A hybrid genetic tabu search algorithm for metro crew scheduling based on a space-time-state network
The crew scheduling problem is highly important for the operation and management of urban rail transit. It is essential to reasonably design an approach for optimizing the crew schedule within the constraints of a provided train diagram so that the schedule is highly versatile and can meet the actua...
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| Veröffentlicht in: | Applied soft computing Jg. 182; S. 113574 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.10.2025
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| ISSN: | 1568-4946 |
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| Abstract | The crew scheduling problem is highly important for the operation and management of urban rail transit. It is essential to reasonably design an approach for optimizing the crew schedule within the constraints of a provided train diagram so that the schedule is highly versatile and can meet the actual operational demand. Additionally, better results can be achieved by using an optimization method, which can reduce operating costs and satisfy crew members’ working preferences to the greatest extent possible to achieve a more rational distribution of tasks. Unlike traditional space-time networks that merely describe spatiotemporal movement trajectories, this study innovatively introduces state attributes to ensure solution feasibility during search. Using these attributes, we establish a space-time-state network for crew scheduling modeling. This model has the objective of reducing task connection time and personnel costs. To solve the provided model, a hybrid genetic tabu search (HGTS) algorithm is created by considering the distinctive characteristics of two methods: tabu search (TS) and genetic algorithm (GA), where TS handles local search and GA performs global optimization. The HGTS algorithm can efficiently address the complex metro crew scheduling problem and obtain an improved crew scheduling plan. The proposed method is validated against data from Chengdu Metro Line 5. Results demonstrate that our constructed methodology can effectively reduce the personnel costs and connection time of crew scheduling over the manual scheduling plan: a total of 148 crew duties were obtained, with an optimization rate of 10.30 % and a total connection time of 198 h 44 min 49 s, with an optimization rate of 7.71 %. Furthermore, the proposed method has a higher computational speed and enhanced stability than the shortest-path faster algorithm based on the greedy approach (G-SPFA) method, especially for large-scale data. Additionally, as a hybrid algorithm, HGTS delivers superior solutions compared to standalone GA and TS. This advantage is evidenced by key metrics: HGTS achieved a total duty duration of 725 h 31 min 51 s versus GA's 778 h 38 min 10 s and TS's 749 h 11 min 31 s, while also demonstrating tighter crew efficiency with standard deviations of 0.067, 0.077, and 0.085 for HGTS, GA, and TS respectively.
•HGTS customized for urban rail crew scheduling with strict cumulative constraints.•Novel space-time-state network framework ensures global feasibility of crew duties, overcoming traditional model limits.•Benchmarking vs GA, TS, G-SPFA shows HGTS superiority in crew efficiency, schedule stability, & computational speed.•Method shows good optimization performance and fast computational speed under large-scale data. |
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| AbstractList | The crew scheduling problem is highly important for the operation and management of urban rail transit. It is essential to reasonably design an approach for optimizing the crew schedule within the constraints of a provided train diagram so that the schedule is highly versatile and can meet the actual operational demand. Additionally, better results can be achieved by using an optimization method, which can reduce operating costs and satisfy crew members’ working preferences to the greatest extent possible to achieve a more rational distribution of tasks. Unlike traditional space-time networks that merely describe spatiotemporal movement trajectories, this study innovatively introduces state attributes to ensure solution feasibility during search. Using these attributes, we establish a space-time-state network for crew scheduling modeling. This model has the objective of reducing task connection time and personnel costs. To solve the provided model, a hybrid genetic tabu search (HGTS) algorithm is created by considering the distinctive characteristics of two methods: tabu search (TS) and genetic algorithm (GA), where TS handles local search and GA performs global optimization. The HGTS algorithm can efficiently address the complex metro crew scheduling problem and obtain an improved crew scheduling plan. The proposed method is validated against data from Chengdu Metro Line 5. Results demonstrate that our constructed methodology can effectively reduce the personnel costs and connection time of crew scheduling over the manual scheduling plan: a total of 148 crew duties were obtained, with an optimization rate of 10.30 % and a total connection time of 198 h 44 min 49 s, with an optimization rate of 7.71 %. Furthermore, the proposed method has a higher computational speed and enhanced stability than the shortest-path faster algorithm based on the greedy approach (G-SPFA) method, especially for large-scale data. Additionally, as a hybrid algorithm, HGTS delivers superior solutions compared to standalone GA and TS. This advantage is evidenced by key metrics: HGTS achieved a total duty duration of 725 h 31 min 51 s versus GA's 778 h 38 min 10 s and TS's 749 h 11 min 31 s, while also demonstrating tighter crew efficiency with standard deviations of 0.067, 0.077, and 0.085 for HGTS, GA, and TS respectively.
•HGTS customized for urban rail crew scheduling with strict cumulative constraints.•Novel space-time-state network framework ensures global feasibility of crew duties, overcoming traditional model limits.•Benchmarking vs GA, TS, G-SPFA shows HGTS superiority in crew efficiency, schedule stability, & computational speed.•Method shows good optimization performance and fast computational speed under large-scale data. |
| ArticleNumber | 113574 |
| Author | Wang, Jincheng Xue, Feng Liang, Peng Yang, Ying |
| Author_xml | – sequence: 1 givenname: Feng orcidid: 0000-0001-8556-8735 surname: Xue fullname: Xue, Feng email: xuefeng.7@swjtu.edu.cn organization: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China – sequence: 2 givenname: Peng orcidid: 0009-0002-8915-6580 surname: Liang fullname: Liang, Peng organization: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China – sequence: 3 givenname: Ying orcidid: 0009-0002-5558-7390 surname: Yang fullname: Yang, Ying organization: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China – sequence: 4 givenname: Jincheng surname: Wang fullname: Wang, Jincheng organization: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 611756, China |
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| Cites_doi | 10.1287/trsc.17.1.4 10.1287/opre.1050.0222 10.1007/s10479-006-0017-8 10.1287/inte.20.1.26 10.1016/j.cie.2023.109354 10.1016/j.tre.2020.102132 10.12928/ijio.v1i1.1421 10.1007/s10951-010-0212-y 10.1016/j.trb.2015.08.002 10.1016/j.trb.2024.102941 10.1016/j.tust.2023.105226 10.1007/BF02614314 10.1287/trsc.1040.0091 10.1023/B:ANOR.0000019090.39650.32 10.70470/SHIFRA/2025/004 10.1016/j.disopt.2008.06.001 10.1007/s10951-016-0499-4 10.1016/j.ijpe.2007.11.011 10.70470/KHWARIZMIA/2023/002 10.1016/j.ijpe.2016.01.016 10.1007/s12599-017-0470-8 10.1007/s10479-014-1619-1 10.1016/j.trc.2023.104081 10.1016/S0305-0548(98)00019-7 10.1109/ACCESS.2019.2900028 10.1016/j.ejor.2019.06.016 10.1016/j.cie.2014.01.002 10.1016/j.ejor.2020.12.058 10.1016/j.jclepro.2020.120590 10.1287/trsc.1100.0322 10.1016/j.ejor.2005.10.008 10.1016/j.trc.2022.103832 10.3390/app8122621 10.1016/j.trc.2017.07.008 10.1016/j.trb.2013.08.003 10.1016/j.ejor.2007.10.065 10.3390/math12121881 10.70470/SHIFRA/2023/003 10.1016/j.ejor.2013.02.055 10.1007/s10732-009-9102-x 10.1287/trsc.1030.0078 10.1016/j.ejor.2020.05.005 |
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| Keywords | Urban rail transit Hybrid genetic tabu search algorithm Crew scheduling Space-time-state network |
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| References | Abdulrahman, Niu (bib42) 2023; 2023 Marta, Luís, Isabel (bib30) 2006; 144 Alhamad, Alkhezi (bib47) 2024; 12 Wang, Yuan, Guan, Xu, WANG, Wang, Liu (bib38) 2020; 258 Janis, Martin, Felix, Kirsten, Udo (bib24) 2021; 293 Caprara, Fischetti, Toth, Vigo, Guide (bib2) 1997; 79 Blais, Lamont, Rousseau (bib6) 1990; 20 Vojtech, Dusan, Jiri, Michal (bib12) 2020 Akbar, Aurachmana (bib48) 2020; 1 Caprara, Kroon, Monaci, Peeters, Toth (bib3) 2007; 4 Lin, Tsai (bib23) 2019; 7 Yunes, Moura, Souza (bib28) 2005; 39 Christian, Ulrich (bib19) 2020; 2 Han, Li (bib32) 2014; 223 Boschetti, Mingozzi, Ricciardelli (bib17) 2008 Silke, Daniel, Ulrich (bib20) 2017; 20 Zaepfel, Boegl (bib29) 2008; 113 Salloum, Norozpour (bib43) 2025; 2025 Wang, Chen, Qin, Yang (bib1) 2023; 139 Zhao, Yang, Cao, Sun, Wu (bib55) 2025 Potthoff, Huisman, Desaulniers (bib18) 2010; 44 Kirsten, Udo, Janis, Felix (bib22) 2017; 59 Xue, Liang, Li, Chen, Zhou (bib41) 2022; 9 Zhong, Yang, Zhou (bib50) 2020; 39 Lusby, Larsen, Ehrgott, Ryan (bib4) 2011; 33 Freling, Lentink, Wagelmans (bib5) 2004; 127 Shen, Peng, Chen, Li (bib31) 2013; 56 Panta, Dusan (bib16) 1999; 33 Wen, Ma, Chung, Khan (bib14) 2020; 144 Xu, Yin, Yang, Zheng, Chang, Wu (bib54) 2025 Huang, Wu, Liang (bib51) 2010 Shibghatullah (bib44) 2023; 2023 Janacek, Kohani, Koniorczyk, Marton (bib26) 2017; 83 Christian, Felix, Ulrich (bib21) 2021; 55 Bouni, Roslee, Mitani, Abujawa, Oaman, Ali (bib46) 2025; 2 Hanafi, Kozan (bib15) 2014; 70 Li, Gao (bib49) 2016; 174 Kwan (bib8) 2011; 14 Tong, Zhou, Miller (bib37) 2015; 81 Zhou, Xu, Long, Ding (bib33) 2022; 143 Ball, Bodin, Dial (bib10) 1983; 7 Heil, Hoffmann, Buscher (bib9) 2020; 283 Souai, Teghem (bib40) 2009; 199 Feng, Lusby, Zhang, Tao, Zhang, Peng (bib36) 2024; 183 Xue, Zhang, Hu, Ma, Chen (bib34) 2023; 182 Lučić, Teodorovi (bib39) 1999; 33 Feng, Lusby, Zhang, Peng, Shang, Tao (bib35) 2023; 149 Mesquita, Moz, Paias, Pato (bib52) 2013; 229 Beasley, Cao (bib7) 1998; 25 Andrew, Ellis, Anton, George (bib11) 2005; 39 Elizondo, Parada, Pradenas, Artigues (bib27) 2010; 16 Vahid, Mohammed, François (bib13) 2020; 287 Elhallaoui, Villeneuve, Soumis (bib53) 2005; 53 Sydney (bib25) 2007; 177 Wei, Li, Jiang, Hu (bib45) 2018; 8 Zhou (10.1016/j.asoc.2025.113574_bib33) 2022; 143 Bouni (10.1016/j.asoc.2025.113574_bib46) 2025; 2 Lučić (10.1016/j.asoc.2025.113574_bib39) 1999; 33 Xue (10.1016/j.asoc.2025.113574_bib34) 2023; 182 Janacek (10.1016/j.asoc.2025.113574_bib26) 2017; 83 Souai (10.1016/j.asoc.2025.113574_bib40) 2009; 199 Yunes (10.1016/j.asoc.2025.113574_bib28) 2005; 39 Caprara (10.1016/j.asoc.2025.113574_bib2) 1997; 79 Huang (10.1016/j.asoc.2025.113574_bib51) 2010 Ball (10.1016/j.asoc.2025.113574_bib10) 1983; 7 Li (10.1016/j.asoc.2025.113574_bib49) 2016; 174 Zaepfel (10.1016/j.asoc.2025.113574_bib29) 2008; 113 Shibghatullah (10.1016/j.asoc.2025.113574_bib44) 2023; 2023 Elizondo (10.1016/j.asoc.2025.113574_bib27) 2010; 16 Elhallaoui (10.1016/j.asoc.2025.113574_bib53) 2005; 53 Blais (10.1016/j.asoc.2025.113574_bib6) 1990; 20 Panta (10.1016/j.asoc.2025.113574_bib16) 1999; 33 Lin (10.1016/j.asoc.2025.113574_bib23) 2019; 7 Abdulrahman (10.1016/j.asoc.2025.113574_bib42) 2023; 2023 Zhong (10.1016/j.asoc.2025.113574_bib50) 2020; 39 Tong (10.1016/j.asoc.2025.113574_bib37) 2015; 81 Christian (10.1016/j.asoc.2025.113574_bib21) 2021; 55 Vojtech (10.1016/j.asoc.2025.113574_bib12) 2020 Marta (10.1016/j.asoc.2025.113574_bib30) 2006; 144 Akbar (10.1016/j.asoc.2025.113574_bib48) 2020; 1 Freling (10.1016/j.asoc.2025.113574_bib5) 2004; 127 Kwan (10.1016/j.asoc.2025.113574_bib8) 2011; 14 Mesquita (10.1016/j.asoc.2025.113574_bib52) 2013; 229 Silke (10.1016/j.asoc.2025.113574_bib20) 2017; 20 Zhao (10.1016/j.asoc.2025.113574_bib55) 2025 Xu (10.1016/j.asoc.2025.113574_bib54) 2025 Xue (10.1016/j.asoc.2025.113574_bib41) 2022; 9 Feng (10.1016/j.asoc.2025.113574_bib36) 2024; 183 Caprara (10.1016/j.asoc.2025.113574_bib3) 2007; 4 Alhamad (10.1016/j.asoc.2025.113574_bib47) 2024; 12 Beasley (10.1016/j.asoc.2025.113574_bib7) 1998; 25 Shen (10.1016/j.asoc.2025.113574_bib31) 2013; 56 Hanafi (10.1016/j.asoc.2025.113574_bib15) 2014; 70 Potthoff (10.1016/j.asoc.2025.113574_bib18) 2010; 44 Wen (10.1016/j.asoc.2025.113574_bib14) 2020; 144 Feng (10.1016/j.asoc.2025.113574_bib35) 2023; 149 Heil (10.1016/j.asoc.2025.113574_bib9) 2020; 283 Han (10.1016/j.asoc.2025.113574_bib32) 2014; 223 Wang (10.1016/j.asoc.2025.113574_bib1) 2023; 139 Wei (10.1016/j.asoc.2025.113574_bib45) 2018; 8 Kirsten (10.1016/j.asoc.2025.113574_bib22) 2017; 59 Vahid (10.1016/j.asoc.2025.113574_bib13) 2020; 287 Salloum (10.1016/j.asoc.2025.113574_bib43) 2025; 2025 Wang (10.1016/j.asoc.2025.113574_bib38) 2020; 258 Boschetti (10.1016/j.asoc.2025.113574_bib17) 2008 Lusby (10.1016/j.asoc.2025.113574_bib4) 2011; 33 Sydney (10.1016/j.asoc.2025.113574_bib25) 2007; 177 Andrew (10.1016/j.asoc.2025.113574_bib11) 2005; 39 Janis (10.1016/j.asoc.2025.113574_bib24) 2021; 293 Christian (10.1016/j.asoc.2025.113574_bib19) 2020; 2 |
| References_xml | – volume: 16 start-page: 575 year: 2010 end-page: 591 ident: bib27 article-title: An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport publication-title: J. Heuristics – volume: 44 start-page: 493 year: 2010 end-page: 505 ident: bib18 article-title: Column generation with dynamic duty selection for railway crew rescheduling publication-title: Transp. Sci. – volume: 9 start-page: 2532 year: 2022 end-page: 2540 ident: bib41 article-title: Optimization of subway crew scheduling based on shortest path faster algorithm publication-title: J. Railw. Sci. Eng. – volume: 81 start-page: 555 year: 2015 end-page: 576 ident: bib37 article-title: Transportation network design for maximizing space–time accessibility publication-title: Transp. Res. Part B Methodol. – volume: 33 start-page: 19 year: 1999 end-page: 45 ident: bib39 article-title: Simulated annealing for the multi-objective aircrew rostering problem publication-title: Transp. Res. Part A – volume: 55 start-page: 1227 year: 2021 end-page: 1458 ident: bib21 article-title: Robust tactical crew scheduling under uncertain demand publication-title: Transp. Sci. – volume: 83 start-page: 165 year: 2017 end-page: 178 ident: bib26 article-title: Optimization of periodic crew schedules with application of column generation method publication-title: Transp. Res. Part C. – volume: 223 start-page: 173 year: 2014 end-page: 193 ident: bib32 article-title: A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system publication-title: Ann. Oper. Res. – volume: 14 start-page: 423 year: 2011 end-page: 434 ident: bib8 article-title: Case studies of successful train crew scheduling optimisation publication-title: J. Sched. – volume: 139 year: 2023 ident: bib1 article-title: Timetable escheduling of metro network during the last train period publication-title: Tunn. Undergr. Space Technol. – volume: 8 start-page: 2621 year: 2018 ident: bib45 article-title: Hybrid genetic simulated annealing algorithm for improved flow shop scheduling with makespan criterion publication-title: Appl. Sci. – volume: 283 start-page: 405 year: 2020 end-page: 425 ident: bib9 article-title: Railway crew scheduling: models, methods and applications publication-title: Eur. J. Oper. Res. – volume: 287 start-page: 211 year: 2020 end-page: 224 ident: bib13 article-title: Alternating lagrangian decomposition for integrated airline crew scheduling problem publication-title: Eur. J. Oper. Res. – volume: 2023 start-page: 10 year: 2023 end-page: 23 ident: bib42 article-title: Multi-objective evolutionary algorithm with decomposition for enhanced community detection in signed networks publication-title: Khwarizmia – volume: 2 start-page: 835 year: 2020 end-page: 862 ident: bib19 article-title: Railway crew scheduling with semi flexible timetables publication-title: Spectrum – volume: 258 year: 2020 ident: bib38 article-title: Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation publication-title: J. Clean. Prod. – volume: 20 start-page: 26 year: 1990 end-page: 42 ident: bib6 article-title: The HASTUS vehicle and manpower scheduling system at the Société de transport de la Communauté urbaine de Montréal publication-title: Interfaces – volume: 127 start-page: 203 year: 2004 end-page: 222 ident: bib5 article-title: A decision support system for crew planning in passenger transportation using a flexible branch-and-price algorithm publication-title: Ann. Oper. Res. – volume: 33 start-page: 19 year: 1999 end-page: 45 ident: bib16 article-title: Simulated annealing for the multi-objective aircrew rostering problem publication-title: Transp. Res. Part A – volume: 7 start-page: 27362 year: 2019 end-page: 27375 ident: bib23 article-title: Integrated crew scheduling and roster problem for trainmasters of passenger railway transportation publication-title: IEEE Access – volume: 143 year: 2022 ident: bib33 article-title: Metro crew planning with day-off pattern, duty type, and rostering scheme considerations publication-title: Transp. Res. Part C. – volume: 25 start-page: 567 year: 1998 end-page: 582 ident: bib7 article-title: A dynamic programming based algorithm for the crew scheduling problem publication-title: Comput. Oper. Res. – start-page: 735 year: 2008 end-page: 747 ident: bib17 article-title: A dual ascent procedure for the set partitioning problem publication-title: Discret. Optim. – volume: 20 start-page: 43 year: 2017 end-page: 55 ident: bib20 article-title: Optimizing railway crew schedules with fairness preferences publication-title: J. Sched. – volume: 182 year: 2023 ident: bib34 article-title: Metro crew planning with heterogeneous duty paths and period-cycle pattern considerations publication-title: Comput. Ind. Eng. – volume: 177 start-page: 1764 year: 2007 end-page: 1778 ident: bib25 article-title: Generating, scheduling and rostering of shift crew-duties, applications at the Hong Kong international airport publication-title: Eur. J. Oper. Res. – volume: 113 start-page: 980 year: 2008 end-page: 996 ident: bib29 article-title: Multi- period vehicle routing and crew scheduling with outsourcing options publication-title: Int. J. Prod. Econ. – volume: 183 year: 2024 ident: bib36 article-title: A branch-and-price algorithm for integrating urban rail crew scheduling and rostering problems publication-title: Transp. Res. Part B Methodol. – volume: 199 start-page: 674 year: 2009 end-page: 683 ident: bib40 article-title: Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem publication-title: Eur. J. Oper. Res. – start-page: 205 year: 2025 ident: bib55 article-title: A synchronous crew scheduling problem with time fairness based on a two-phrase assignment strategy in an urban rail transit network publication-title: Comput. Ind. Eng. – start-page: 5372567 year: 2020 ident: bib12 article-title: Dynamic model for scheduling crew shifts publication-title: Math. Probl. Eng. – start-page: 201 year: 2025 ident: bib54 article-title: Cross-line crew scheduling optimization in urban rail transit systems publication-title: Comput. Ind. Eng. – volume: 4 start-page: 129 year: 2007 end-page: 187 ident: bib3 article-title: Chapter 3 passenger railway optimization publication-title: Handb. Oper. Res. Manag. Sci. – volume: 174 start-page: 93 year: 2016 end-page: 110 ident: bib49 article-title: An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem publication-title: Int. J. Prod. Econ. – volume: 39 start-page: 1525 year: 2020 end-page: 1538 ident: bib50 article-title: Application of hybrid GA-PSO based on intelligent control fuzzy system in the integrated scheduling in automated container terminal publication-title: J. Intell. Fuzzy Syst. – volume: 149 year: 2023 ident: bib35 article-title: An ADMM-based dual decomposition mechanism for integrating crew scheduling and rostering in an urban rail transit line publication-title: Transp. Res. Part C Emerg. Technol. – volume: 293 start-page: 1113 year: 2021 end-page: 1130 ident: bib24 article-title: An efficient column generation approach for practical railway crew scheduling with attendance rates publication-title: Eur. J. Oper. Res. – volume: 39 start-page: 273 year: 2005 end-page: 288 ident: bib28 article-title: Hybrid column generation approaches for urban transit crew management problems publication-title: Transp. Sci. – volume: 2 start-page: 2502617 year: 2025 ident: bib46 article-title: A hybrid GA-SA resource allocation scheme enhanced with SINR optimization for NOMA-MIMO systems in 5G networks publication-title: Cogent Eng. – volume: 1 start-page: 15 year: 2020 end-page: 28 ident: bib48 article-title: Hybrid genetic–tabu search algorithm to optimize the route for capacitated vehicle routing problem with time window publication-title: Int. J. Ind. Optim. – volume: 56 start-page: 174 year: 2013 end-page: 185 ident: bib31 article-title: Evolutionary crew scheduling with adaptive chromosomes publication-title: Transp. Res. Part B Methodol. – volume: 33 start-page: 843 year: 2011 end-page: 883 ident: bib4 article-title: Railway track allocation: models and methods publication-title: spectrum – volume: 2025 start-page: 69 year: 2025 end-page: 80 ident: bib43 article-title: XAI-IDS: A transparent and interpretable framework for robust cybersecurity using explainable artificial intelligence publication-title: Shifra – volume: 53 start-page: 632 year: 2005 end-page: 645 ident: bib53 article-title: Dynamic aggregation of setpartitioning constraints in column generation publication-title: Oper. Res. – volume: 79 start-page: 125 year: 1997 end-page: 141 ident: bib2 article-title: Algorithms for railway crew management publication-title: Math. Program. – volume: 144 start-page: 111 year: 2006 end-page: 132 ident: bib30 article-title: The crew timetabling problem: an extension of the crew scheduling problem publication-title: Ann. Oper. Res. – volume: 12 start-page: 1881 year: 2024 ident: bib47 article-title: Hybrid genetic algorithm and tabu search for solving preventive maintenance scheduling for cogeneration plants publication-title: Mathematics – volume: 2023 start-page: 17 year: 2023 end-page: 25 ident: bib44 article-title: Mitigating developed persistent threats (APTs) through machine learning-based intrusion detection systems: a comprehensive analysis publication-title: Shifra – start-page: 226 year: 2010 end-page: 233 ident: bib51 article-title: GA-ACO in job-shop schedule problem research publication-title: Computational Intelligence and Intelligent Systems – volume: 229 start-page: 318 year: 2013 end-page: 331 ident: bib52 article-title: A decomposition approach for the integrated vehicle-crew-roster problem with days-off pattern publication-title: Eur. J. Oper. Res. – volume: 144 year: 2020 ident: bib14 article-title: Robust airline crew scheduling with flight flying time variability publication-title: Transp. Res. Part E – volume: 59 start-page: 147 year: 2017 end-page: 159 ident: bib22 article-title: Solving practical railway crew scheduling problems with attendance rates publication-title: Bus. Inf. Syst. Eng. – volume: 7 start-page: 4 year: 1983 end-page: 31 ident: bib10 article-title: A matching based heuristic for scheduling mass transit crews and vehicles publication-title: Transp. Sci. – volume: 39 start-page: 340 year: 2005 end-page: 348 ident: bib11 article-title: Airline crew scheduling under uncertainty publication-title: Transp. Sci. – volume: 70 start-page: 11 year: 2014 end-page: 19 ident: bib15 article-title: A hybrid constructive heuristic and simulated annealing for railway crew scheduling publication-title: Comput. Ind. Eng. – volume: 9 start-page: 2532 year: 2022 ident: 10.1016/j.asoc.2025.113574_bib41 article-title: Optimization of subway crew scheduling based on shortest path faster algorithm publication-title: J. Railw. Sci. Eng. – start-page: 201 year: 2025 ident: 10.1016/j.asoc.2025.113574_bib54 article-title: Cross-line crew scheduling optimization in urban rail transit systems publication-title: Comput. Ind. Eng. – volume: 7 start-page: 4 issue: 1 year: 1983 ident: 10.1016/j.asoc.2025.113574_bib10 article-title: A matching based heuristic for scheduling mass transit crews and vehicles publication-title: Transp. Sci. doi: 10.1287/trsc.17.1.4 – start-page: 226 year: 2010 ident: 10.1016/j.asoc.2025.113574_bib51 article-title: GA-ACO in job-shop schedule problem research – volume: 53 start-page: 632 issue: 4 year: 2005 ident: 10.1016/j.asoc.2025.113574_bib53 article-title: Dynamic aggregation of setpartitioning constraints in column generation publication-title: Oper. Res. doi: 10.1287/opre.1050.0222 – volume: 144 start-page: 111 year: 2006 ident: 10.1016/j.asoc.2025.113574_bib30 article-title: The crew timetabling problem: an extension of the crew scheduling problem publication-title: Ann. Oper. Res. doi: 10.1007/s10479-006-0017-8 – volume: 20 start-page: 26 issue: 1 year: 1990 ident: 10.1016/j.asoc.2025.113574_bib6 article-title: The HASTUS vehicle and manpower scheduling system at the Société de transport de la Communauté urbaine de Montréal publication-title: Interfaces doi: 10.1287/inte.20.1.26 – volume: 182 year: 2023 ident: 10.1016/j.asoc.2025.113574_bib34 article-title: Metro crew planning with heterogeneous duty paths and period-cycle pattern considerations publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2023.109354 – volume: 144 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib14 article-title: Robust airline crew scheduling with flight flying time variability publication-title: Transp. Res. Part E doi: 10.1016/j.tre.2020.102132 – volume: 1 start-page: 15 issue: 1 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib48 article-title: Hybrid genetic–tabu search algorithm to optimize the route for capacitated vehicle routing problem with time window publication-title: Int. J. Ind. Optim. doi: 10.12928/ijio.v1i1.1421 – volume: 14 start-page: 423 issue: 5 year: 2011 ident: 10.1016/j.asoc.2025.113574_bib8 article-title: Case studies of successful train crew scheduling optimisation publication-title: J. Sched. doi: 10.1007/s10951-010-0212-y – volume: 81 start-page: 555 issue: 2 year: 2015 ident: 10.1016/j.asoc.2025.113574_bib37 article-title: Transportation network design for maximizing space–time accessibility publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2015.08.002 – volume: 183 year: 2024 ident: 10.1016/j.asoc.2025.113574_bib36 article-title: A branch-and-price algorithm for integrating urban rail crew scheduling and rostering problems publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2024.102941 – volume: 139 year: 2023 ident: 10.1016/j.asoc.2025.113574_bib1 article-title: Timetable escheduling of metro network during the last train period publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2023.105226 – volume: 79 start-page: 125 issue: 1–3 year: 1997 ident: 10.1016/j.asoc.2025.113574_bib2 article-title: Algorithms for railway crew management publication-title: Math. Program. doi: 10.1007/BF02614314 – volume: 39 start-page: 340 issue: 03 year: 2005 ident: 10.1016/j.asoc.2025.113574_bib11 article-title: Airline crew scheduling under uncertainty publication-title: Transp. Sci. doi: 10.1287/trsc.1040.0091 – volume: 127 start-page: 203 issue: 1-4 year: 2004 ident: 10.1016/j.asoc.2025.113574_bib5 article-title: A decision support system for crew planning in passenger transportation using a flexible branch-and-price algorithm publication-title: Ann. Oper. Res. doi: 10.1023/B:ANOR.0000019090.39650.32 – volume: 2025 start-page: 69 year: 2025 ident: 10.1016/j.asoc.2025.113574_bib43 article-title: XAI-IDS: A transparent and interpretable framework for robust cybersecurity using explainable artificial intelligence publication-title: Shifra doi: 10.70470/SHIFRA/2025/004 – start-page: 735 issue: 4 year: 2008 ident: 10.1016/j.asoc.2025.113574_bib17 article-title: A dual ascent procedure for the set partitioning problem publication-title: Discret. Optim. doi: 10.1016/j.disopt.2008.06.001 – volume: 4 start-page: 129 year: 2007 ident: 10.1016/j.asoc.2025.113574_bib3 article-title: Chapter 3 passenger railway optimization publication-title: Handb. Oper. Res. Manag. Sci. – start-page: 205 year: 2025 ident: 10.1016/j.asoc.2025.113574_bib55 article-title: A synchronous crew scheduling problem with time fairness based on a two-phrase assignment strategy in an urban rail transit network publication-title: Comput. Ind. Eng. – volume: 20 start-page: 43 year: 2017 ident: 10.1016/j.asoc.2025.113574_bib20 article-title: Optimizing railway crew schedules with fairness preferences publication-title: J. Sched. doi: 10.1007/s10951-016-0499-4 – volume: 33 start-page: 19 year: 1999 ident: 10.1016/j.asoc.2025.113574_bib39 article-title: Simulated annealing for the multi-objective aircrew rostering problem publication-title: Transp. Res. Part A – start-page: 5372567 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib12 article-title: Dynamic model for scheduling crew shifts publication-title: Math. Probl. Eng. – volume: 2 start-page: 2502617 year: 2025 ident: 10.1016/j.asoc.2025.113574_bib46 article-title: A hybrid GA-SA resource allocation scheme enhanced with SINR optimization for NOMA-MIMO systems in 5G networks publication-title: Cogent Eng. – volume: 113 start-page: 980 issue: 02 year: 2008 ident: 10.1016/j.asoc.2025.113574_bib29 article-title: Multi- period vehicle routing and crew scheduling with outsourcing options publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2007.11.011 – volume: 2023 start-page: 10 year: 2023 ident: 10.1016/j.asoc.2025.113574_bib42 article-title: Multi-objective evolutionary algorithm with decomposition for enhanced community detection in signed networks publication-title: Khwarizmia doi: 10.70470/KHWARIZMIA/2023/002 – volume: 174 start-page: 93 year: 2016 ident: 10.1016/j.asoc.2025.113574_bib49 article-title: An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2016.01.016 – volume: 33 start-page: 19 year: 1999 ident: 10.1016/j.asoc.2025.113574_bib16 article-title: Simulated annealing for the multi-objective aircrew rostering problem publication-title: Transp. Res. Part A – volume: 59 start-page: 147 issue: 03 year: 2017 ident: 10.1016/j.asoc.2025.113574_bib22 article-title: Solving practical railway crew scheduling problems with attendance rates publication-title: Bus. Inf. Syst. Eng. doi: 10.1007/s12599-017-0470-8 – volume: 223 start-page: 173 issue: 1 year: 2014 ident: 10.1016/j.asoc.2025.113574_bib32 article-title: A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system publication-title: Ann. Oper. Res. doi: 10.1007/s10479-014-1619-1 – volume: 149 year: 2023 ident: 10.1016/j.asoc.2025.113574_bib35 article-title: An ADMM-based dual decomposition mechanism for integrating crew scheduling and rostering in an urban rail transit line publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2023.104081 – volume: 25 start-page: 567 issue: 7-8 year: 1998 ident: 10.1016/j.asoc.2025.113574_bib7 article-title: A dynamic programming based algorithm for the crew scheduling problem publication-title: Comput. Oper. Res. doi: 10.1016/S0305-0548(98)00019-7 – volume: 2 start-page: 835 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib19 article-title: Railway crew scheduling with semi flexible timetables publication-title: Spectrum – volume: 7 start-page: 27362 year: 2019 ident: 10.1016/j.asoc.2025.113574_bib23 article-title: Integrated crew scheduling and roster problem for trainmasters of passenger railway transportation publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2900028 – volume: 39 start-page: 1525 issue: 2 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib50 article-title: Application of hybrid GA-PSO based on intelligent control fuzzy system in the integrated scheduling in automated container terminal publication-title: J. Intell. Fuzzy Syst. – volume: 283 start-page: 405 issue: 2 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib9 article-title: Railway crew scheduling: models, methods and applications publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2019.06.016 – volume: 70 start-page: 11 year: 2014 ident: 10.1016/j.asoc.2025.113574_bib15 article-title: A hybrid constructive heuristic and simulated annealing for railway crew scheduling publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2014.01.002 – volume: 55 start-page: 1227 issue: 6 year: 2021 ident: 10.1016/j.asoc.2025.113574_bib21 article-title: Robust tactical crew scheduling under uncertain demand publication-title: Transp. Sci. – volume: 293 start-page: 1113 issue: 03 year: 2021 ident: 10.1016/j.asoc.2025.113574_bib24 article-title: An efficient column generation approach for practical railway crew scheduling with attendance rates publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2020.12.058 – volume: 258 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib38 article-title: Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.120590 – volume: 44 start-page: 493 issue: 4 year: 2010 ident: 10.1016/j.asoc.2025.113574_bib18 article-title: Column generation with dynamic duty selection for railway crew rescheduling publication-title: Transp. Sci. doi: 10.1287/trsc.1100.0322 – volume: 177 start-page: 1764 issue: 3 year: 2007 ident: 10.1016/j.asoc.2025.113574_bib25 article-title: Generating, scheduling and rostering of shift crew-duties, applications at the Hong Kong international airport publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2005.10.008 – volume: 143 year: 2022 ident: 10.1016/j.asoc.2025.113574_bib33 article-title: Metro crew planning with day-off pattern, duty type, and rostering scheme considerations publication-title: Transp. Res. Part C. doi: 10.1016/j.trc.2022.103832 – volume: 8 start-page: 2621 issue: 12 year: 2018 ident: 10.1016/j.asoc.2025.113574_bib45 article-title: Hybrid genetic simulated annealing algorithm for improved flow shop scheduling with makespan criterion publication-title: Appl. Sci. doi: 10.3390/app8122621 – volume: 83 start-page: 165 year: 2017 ident: 10.1016/j.asoc.2025.113574_bib26 article-title: Optimization of periodic crew schedules with application of column generation method publication-title: Transp. Res. Part C. doi: 10.1016/j.trc.2017.07.008 – volume: 56 start-page: 174 year: 2013 ident: 10.1016/j.asoc.2025.113574_bib31 article-title: Evolutionary crew scheduling with adaptive chromosomes publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2013.08.003 – volume: 199 start-page: 674 issue: 3 year: 2009 ident: 10.1016/j.asoc.2025.113574_bib40 article-title: Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2007.10.065 – volume: 12 start-page: 1881 issue: 12 year: 2024 ident: 10.1016/j.asoc.2025.113574_bib47 article-title: Hybrid genetic algorithm and tabu search for solving preventive maintenance scheduling for cogeneration plants publication-title: Mathematics doi: 10.3390/math12121881 – volume: 33 start-page: 843 year: 2011 ident: 10.1016/j.asoc.2025.113574_bib4 article-title: Railway track allocation: models and methods publication-title: spectrum – volume: 2023 start-page: 17 year: 2023 ident: 10.1016/j.asoc.2025.113574_bib44 article-title: Mitigating developed persistent threats (APTs) through machine learning-based intrusion detection systems: a comprehensive analysis publication-title: Shifra doi: 10.70470/SHIFRA/2023/003 – volume: 229 start-page: 318 issue: 2 year: 2013 ident: 10.1016/j.asoc.2025.113574_bib52 article-title: A decomposition approach for the integrated vehicle-crew-roster problem with days-off pattern publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2013.02.055 – volume: 16 start-page: 575 issue: 4 year: 2010 ident: 10.1016/j.asoc.2025.113574_bib27 article-title: An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport publication-title: J. Heuristics doi: 10.1007/s10732-009-9102-x – volume: 39 start-page: 273 issue: 2 year: 2005 ident: 10.1016/j.asoc.2025.113574_bib28 article-title: Hybrid column generation approaches for urban transit crew management problems publication-title: Transp. Sci. doi: 10.1287/trsc.1030.0078 – volume: 287 start-page: 211 year: 2020 ident: 10.1016/j.asoc.2025.113574_bib13 article-title: Alternating lagrangian decomposition for integrated airline crew scheduling problem publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2020.05.005 |
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| Title | A hybrid genetic tabu search algorithm for metro crew scheduling based on a space-time-state network |
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