1 Introduction
2 Related works
Generator | MS-RCPSP instances | Solvers | Validation | Fitness type | Visualization |
---|---|---|---|---|---|
.jar application | d36 (30+ d1-d6) | Greedy | .jar application | Cost/duration | .jar application |
small EDU (6) | GA with Greedy | \(w_\tau \) weighted | |||
bi-criteria | |||||
– | d36: part d1–d6 | Heuristics (Skowroński et al. 2013b) | – | Cost/duration | – |
– | d36: part d1–d6 | Tabu Search (Skowroński et al. 2013a) | – | Cost/duration | – |
– | d36: part d1–d6 | GA spec. op. (Skowroński and Myszkowski 2013) | – | Cost/duration | – |
– | d36 | Greedy (Myszkowski et al. 2015b) | – | Cost/duration | – |
– | d36 | GRASP (Myszkowski and Siemieński 2016) | – | Cost/duration | – |
– | d36 | HantCo (Myszkowski et al. 2015a) | – | Duration/cost | – |
– | d36 | HantCo (Myszkowski et al. 2018) | – | \(w_\tau \) Weighted | – |
– | d36 | Coev.Algorithm (Myszkowski et al. 2017a) | – | Duration | – |
– | d36 | DEGR (Myszkowski et al. 2018) | – | Duration/cost | – |
– | d36 | DEGR (Myszkowski et al. 2017b) | – | \(w_\tau \) weighted | – |
– | d36 | NTGA (Myszkowski et al. 2017b) | – | Bi-criteria | – |
– | d36 | TLBO (Zheng et al. 2017) | – | Duration/cost | – |
– | d36 | MOFA (Wang and Zheng 2018) | – | Bi-criteria | – |
3 Formulation of MS-RCPSP
4 iMOPSE library
4.1 Instance generator
4.2 Predefined instances: small and benchmark iMOPSE
Instance | Tasks | Resources | Relations | Skills |
---|---|---|---|---|
10_3_5_3 | 10 | 3 | 5 | 3 |
10_5_8_5 | 10 | 5 | 8 | 5 |
10_7_10_7 | 10 | 7 | 10 | 7 |
15_3_5_3 | 15 | 3 | 5 | 3 |
15_6_10_6 | 15 | 6 | 10 | 6 |
15_9_12_9 | 15 | 9 | 12 | 9 |
Instance | Tasks | Resources | Relations | Skills |
---|---|---|---|---|
100_20_23_9_D1 | 100 | 20 | 23 | 9 |
100_20_22_15 | 100 | 20 | 22 | 15 |
100_20_47_9 | 100 | 20 | 47 | 9 |
100_20_46_15 | 100 | 20 | 46 | 15 |
100_20_65_9 | 100 | 20 | 65 | 9 |
100_20_65_15 | 100 | 20 | 65 | 15 |
100_10_27_9_D2 | 100 | 10 | 27 | 9 |
100_10_26_15 | 100 | 10 | 26 | 15 |
100_10_47_9 | 100 | 10 | 47 | 9 |
100_10_48_15 | 100 | 10 | 48 | 15 |
100_10_64_9 | 100 | 10 | 64 | 9 |
100_10_65_15 | 100 | 10 | 65 | 15 |
100_5_20_9_D3 | 100 | 5 | 20 | 9 |
100_5_20_15 | 100 | 5 | 22 | 15 |
100_5_48_9 | 100 | 5 | 48 | 9 |
100_5_48_15 | 100 | 5 | 46 | 15 |
100_5_64_9 | 100 | 5 | 64 | 9 |
100_5_64_15 | 100 | 5 | 64 | 15 |
200_40_45_9 | 200 | 40 | 45 | 9 |
200_40_45_15 | 200 | 40 | 45 | 15 |
200_40_90_9 | 200 | 40 | 90 | 9 |
200_40_91_9 | 200 | 40 | 91 | 15 |
200_40_130_9_D4 | 200 | 40 | 130 | 9 |
200_40_144_15 | 200 | 40 | 133 | 15 |
200_20_55_9 | 200 | 20 | 55 | 9 |
200_20_54_15 | 200 | 20 | 54 | 15 |
200_20_97_9 | 200 | 20 | 97 | 9 |
200_20_97_15 | 200 | 20 | 97 | 15 |
200_20_150_9_D5 | 200 | 20 | 150 | 9 |
200_20_145_15 | 200 | 20 | 145 | 15 |
200_10_50_9 | 200 | 10 | 50 | 9 |
200_10_50_15 | 200 | 10 | 50 | 15 |
200_10_84_9 | 200 | 10 | 84 | 9 |
200_10_85_15 | 200 | 10 | 85 | 15 |
200_10_135_9_D6 | 200 | 10 | 135 | 9 |
200_10_128_15 | 200 | 10 | 128 | 15 |
4.3 iMOPSE - a library project
4.4 Solver: Greedy
4.5 Solver: Greedy guided by Genetic Algorithm
4.6 Solution validator
4.7 Solution visualization
\(w_\tau \)
| \(w_\tau \)=0.0 | \(w_\tau \)=1.0 | ||||||
---|---|---|---|---|---|---|---|---|
Instances | Best | Average | Best | Average | ||||
Cost | Duration | Cost | Duration | Cost | Duration | Cost | Duration | |
10_3_5_3 | 10845.3 | 149 | 10845.3 | 149 | 12992.7 | 108 | 11987.3 | 143 |
10_5_8_5 | 9013.6 | 154 | 9013.6 | 154 | 10736.4 | 100 | 10979.8 | 129 |
10_7_10_7 | 10215.2 | 149 | 10215.2 | 149 | 14153.0 | 104 | 15429.5 | 128 |
15_3_5_3 | 6289.5 | 239 | 6289.5 | 239 | 8297.0 | 230 | 9346.6 | 259 |
15_6_10_6 | 6946.2 | 233 | 6946.2 | 233 | 15040.7 | 131 | 14640 | 158 |
15_9_12_9 | 9841.5 | 155 | 9841.5 | 155 | 18049.6 | 90 | 17478.6 | 129 |
5 Case studies
5.1 Didactic small instances: results of Greedy
5.2 Schedule optimization (cost vs duration) by Greedy guided by Genetic Algorithm
\(w_\tau \)
| \(w_\tau \)=0.0 | \(w_\tau \)=0.5 | \(w_\tau \)=1.0 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Instance | Best | Average | Best | Average | Best | Average | ||||||
Cost | Duration | Cost | Duration | Cost | Duration | Cost | Duration | Cost | Duration | Cost | Duration | |
10_3_5_3 | 10845.3 | 149 | 10845.3 | 149 | 12622.2 | 93 | 12622.2 | 93 | 12622.2 | 93 | 12622.2 | 93 |
10_5_8_5 | 9013.6 | 154 | 9013.6 | 154 | 10802.2 | 100 | 10802.2 | 100 | 11148.5 | 100 | 11774.6 | 100 |
10_7_10_7 | 10215.2 | 131 | 10215.2 | 131 | 10731.2 | 104 | 10731.2 | 104 | 13423.1 | 104 | 14279.4 | 104 |
15_3_5_3 | 6289.5 | 263 | 6289.5 | 263 | 8297.0 | 230 | 8297.0 | 230 | 8297.0 | 230 | 9281.6 | 230 |
15_6_10_6 | 6946.2 | 216 | 6946.2 | 216 | 11109.7 | 102 | 11109.7 | 102 | 11989.5 | 102 | 14525.2 | 102 |
15_9_12_9 | 9841.5 | 155 | 9841.5 | 155 | 12292.2 | 90 | 12739.8 | 90 | 16375.7 | 90 | 18170.3 | 90 |