | 1,455 | 1,317 | 7.0 | 1,565.90 | 5,160 | 15.1 | |
n035w4_0_4-2-1-6 | 1605 | 1605 | 203 | 103 | 0 | 1,705 | 1,803 | 6.2 | 1,615 | 5,124 | 0.6 | 1,663 | 1,317 | 3.6 | 1,760.50 | 5,160 | 9.7 | |
n035w4_0_5-9-5-6 | 1500 | 1500 | 5188 | 241 | 0 | 1,575 | 1,803 | 5.0 | 1,540 | 6,872 | 2.7 | 1,544 | 1,317 | 2.9 | 1,628.30 | 5,160 | 8.6 | |
n035w4_0_9-8-7-7 | 1335 | 1335 | 2460 | 1110 | 0 | 1,430 | 1,803 | 7.1 | 1,365 | 4,475 | 2.2 | 1,421 | 1,317 | 6.4 | 1,500.00 | 5,160 | 12.4 | |
n035w4_1_0-6-9-2 | 1300 | 1300 | 361 | 361 | 0 | 1,375 | 1,803 | 5.8 | 1,385 | 5,359 | 6.5 | 1,391 | 1,317 | 7.0 | 1,487.00 | 5,160 | 14.4 | |
n035w4_2_8-6-7-1 | 1080 | 1080 | 287 | 287 | 0 | 1,425 | 1,803 | 31.9 | 1,335 | 6,453 | 23.6 | 1,340 | 1,317 | 24.1 | 1,455.50 | 5,160 | 34.8 | |
n035w4_2_9-2-2-6 | 1080 | 1080 | 294 | 294 | 0 | 1,595 | 1,803 | 47.7 | 1,525 | 6,204 | 41.2 | 1,577 | 1,317 | 46.0 | 1,696.50 | 5,160 | 57.1 | |
n035w4_2_9-7-2-2 | 1080 | 1080 | 291 | 291 | 0 | 1,550 | 1,803 | 43.5 | 1,480 | 12,340 | 37.0 | 1,539 | 1,317 | 42.5 | 1,624.00 | 5,160 | 50.4 | |
n035w4_2_9-9-2-1 | 1080 | 1080 | 284 | 284 | 0 | 1,540 | 1,803 | 42.6 | 1509 | 1,317 | 39.7 | 1,651.50 | 5,160 | 52.9 | ||||
staff=70 | n070w4_0_3-6-5-1 | 2380 | 2380 | 35125 | 480 | 0 | 2,430 | 3,206 | 2.1 | 2,460 | 3,640 | 3 | 2,455.00 | 2,342 | 3 | 2,842.50 | 5,160 | 19.4 |
n070w4_0_4-9-6-7 | 2115 | 2115 | 593 | 593 | 0 | 2,125 | 3,206 | 0.5 | 2,330 | 4,943 | 10.2 | 2,190.00 | 2,342 | 3.5 | 2,535.50 | 5,160 | 19.9 | |
n070w4_0_4-9-7-6 | 2140 | 2140 | 914 | 914 | 0 | 2,210 | 3,206 | 3.3 | 2,315 | 9,465 | 8.2 | 2,229.00 | 2,342 | 4.2 | 2,587.00 | 5,160 | 20.9 | |
n070w4_0_8-6-0-8 | 2285 | 2285 | 10433 | 659 | 0 | 2,320 | 3,206 | 1.5 | 2,400 | 1,795 | 5.0 | 2,345.50 | 2,342 | 2.6 | 2,668.50 | 5,160 | 16.8 | |
n070w4_0_9-1-7-5 | 2080 | 2080 | 425 | 425 | 0 | 2,100 | 2,342 | 1.0 | 2,225 | 3,395 | 7.0 | 2,147.00 | 2,342 | 3.2 | 2,448.30 | 5,160 | 17.7 | |
n070w4_1_1-3-8-8 | 2080 | 2080 | 425 | 425 | 0 | 2,530 | 2,342 | 21.6 | 2,615 | 3,457 | 25.7 | 2,582.50 | 2,342 | 24.2 | 2,915.40 | 5,160 | 40.2 | |
n070w4_2_0-5-6-8 | 2270 | 2280 | 4665 | 4665 | 0 | 2,360 | 3,206 | 4.0 | 2,415 | 2,990 | 6.4 | 2,365.00 | 2,342 | 4.2 | 2,688.40 | 5,160 | 18.4 | |
n070w4_2_3-5-8-2 | 2325 | 2335 | 525 | 525 | 0 | 2,380 | 2,342 | 2.4 | 2,405 | 5,032 | 3.4 | 2,424.50 | 2,342 | 4.3 | 2,690.00 | 5,160 | 15.7 | |
n070w4_2_5-8-2-5 | 2290 | 2295 | 513 | 513 | 0 | 2,345 | 3,206 | 2.4 | 2,390 | 7,580 | 4.4 | 2,366.50 | 2,342 | 3.3 | 2,653.40 | 5,160 | 15.9 | |
n070w4_2_9-5-6-5 | 2355 | 2365 | 426 | 426 | 0 | 2,465 | 3,206 | 4.7 | 2,480 | 2,495 | 5.3 | 2,416.00 | 2,342 | 2.6 | 2,764.50 | 5,160 | 17.4 | |
staff=110 | n110w4_0_1-4-2-8 | 2330 | 2330 | 25537 | 760 | 0 | 2,390 | 4,809 | 2.6 | 2,560 | 13,084 | 9.9 | 2,387.50 | 3,513 | 2.5 | 3,020.00 | 5,160 | 29.6 |
n110w4_0_1-9-3-5 | 2455 | 2455 | 402 | 402 | 0 | 2,525 | 4,809 | 2.9 | 2,640 | 9,624 | 7.5 | 2,566.50 | 3,513 | 4.5 | 3,205.50 | 5,160 | 30.6 | |
n110w4_1_0-1-6-4 | 2530 | 2530(2785) | 305 | 305 | 0 | 2,680 | 4,809 | 5.9 | 2,690 | 24,585 | 6.3 | 2,609.00 | 3,513 | 3.1 | 3,241.00 | 5,160 | 28.1 | |
n110w4_1_0-5-8-8 | 2470 | 2475 | 415 | 0.2 | 2,625 | 4,809 | 6.3 | 2,705 | 12,838 | 9.5 | 2,596.00 | 3,513 | 5.1 | 3,254.00 | 5,160 | 31.7 | ||
n110w4_1_2-9-2-0 | 2870 | 2875 | 1641 | 0 | 2,975 | 3,513 | 3.7 | 3,170 | 11,570 | 10.5 | 3,032.00 | 3,513 | 5.6 | 3,646.00 | 5,160 | 27.0 | ||
n110w4_1_4-8-7-2 | 2430 | 2430 | 4740 | 2147 | 0 | 2,570 | 4,809 | 5.8 | 2,630 | 8,350 | 8.2 | 2,545.50 | 3,513 | 4.8 | 3,217.50 | 5,160 | 32.4 | |
n110w4_2_0-2-7-0 | 2640 | 2640 | 7212 | 2193 | 0 | 2,780 | 4,809 | 5.3 | 2,960 | 10,882 | 12.1 | 2,763.50 | 3,513 | 4.7 | 3,388.50 | 5,160 | 28.4 | |
n110w4_2_5-1-3-0 | 2640 | 2640 | 604 | 604 | 0 | 2,700 | 4,809 | 2.3 | 2,770 | 9,079 | 4.9 | 2,719.00 | 3,513 | 3.0 | 3,285.50 | 5,160 | 24.5 | |
n110w4_2_8-9-9-2 | 2855 | 2860 | 4454 | 0.2 | 2,980 | 3,513 | 4.4 | 3,140 | 15,184 | 10.0 | 3,049.00 | 3,513 | 6.8 | 3,720.90 | 5,160 | 30.3 | ||
n110w4_2_9-8-4-9 | 2695 | 2700 | 1274 | 0.2 | 2,775 | 3,513 | 3.0 | 3,005 | 11,311 | 11.5 | 2,834.00 | 3,513 | 5.2 | 3,449.00 | 5,160 | 28.0 |
Note: New best objective function values by a validator are available per the following links.
https://github.com/sugawara-system/Schedule_Nurse3_Gallery/tree/main/English/Benchmarks/INRC2/4weeks
Detail Data





















8weeks
Instance | Weeks | Employees | Best known LB | Best known UB | Known Best Gap | Schedule NurseⅢ LB | Schedule NurseⅢ UB | Schedule Nurse Ⅲ Gap | Note |
---|---|---|---|---|---|---|---|---|---|
n030w8 1 2-7-0-9-3-6-0-6 | 8 | 30 | 1920 | 2070 | 7.81% | 1994 | 2010 | 0.80% | |
n030w8 1 6-7-5-3-5-6-2-9 | 8 | 30 | 1620 | 1735 | 7.10% | 1710 | 1730 | 1.17% | |
n035w8 0 6-2-9-8-7-7-9-8 | 8 | 35 | 2330 | 2555 | 9.66% | 2408 | 2445 | 1.54% | |
n035w8 1 0-8-1-6-1-7-2-0 | 8 | 35 | 2180 | 2305 | 5.73% | 2153 | 2245 | 4.27% | |
n040w8 0 0-6-8-9-2-6-6-4 | 8 | 40 | 2340 | 2620 | 11.97% | 2464 | 2540 | 3.08% | |
n040w8 2 5-0-4-8-7-1-7-2 | 8 | 40 | 2205 | 2420 | 9.75% | 2285 | 2315 | 1.31% | |
n050w8 1 1-7-8-5-7-4-1-8 | 8 | 50 | 4625 | 4900 | 5.95% | 4778 | 4825 | 0.98% | |
n050w8 1 9-7-5-3-8-8-3-1 | 8 | 50 | 4530 | 4925 | 8.72% | 4744 | 4770 | 0.55% | |
n060w8 0 6-2-9-9-0-8-1-3 | 8 | 60 | 1970 | 2345 | 19.04% | 2099 | 2155 | 2.67% | |
n060w8 2 1-0-3-4-0-3-9-1 | 8 | 60 | 2260 | 2590 | 14.60% | 2394 | 2440 | 1.92% | |
n070w8 0 3-3-9-2-3-7-5-2 | 8 | 70 | 4400 | 4595 | 4.43% | 4475 | 4540 | 1.45% | |
n070w8 0 9-3-0-7-2-1-1-0 | 8 | 70 | 4540 | 4760 | 4.85% | 4637 | 4675 | 0.82% | |
n080w8 1 4-4-9-9-3-6-0-5 | 8 | 80 | 3775 | 4180 | 10.73% | 3942 | 4015 | 1.85% | |
n080w8 2 0-4-0-9-1-9-6-2 | 8 | 80 | 4125 | 4450 | 7.88% | 4287 | 4325 | 0.89% | |
n100w8 0 0-1-7-8-9-1-5-4 | 8 | 100 | 2005 | 2125 | 5.99% | 2026 | 2045 | 0.94% | |
n100w8 1 2-4-7-9-3-9-2-8 | 8 | 100 | 2125 | 2210 | 4.00% | 2153 | 2170 | 0.79% | |
n110w8 0 2-1-1-7-2-6-4-7 | 8 | 110 | 3870 | 4010 | 3.62% | 3990 | 3990 | 0.00% | SC3 shows UB=4050, while Verilator shows UB=3990 |
n110w8 0 3-2-4-9-4-1-3-7 | 8 | 110 | 3375 | 3560 | 5.48% | 3450 | 3450 | 0.00% | SC3 shows UB=3510, while Verilator shows UB=3450 |
n120w8 0 0-9-9-4-5-1-0-3 | 8 | 120 | 2295 | 2600 | 13.29% | 2485 | 2490 | 0.20% | |
n120w8 1 7-2-6-4-5-2-0-2 | 8 | 120 | 2535 | 3095 | 22.09% | 2912 | 2920 | 0.27% |
Detail Data




















Nurse Rostering Benchmark Instances
References
-
computational_results_on_new_staff_scheduling_benchmark_instances
-
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.
-
Strandmark, P., Qu, Y. and Curtois, T. First-order linear programming in a column generation-based heuristic approach to the nurse rostering problem. Computers & Operations Research, 2020. 120, p. 104945. (pdf)
-
Demirović, E., Musliu, N., and Winter, F. Modeling and solving staff scheduling with partial weighted maxSAT. Annals of Operations Research, 2019. 275(1): p. 79-99.
-
Smet P. Constraint reformulation for nurse rostering problems, in: PATAT 2018 twelfth international conference on the practice and theory of automated timetabling, Vienna, August, 2018, p. 69-80.
-
Rahimian, E., Akartunalı, K., and Levine, J. A hybrid integer programming and variable neighbourhood search algorithm to solve nurse rostering problems. European Journal of Operational Research, 2017. 258(2): p. 411-423.
Speed Comparison

Optimality Proven Instances
Instance Name | Cplex | Gurobi | AutoRoster | ScheduleNurse3 |
---|---|---|---|---|
Instance4 | 4.4/0.6=7.3 | 4/0.6=6.7 | 6/0.6=10 | 0.6/0.6=1 |
Instance5 | 29/2.4=12.1 | 16/2.4=6.7 | - | 2.4/2.4=1 |
Instance6 | 7/1.6=4.4 | 5/1.6=3.1 | - | 1.6/1.6=1 |
Instance7 | 61/6.2=9.8 | 20/6.2=3.2 | - | 6.2/6.2=1 |
Instance8 | 4623/50=92.5 | 931/50=18.6 | - | 50/50=1 |
Instance9 | - | - | - | - |
Instance10 | 41/13=3.2 | 20/13=1.5 | 660/13=50.8 | 13/13=1 |
Instance11 | 45/18=2.5 | 18/18=1 | 71/18=3.9 | 37/18=2.1 |
Instance12 | 260/54=4.8 | 185/54=3.4 | 660/54=12.2 | 54/54=1 |
Instance13 | - | 12115/572=21.2 | - | 572/572=1 |
Instance14 | 690/4.3=160.5 | 205/4.3=47.7 | - | 4.3/4.3=1 |
Instance15 | - | - | - | - |
Instance16 | 937/4.3=217.9 | 78/4.3=18.1 | - | 4.3/4.3=1 |
Instance17 | 4022/4.8=837.9 | 143/4.8=29.8 | 10000/4.8=2083.3 | 4.8/4.8=1 |
Instance18 | 21387/135=158.4 | 787/135=5.8 | - | 135/135=1 |
Instance19 | - | 3006/3006=1 | - | 5285/3006=1.8 |
Instance20 | - | 3665/678=5.4 | - | 678/678=1 |
Optimal Objective Reached Instances
|
Instance Name | Cplex | Gurobi | AutoRoster | ScheduleNurse3 |
---|---|---|---|---|
Instance4 | 4.4/0.5=8.8 | 4/0.5=8 | 6/0.5=12 | 0.5/0.5=1 |
Instance5 | 29/2.4=12.1 | 16/2.4=6.7 | 11/2.4=4.6 | 2.4/2.4=1 |
Instance6 | 7/1.6=4.4 | 5/1.6=3.1 | - | 1.6/1.6=1 |
Instance7 | 61/6.2=9.8 | 20/6.2=3.2 | - | 6.2/6.2=1 |
Instance8 | 4623/50=92.5 | 931/50=18.6 | - | 50/50=1 |
Instance9 | - | - | - | - |
Instance10 | 41/13=3.2 | 20/13=1.5 | 660/13=50.8 | 13/13=1 |
Instance11 | 45/18=2.5 | 18/18=1 | 71/18=3.9 | 37/18=2.1 |
Instance12 | 260/54=4.8 | 185/54=3.4 | 660/54=12.2 | 54/54=1 |
Instance13 | - | 12115/572=21.2 | - | 572/572=1 |
Instance14 | 690/4.3=160.5 | 205/4.3=47.7 | - | 4.3/4.3=1 |
Instance15 | - | - | - | - |
Instance16 | 937/4.3=217.9 | 78/4.3=18.1 | - | 4.3/4.3=1 |
Instance17 | 4022/4.8=837.9 | 143/4.8=29.8 | 10000/4.8=2083.3 | 4.8/4.8=1 |
Instance18 | 21387/135=158.4 | 787/135=5.8 | - | 135/135=1 |
Instance19 | - | 3006/3006=1 | - | 5285/3006=1.8 |
Instance20 | - | 3665/678=5.4 | - | 678/678=1 |
Time - Number of Instances proven optimality


Time - Number of Instances reached optimal objective


Detail Data
















First International Nurse Rostering Competition Instances
References
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














