Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt

Scheduling is important when dealing with task distributions and time management. In most organisations, the scheduling process is still generated manually. It consumes a lot of time and energy; consequently, the generated schedule is not really efficient. One of the main issues in scheduling is unf...

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Main Author: Shaffiei, Zatul Alwani
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Published: 2021
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topic Q Science (General)
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Shaffiei, Zatul Alwani
Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
description Scheduling is important when dealing with task distributions and time management. In most organisations, the scheduling process is still generated manually. It consumes a lot of time and energy; consequently, the generated schedule is not really efficient. One of the main issues in scheduling is unfair tasks distribution among drivers. A fair schedule is necessary since it determines the quality of service as well as staff or customer satisfaction. Basically, a fair schedule can be defined as a well-balanced distribution of tasks among machines or staff by satisfying most of their constraints and personal preferences. There are two types of constraint to be considered in scheduling, which are hard constraint and soft constraint. This research was focused on driver scheduling problem for university shuttle bus (DSPUSB). Based on previous research using one of metaheuristic algorithms known as harmony search (HS), the generated schedule was still not optimum and cannot be solved maximally as there were too much repetitions of task (shift and route) occurred among drivers. The existing techniques (HS and its variants) have issues in terms of searching strategy (exploration and exploitation), slow convergence rate and high computation time for solving the scheduling problems maximally or near to optimal one. Therefore, a tasks distribution in driver scheduling using dynamic set of bandwidth in harmony search algorithm with 2-opt (SBHS2-opt) was proposed in this research. In the standard HS, the value of bandwidth (BW) parameter was static, while in this research, a dynamic set of bandwidth (BW2) value was formed based on constraints (problem domain). The BW2 value was dynamically changed and determined based on the current solution (with heuristic concept) of each driver every week, whereas the 2-opt swapping, which is normally used in travelling salesman problem, was applied for route constraint based on specific rules. The SBHS2-opt has guided searching strategy using heuristic concept or known as informed search. Knowledge on the problem is needed to assist the searching process and to strengthen the exploitation. There were 33 experiments carried out with different numbers of driver, route and shift. The results produced by SBHS2-opt outperformed 31 experiments out of 33 experiments. Hence, it was clearly shown that these improvements were capable in strengthen the exploitation, increase convergence rate, low computation time and at the same time balance the tasks distribution among drivers. In addition, the statistical analysis using Wilcoxon Rank-Sum Test and Bonferroni-Holm Correction as well as Box–Whisker plotting demonstrated that the SBHS2-opt has a significant difference in most of the experiments and was more stable in searching the best solution compared to HS, improved HS, parameter adaptive HS and step function HS.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Shaffiei, Zatul Alwani
author_facet Shaffiei, Zatul Alwani
author_sort Shaffiei, Zatul Alwani
title Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
title_short Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
title_full Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
title_fullStr Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
title_full_unstemmed Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt
title_sort tasks distribution in driver scheduling using dynamic set of bandwidth in harmony search algorithm with 2-opt
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Information and Communication Technology
publishDate 2021
url http://eprints.utem.edu.my/id/eprint/25385/1/Tasks%20Distribution%20In%20Driver%20Scheduling%20Using%20Dynamic%20Set%20Of%20Bandwidth%20In%20Harmony%20Search%20Algorithm%20With%202-Opt.pdf
http://eprints.utem.edu.my/id/eprint/25385/2/Tasks%20Distribution%20In%20Driver%20Scheduling%20Using%20Dynamic%20Set%20Of%20Bandwidth%20In%20Harmony%20Search%20Algorithm%20With%202-Opt.pdf
_version_ 1747834114715680768
spelling my-utem-ep.253852021-11-17T08:48:19Z Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt 2021 Shaffiei, Zatul Alwani Q Science (General) QA Mathematics Scheduling is important when dealing with task distributions and time management. In most organisations, the scheduling process is still generated manually. It consumes a lot of time and energy; consequently, the generated schedule is not really efficient. One of the main issues in scheduling is unfair tasks distribution among drivers. A fair schedule is necessary since it determines the quality of service as well as staff or customer satisfaction. Basically, a fair schedule can be defined as a well-balanced distribution of tasks among machines or staff by satisfying most of their constraints and personal preferences. There are two types of constraint to be considered in scheduling, which are hard constraint and soft constraint. This research was focused on driver scheduling problem for university shuttle bus (DSPUSB). Based on previous research using one of metaheuristic algorithms known as harmony search (HS), the generated schedule was still not optimum and cannot be solved maximally as there were too much repetitions of task (shift and route) occurred among drivers. The existing techniques (HS and its variants) have issues in terms of searching strategy (exploration and exploitation), slow convergence rate and high computation time for solving the scheduling problems maximally or near to optimal one. Therefore, a tasks distribution in driver scheduling using dynamic set of bandwidth in harmony search algorithm with 2-opt (SBHS2-opt) was proposed in this research. In the standard HS, the value of bandwidth (BW) parameter was static, while in this research, a dynamic set of bandwidth (BW2) value was formed based on constraints (problem domain). The BW2 value was dynamically changed and determined based on the current solution (with heuristic concept) of each driver every week, whereas the 2-opt swapping, which is normally used in travelling salesman problem, was applied for route constraint based on specific rules. The SBHS2-opt has guided searching strategy using heuristic concept or known as informed search. Knowledge on the problem is needed to assist the searching process and to strengthen the exploitation. There were 33 experiments carried out with different numbers of driver, route and shift. The results produced by SBHS2-opt outperformed 31 experiments out of 33 experiments. Hence, it was clearly shown that these improvements were capable in strengthen the exploitation, increase convergence rate, low computation time and at the same time balance the tasks distribution among drivers. In addition, the statistical analysis using Wilcoxon Rank-Sum Test and Bonferroni-Holm Correction as well as Box–Whisker plotting demonstrated that the SBHS2-opt has a significant difference in most of the experiments and was more stable in searching the best solution compared to HS, improved HS, parameter adaptive HS and step function HS. 2021 Thesis http://eprints.utem.edu.my/id/eprint/25385/ http://eprints.utem.edu.my/id/eprint/25385/1/Tasks%20Distribution%20In%20Driver%20Scheduling%20Using%20Dynamic%20Set%20Of%20Bandwidth%20In%20Harmony%20Search%20Algorithm%20With%202-Opt.pdf text en public http://eprints.utem.edu.my/id/eprint/25385/2/Tasks%20Distribution%20In%20Driver%20Scheduling%20Using%20Dynamic%20Set%20Of%20Bandwidth%20In%20Harmony%20Search%20Algorithm%20With%202-Opt.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119744 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Abal Abas, Zuraida 1. A. Wren, R.S.K. Kwan, and M.E. Parker, 1994. Scheduling of rail driver duties. Transactions on the Built Environment, 7. 2. A.Wren and J-M Rosseau, 1995. Computer-Aided Transit Scheduling. 3. Abang Abdullah, D.N. and Von, H.L., 2011. Factors of Fatigue and Bus Accident. International Conference On Innovation management And Service, 14, pp.317–321. 4. Abas, Z., Shaffiei, Z., Rahman, A.F.N.A., and Samad, A., 2014. Using Harmony Search for Optimising University Shuttle Bus Driver Scheduling for Better Operational Management. International Conference on Innovative Trends in Multidisciplinary Academic Research ” (ITMAR- 2014), 1, pp.614–621. 5. Abedinpourshotorban, H., Hasan, S., Shamsuddin, S.M., and As’Sahra, N.F., 2016. A differential-based harmony search algorithm for the optimization of continuous problems. Expert Systems with Applications, 62, pp.317–332. 6. Abdel-raouf, O., 2013. A Survey of Harmony Search Algorithm. International Journal of Computer Applications, 70 (28), pp.17–26. 7. Abramson, D., Krishnamoorthy, M., and Dang, H., 1999. Simulated annealing cooling schedules for the school timetabling problem. Asia-Pacific Journal of Operational Research, 16 (1), pp.1–22. 8. Abdul-Razaq, T.S., Potts, C.N., and Wassenhove, L.N.V., 1990. A survey of algorithms for the single machine total weighted tardiness scheduling problem. Discrete Applied Mathematics, 26, pp.235–253. 9. Adamuthe, A.C. and Bichkar, R.S., 2012. Tabu search for solving personnel scheduling problem. 2012 International Conference on Communication, Information and Computing Technology (ICCICT), pp.1–6. 10. Afshar-Nadja, B., 2017. A Hybrid of Tabu Search and Simulated Annealing Algorithms for Preemptive Project Scheduling Problem, 10350, pp.102–111. 11. Agoston E. Eiben, J.E.S., 2004. Introduction to Evolutionary Computing. Evolutionary Computation. SpringerVerlag. 12. Ahmad, M., 2015. Mathematical Models and Methods Based on Metaheuristic Approach for Timetabling Problem. 13. Aickelin, U. and Dowsland, K.A., 2004. An Indirect Genetic Algorithm for a Nurse Scheduling Problem. Computers and Operations Research. 14. Al-Betar, M.A. and Khader, A.T., 2012. A harmony search algorithm for university course timetabling. Annals of Operations Research, 194 (1), pp.3–31. 15. Al-Betar, M.A., Khader, A.T., and Nadi, F., 2010. Selection mechanisms in memory consideration for examination timetabling with harmony search. Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO ’10, pp.1203. 16. Al-Betar, M.A., Khader, A.T., and Zaman, M., 2012. University Course Timetabling Using a Hybrid Harmony Search Metaheuristic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42 (5), pp.664–681. 17. Al-Betar, M.A., Khader, A.T., Geem, Z.W., Doush, I.A., and Awadallah, M. a., 2013. An analysis of selection methods in memory consideration for harmony search. Applied Mathematics and Computation, 219 (22), pp.10753–10767. 18. Alamri, H.S. and Zamli, K.Z., 2019. PMT : Opposition-Based Learning Technique for Enhancing Meta-Heuristic Performance. IEEE Access, 7. 19. Alatas, B., 2010a. Chaotic harmony search algorithms. Applied Mathematics and Computation, 216 (9), pp.2687–2699. 20. Al-omoush, A.A., Alsewari, A.A., Alamri, H.S., and Zamli, K.Z., 2019. Comprehensive Review of the Development of the Harmony Search Algorithm and Its Applications. IEEE Access, 7, pp.14233–14245. 21. Alomoush, A.A., Alsewari, A.A., Alamri, H.S., Zamli, K.Z., Alomoush, W., and Younis, M.I., 2020. Modified Opposition Based Learning to Improve Harmony Search Variants Exploration Modified Opposition Based Learning to Improve Harmony Search Variants Exploration. Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. 22. Advances in Intelligent Systems and Computing, 1073 (July), pp.279–287. 23. Alia, O.M., Mandava, R., and Aziz, M.E., 2011. A hybrid harmony search algorithm for MRI brain segmentation. Evolutionary Intelligence, 4 (1), pp.31–49. 24. Altamirano, L., Riff, M.C., and Trilling, L., 2010. A PSO algorithm to solve a real anaesthesiology nurse scheduling problem. Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010, pp.139–144. 25. Alwani, Z., Abas, Z.A., Nizam, A.F., and Rahman, A., 2014. OPTIMIZATION IN DRIVER’s SCHEDULING FOR UNIVERSITY. International Symposium on Research in Innovation and Sustainability 2014 (ISoRIS ’14) 15-16 October 2014, Malacca, Malaysia, 2014 (October), pp.15–16. 26. Andersen, A.C. and Funch, C., 2008. Rostering Optimization for Business Jet Airlines. 27. Anupam Shukla, Tiwari, R., and Kala, R., 2010. Towards Hybrid and Adaptive Computing. 28. Anwar, K., Awadallah, M.A., Khader, A.T., and Al-Betar, M.A., 2014. Hyper-heuristic approach for solving nurse rostering problem. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIEL 2014: 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning, Proceedings, (August 2016). 29. Anwar, K., Khader, A.T., Al-Betar, M.A., and Awadallah, M. A., 2013. Harmony Search-based Hyper-heuristic for examination timetabling. 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, pp.176–181. 30. Arntzen, H. and Løkketangen, A., 2005a. A tabu search heuristic for a university timetabling problem. Operations Research/ Computer Science Interfaces Series, 32, pp.65–85. 31. Arntzen, H. and Løkketangen, A., 2005b. A Tabu Search Heuristic for a University Timetabling Problem. Metaheuristics: Progress as Real Problem Solvers, pp.65–85. 32. Awadallah, M.A., Khader, A.T., Al-Betar, M.A., and Bolaji, A.L.A., 2013. Hybrid harmony search for nurse rostering problems. Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Scheduling, CISched 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, (April), pp.60–67. 33. Ayob, M., Hadwan, M., and Sarim, H.M., 2013. The harmony search algorithms hi solving combinatorial optimization problems. Research Journal of Applied Sciences, 8 (3), pp.191–198. 34. Ayob, M. and Jaradat, G., 2009. Hybrid Ant Colony Systems for course timetabling problems. 2009 2nd Conference on Data Mining and Optimization, DMO 2009, (November), pp.120–126. 35. Ayvaz, M.T., 2009. Identification of Groundwater Parameter Structure Using Harmony Search Algorithm, pp.129–140. 36. Azadeh, A., Farahani, M.H., Eivazy, H., Nazari-Shirkouhi, S., and Asadipour, G., 2013. A hybrid meta-heuristic algorithm for optimization of crew scheduling. Applied Soft Computing Journal. 37. B.Liu, 2009. Theory and Practice of Uncertain Programming. Springer - Verlag Berlin Heidelberg, pp.9–17. 38. Babaei, H., Karimpour, J., and Hadidi, A., 2014. A Survey of Approaches for University Course Timetabling Problem. Computers & Industrial Engineering, 86, pp.43–59. 39. Baker, J.E., 1985. Adaptive Selection Methods for Genetic Algorithms, pp.101–111. 40. Bai, R., Burke, E.K., Kendall, G., Li, J., and McCollum, B., 2010. A hybrid evolutionary approach to the nurse rostering problem. IEEE Transactions on Evolutionary Computation, 14 (4), pp.580–590. 41. Bailey, R.N., Garner, K.M., and Hobbs, M.F., 1997. Using simulated annealing and genetic algorithms to solve staff scheduling problems. Asia-Pacific Journal of Operational Research. 42. Barnes, J.W. and Laguna, M., 1993. A tabu search experience in production scheduling. Annals of Operations Research, 41, pp.141–156. 43. Bauer, A., 1998. Ant Colony Optimization for the Single Machine Total Tardiness Problem. unpublished Master Thesis (in German), Department of Management Science, (January). 44. Belén, M., García, V., Zanón, B.B., and Rodríguez, E.C., 2012. Combining Metaheuristic Algorithms to Solve a Scheduling Problem. 45. Biggs, H.C., Dingsdag, D., and Stenson, N., 2009. Fatigue factors affecting metropolitan bus drivers: A qualitative investigation. Work, 32 (1), pp.5–10. 46. Blum, C. and Roli, A., 2003. Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys, 35 (3), pp.268–308. 47. Bomberger, E.E., 1966. A Dynamic Programming Approach to a Lot Size Scheduling Problem. Management Science, 12 (11), pp.778–784. 48. Borna, K. and Hashemi, V.H., 2014. An Improved Genetic Algorithm with a Local Optimization Strategy and an Extra Mutation Level for Solving Traveling Salesman Problem. International Journal of Computer Science, Engineering and Information Technology, 4 (4), pp.47–53. 49. Borndorfer, R., Reuther, M., Schlechte, T., Schulz, C., Swarat, E., and Weider, S., 2015. Duty Rostering in Public Transport - Facing Preferences , Fairness , and Fatigue, 44 (September). 50. Boussaïd, I., Lepagnot, J., and Siarry, P., 2013. A survey on optimization metaheuristics. Information Sciences, 237, pp.82–117. 51. Bowden, Z.E. and Ragsdale, C.T., 2018. The truck driver scheduling problem with fatigue monitoring. Decision Support Systems, 110 (May 2017), pp.20–31. 52. Bratton, D. and Kennedy, J., 2007. Defining a Standard for Particle Swarm Optimization. 2007 IEEE Swarm Intelligence Symposium, (Sis), pp.120–127. 53. Brown, I.D., 1994. Driver fatigue. Human Factors, 36 (2), pp.298–314. 54. Brucker, P., Qu, R., and Burke, E., 2011. Personnel scheduling: Models and complexity. European Journal of Operational Research, 210 (3), pp.467–473. 55. Burke, E., De Causmaecker, P., and Vanden Berghe, G., 2007. A Hybrid Tabu Search Algorithm for the Nurse Rostering Problem, (0044), pp.187–194. 56. Burke, E.K. and Petrovic, S., 2002. Recent research directions in automated timetabling Edmund. European Journal of Operational Research, pp.266–280. 57. Busetti, F., 1983. Simulated annealing overview, (1), pp.1–10. 58. Černý, V., 1985. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45 (1), pp.41–51. 59. Chang-ming, X. and Lin, Y., 2012. Research on adjustment strategy of PAR in harmony search algorithm. International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), pp.1705–1708. 60. Chang, Y., Li, Z., Kou, Y., Sun, Q., Yang, H., and Zhao, Z., 2017. A New Approach to Weapon-Target Assignment in Cooperative Air Combat. Mathematical Problems in Engineering, 2017. 61. Che, A., Zeng, Y., and Lyu, K., 2016. An efficient greedy insertion heuristic for energy-conscious single machine scheduling problem under time-of-use electricity tariffs. Journal of Cleaner Production, 129 (April), pp.565–577. 62. Che, A., Zhang, S., and Wu, X., 2017. Energy-conscious unrelated parallel machine scheduling under time-of-use electricity tariffs. Journal of Cleaner Production, 156 (April), pp.688–697. 63. Cheang, B., Li, H., Lim, A., and Rodrigues, B., 2003. Nurse rostering problems - A bibliographic survey. European Journal of Operational Research, 151 (3), pp.447–460. 64. Chen, F.F., 2014. Unrelated parallel machine scheduling with setup times using simulated annealing simulated annealing. Robotics and Computer Integrated Manufacturing 18, 5845 (February), pp.223–231. 65. Chen, M. and Niu, H., 2012. A Model for Bus Crew Scheduling Problem with Multiple Duty Types. Discrete Dynamics in Nature and Society, 2012, pp.1–11. 66. Chen, Q. and Li, C., 2010. An Approach to Bus-Driver Scheduling Problem. 2010 Second WRI Global Congress on Intelligent Systems, pp.379–382. 67. Chen, R.M. and Shih, H.F., 2013. Solving university course timetabling problems using constriction particle swarm optimization with local search. Algorithms, 6 (2), pp.227–244. 68. Chen, J., Pan, Q., and Li, J., 2012. Harmony search algorithm with dynamic control parameters. Applied Mathematics and Computation, 219 (2), pp.592–604. 69. Cheng, M., Ozaku, H.I., Kuwahara, N., Kogure, K., and Ota, J., 2008. Simulated annealing algorithm for scheduling problem in daily nursing cares. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp.1681–1687. 70. Cheng, M.Y., Prayogo, D., Wu, Y.W., and Lukito, M.M., 2016. A Hybrid Harmony Search Algorithm for Discrete Sizing Optimization of Truss Structure. Automation in Construction, 69, pp.21–33. 71. Čičková, Z. and Števo, S., 2010. Flow Shop Scheduling using Differential Evolution. Management Information Systems, 5, pp.8–13. 72. Colorni, A., Dorigo, M., Maniezzo, V., Milano, P., and Elettronica, D., 1990. Genetic Algorithms: A New Approach to the Time-Table Problem. Lecture Notes in Computer Science - NATO ASI Series, Vol.F 82, Combinatorial Optimization, (Ed. M.Akgül and others), Springer-Verlag, 235–239, pp.235–239. 73. Constantino, A.A., de Mendonça Neto, C.F.X., de Araujo, S.A., Landa-Silva, D., Calvi, R., and dos Santos, A.F., 2017. Solving a large real-world bus driver scheduling problem with a multi-assignment based heuristic algorithm. Journal of Universal Computer Science, 23 (5), pp.479–504. 74. Corne, D., Fang, H.-L., and Mellish, C., 1993. Solving the Modular Exam Sheduling Problem with Genetic Algorithm. DAI Research Paper, (622). 75. Crawford, B., Castro, C., and Monfroy, E., 2006. A Hybrid Ant Algorithm for the Airline Crew Pairing Problem, pp.381–391. 76. Croes, G.A., 1958. A Method for Solving Traveling-Salesman Problems. Operations Research, 6 (6), pp.791–812. 77. Dahiya, T., Kapil, E., Goyal, E., tech Student, M., Deptt, C., and CSE deptt, A., 2015. Exam Timetabling Problem Using G.A. International Journal of Recent Research Aspects, 2 (2), pp.165–168. 78. Dastanpour, A. and Mahmood, R.A.R., 2013. Feature selection based on genetic algorithm and SupportVector machine for intrusion detection system. International Conference on Informatics Engineering & Information Science, (September 2014), pp.169–181. 79. Dawson, D., Reynolds, A.C., Van Dongen, H.P.A., and Thomas, M.J.W., 2018. Determining the likelihood that fatigue was present in a road accident: A theoretical review and suggested accident taxonomy. Sleep Medicine Reviews, 42, pp.202–210. 80. De Leone, R., Festa, P., and Marchitto, E., 2010. A Bus Driver Scheduling Problem: a new mathematical model and a GRASP approximate solution. Journal of Heuristics, 17 (4), pp.441–466. 81. De Matta, R., and Peters, E., 2009. Developing work schedules for an inter-city transit system with multiple driver types and fleet types. European Journal of Operational Research, 192 (3), pp.852–865. 82. Deneubourg, J.-L., S. Aron, Goss, S., and Pasteels, J.M., 1990. The Self-Organizing Exploratory Pattern of the Argentine Ant. Journal of insect behavior, 3 (2), pp.159–168. 83. Deng, G.F. and Lin, W.T., 2011. Ant colony optimization-based algorithm for airline crew scheduling problem. Expert Systems with Applications. 84. Di Gaspero, L. and Schaerf, A., 2007. Tabu Search Techniques for Examination Timetabling, (January 2000), pp.104–117. 85. Diao, R. and Shen, Q., 2012. Feature selection with harmony search. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 42 (6), pp.1509–1523. 86. Dias, T.G., Sousa, J.P., and Cunha, J.F., 2001. A Genetic Algorithm for the Bus Driver Scheduling Problem. MIC’2001 - 4th Metaheuristics International Conference, pp.35–40. 87. Dias, T.G., Sousa, J.P., and Cunha, J.F., 2002. Genetic algorithms for the bus driver scheduling problem: a case study. Journal of the Operational Research Society, 53 (3), pp.324–335. 88. Dohn, A. and Mason, A., 2013. Branch-and-price for staff rostering: An efficient implementation using generic programming and nested column generation. European Journal of Operational Research, 230 (1), pp.157–169. 89. Dorigo, M., Maniezzo, V., and Colorni, a, 1996. Ant system: optimization by a colony of cooperating agents. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 26 (1), pp.29–41. 90. Dorigo, M. and Stützle, T., 2018. The Ant Colony Optimization Metaheuristic. Ant Colony Optimization, pp.1–42. 91. Dowsland, K. and Thompson, J., 2000. Solving a nurse scheduling problem with knapsacks, networks and tabu search. Journal of the Operational Research Society, 51 (7), pp.825–833. 92. Dowsland, K.A., 1998. Nurse Scheduling with tabu search and strategic oscillarion KA 93. Dowsland - European journal of operational research. European Journal of Operational Research. 94. Dowsland, K.A. and Park, S., 2000. Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem . Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem . Journal of Scheduling, 3 (3). 95. Duong, T. and Lam, K., 2004. Combining Constraint Programming And Simulated Annealing On University Exam Timetabling. International Conference on Computing and Communication Technologies RIVF’04, pp.205–210. 96. Eberhart and Yuhui Shi, 2002. Particle swarm optimization: developments, applications and resources, pp.81–86. 97. Eddaly, M., Rebaï, A., Jarboui, B., and Toumi, S., 2017. Branch-and-bound algorithm for solving blocking flowshop scheduling problems with makespan criterion. International Journal of Mathematics in Operational Research, 10 (1), pp.34. 98. Emden-weinert, T., Kotas, H., and Speer, U., 2001. DISSY – A Driver Rostering System for Public Transport. Rivers. 99. El-Abd, M., 2013. An improved global-best harmony search algorithm. Applied Mathematics and Computation, 222, pp.94–106. 100. Emden-Weinert, T. and Proksch, M., 1999. Best practice simulated annealing for the airline crew scheduling problem. Journal of Heuristics, 5 (4), pp.419–436. 101. Enayatifar, R. and Yousefi, M., 2013. LAHS: A Novel Harmony Search Algorithm Based on Learning Automata. Communications in Nonlinear Science and Numerical Simulation, pp. 3481–3497. 102. Englert, M., Röglin, H., and Vöcking, B., 2014. Worst case and probabilistic analysis of the 2-opt algorithm for the TSP. Algorithmica, 68 (1), pp.190–264. 103. Ernst, A.T., Jiang, H., Krishnamoorthy, M., and Sier, D., 2004. Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153 (1), pp.3–27. 104. Ezzinbi, O., Sarhani, M., El Afia, A., and Benadada, Y., 2014. Particle swarm optimization algorithm for solving airline crew scheduling problem. Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014, pp.52–56. 105. Fan, Q. and Yan, X., 2015. Self-adaptive differential evolution algorithm with discrete mutation control parameters. Expert Systems with Applications, 42 (3), pp.1551–1572. 106. Fanjul-Peyro, L. and Ruiz, R., 2011. Size-reduction heuristics for the unrelated parallel machines scheduling problem. Computers & Operations Research, 38 (1), pp.301–309. 107. Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., and Alizadeh, Y., 2008a. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Computer Methods in Applied Mechanics and Engineering, 197 (33–40), pp.3080–3091. 108. Fleischer, M., 1995. Simulated annealing: past, present, and future. Winter Simulation Conference Proceedings 1995, pp.155–161. 109. Fores, S., 1996. Column Generation Approaches to Bus Driver Scheduling. Strategies, (March). 110. Forsati, R., Mahdavi, M., Kangavari, M., and Safarkhani, B., 2008a. Web page clustering using harmony search optimization. Canadian Conference on Electrical and Computer Engineering, pp.1601–1604. 111. Forsati, R., Meybodi, M., Mahdavi, M., and Neiat, A., 2008b. Hybridization of K-Means and Harmony Search Methods for Web Page Clustering. 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp.329–335. 112. Forsyth, P. and Wren, A., 1997. An Ant System for Bus Driver Scheduling. 7th International Workshop on Computer-Aided Scheduling of Public Transport, (July). 113. Gartner, J., Musliu, N., and Slany, W., 2001. Rota: a research project on algorithms for workforce scheduling and shift design optimization. AI Communications, pp.83–92. 114. Geem, Z.W., Kim, J.H., and Yoon, Y.N., 2000. Parameter Calibration of the Nonlinear Muskingum Model using Harmony Search. 115. Geem, Z.W., Joong H.K., and Loganathan, G.V., 2001. A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76 (2), pp.60–68. 116. Geem, Z.W., Joong H.K., Loganathan, G.V., 2002. Application of Harmony Search 117. Algorithm to Water Resources Problems. Proceedings of 2002 Conference of the Environmental and Water Resources Institute of ASCE, Roanoke, USA, May 2002, 106 (11), pp.1323–1330. 118. Geem, Z.W., 2005. School Bus Routing using Harmony Search. 119. Geem, Z.W., Tseng, C.-L., and Park, Y., 2005. Harmony Search for Generalized Orienteering Problem: Best Touring in China, pp.741–750. 120. Geem, Z.W., 2007. Harmony Search Algorithm for Solving Sudoku, pp.1–8. 121. Geem, Z.W., 2007. Optimal cost design of water distribution networks using harmony search. Engineering Optimization, 39 (3), pp.259–277. 122. Grefenstette, J.J. and Baker, J.E., 1989. How genetic algorithms work a critical look at implicit parallelism. 123. Ghasem Moslehi and Mehdi Mahnam, 2011. A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics. 124. Glass, C. a. and Knight, R. a., 2010. The nurse rostering problem: A critical appraisal of the problem structure. European Journal of Operational Research, 202 (2), pp.379–389. 125. Glover, F., 1986. Future Paths for Integer Programming and Links to Artificial Intelligence. Computers and Operations Research, 13 (5), pp.533–549. 126. Goel, A., 2009. Vehicle Scheduling and Routing with Drivers ’ Working Hours, (October 2014). 127. Goel, A., Archetti, C., and Savelsbergh, M., 2012. Truck driver scheduling in Australia. Computers & Operations Research, 39 (5), pp.1122–1132. 128. Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA ©1989, pp.412. 129. Grant, T.J., 1986. Lessons for O.R. from A.I.: A scheduling case study. Journal of the Operational Research Society, 37 (1), pp.41–57. 130. Grosche, T., 2009. Computational Intelligence in Integrated Airline Scheduling. Springer. 131. Guillermo, C.G. and José, M.R.L., 2009. Hybrid algorithm of tabu search and integer programming for the railway crew scheduling problem. PACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications, 2, pp.413–416. 132. Gunawan, A., Ng, K.M., and Poh, K.L., 2012. A hybridized Lagrangian relaxation and simulated annealing method for the course timetabling problem. Computers and Operations Research. 133. Güney, K. and Akdağli, A., 2001. Null steering of linear antenna arrays using a modified tabu search algorithm. Journal of Electromagnetic Waves and Applications, 15 (7), pp.915–916. 134. Guo, Z., Yang, H., Wang, S., Zhou, C., and Liu, X., 2016. Adaptive harmony search with best-based search strategy. Soft Computing. 135. Gutjahr, W.J. and Rauner, M.S., 2007. An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Computers and Operations Research. 136. Hadwan, M. and Ayob, M., 2010. A constructive shift patterns approach with simulated annealing for Nurse Rostering problem. Proceedings 2010 International Symposium on Information Technology - Visual Informatics, ITSim’10, 1 (July). 137. Hadwan, M., Ayob, M., Sabar, N.R., and Qu, R., 2013. A harmony search algorithm for nurse rostering problems. Information Sciences, 233, pp.126–140. 138. Hanafi, R. and Kozan, E., 2014. A hybrid constructive heuristic and simulated annealing for railway crew scheduling. Computers & Industrial Engineering, pp.11–19. 139. Hao, P., Wang, Z., Wu, G., Kanok, and Barth, M., 2017. Intra-platoon vehicle sequence optimization for eco-cooperative adaptive cruise control. IEEE 20th International Conference on Intelligent Transportation Systems. 140. Haralick, R.M. and Elliott, G.L., 1980. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14 (3), pp.263–313. 141. Harrington, J.M., 2015. Health effects of shift work and Extended hours of work. Occup Environ Med 2001, pp.68–72. 142. Hegerty, B., Hung, C., and Kasprak, K., 2009. A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems. Mexican International Conference on Artificial Intelligence. 143. Heinonen, J. and Pettersson, F., 2007. Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem. Applied Mathematics and Computation, 187 (2), pp.989–998. 144. Herroelen, W., 1997. A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. European journal of operational research, (9206). 145. Hojati, M. and Patil, A.S., 2011. An integer linear programming-based heuristic for scheduling heterogeneous, part-time service employees. European Journal of Operational Research, 209 (1), pp.37–50. 146. Holland, J.H., 1984. Genetic Algorithms and Adaptation. Springer, 16, pp.317–333. 147. Holland, J.H., 1992. Adaptation in natural and artificial systems. The MIT Press. 148. Hou, E.S.H., Ansari, N., and Ren, H., 1994. A Genetic Algorithm for Multiprocessor Scheduling. IEEE Transactions on Parallel and Distributed Systems, 5 (2), pp.113–120. 149. Hou, P., Wang, D., and Li, X., 2012. An improved harmony search algorithm for blocking job shop to minimize makespan, pp.763–768. 150. Huang, K.L. and Liao, C.J., 2008. Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers and Operations Research. 151. Huang, S.H., Yang, T.H., and Wang, R.T., 2011. Ant colony optimization for railway driver crew scheduling: From modeling to implementation. Journal of the Chinese Institute of Industrial Engineers, 28 (6), pp.437–449. 152. Ibaraki, T. and Nakamura, Y., 1994. A dynamic programming method for single machine scheduling. European Journal of Operational Research, 76 (1), pp.72–82. 153. Ignall, E. and Schrage, L., 1964. Application of the branch and bound technique to some flow-shop scheduling problems. 154. Irene, H.S.F., Deris, S., Hashim, M., and Zaiton, S., 2009. University course timetable planning using hybrid particle swarm optimization, pp.239. 155. Ishibuchi, H., Misaki, S., and Tanaka, H., 1995. Modified simulated annealing algorithms for the flow shop sequencing problem. European Journal of Operational Research. 156. Islam, S.M., Das, S., Ghosh, S., Roy, S., and Suganthan, P.N., 2012. An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for 157. Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (2), pp.482–500. 158. J.Watson, Ross, C., Eisele, V., Denton, J., Bins, J., Guerra, C., D.Whitley, and Howe, A., 1998. The Traveling Salesrep Problem , Edge Assembly Crossover , and 2-opt. Parallel Problem Solving from Nature, 5th International Conference, pp.823–834. 159. James, R.J.W., 1997. Using tabu search to solve the common due date early/tardy machine scheduling problem. Computers and Operations Research, 24 (3), pp.199–208. 160. Jaradat, G., Al-Badareen, A., Ayob, M., Al-Smadi, M., Al-Marashdeh, I., Ash-Shuqran, M., and Al-Odat, E., 2019. Hybrid Elitist-Ant System for Nurse-Rostering Problem.pdf. Journal of King Saud University - Computer and Information Sciences, pp.378–384. 161. Jasmi, M.I., Rahman, A.F.N.A., Abas, Z.A., and Shibghatullah, A.S., 2016. Optimized Coating Design of Energy Saving Glass Using Binary Harmony Search for Better Transmission Signal. International Journal of Computer Science and Information Security;, 14 (8), pp.436–443. 162. Javier, D. S., Miren, N. B., Cristina, P., and Sancho, S.S., 2016. A Harmony Search Approach for the Selective Pick-Up and Delivery Problem with Delayed Drop-Off. Harmony Search Algorithm: Proceedings of the 2nd International Conference on Harmony Search Algorithm (ICHSA2015). 163. Jia, H.Z., Fuh, J.Y.H., Nee, A.Y.C., and Zhang, Y.F., 2007. Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers and Industrial Engineering, 53 (2), pp.313–320. 164. John H. Holland, 1992. Adaptation in natural and artificial systems. 165. Johnston, M.D., Johnston, M.D., Minton, S., and Minton, S., 1994. Analyzing a Heuristic Strategy for Constraint Satisfaction and Scheduling. Intelligent Scheduling. 166. Jütte, S., Müller, D., and Thonemann, U.W., 2017. Optimizing railway crew schedules with fairness preferences. Journal of Scheduling, 20 (1), pp.43–55. 167. Kang, S. and Chae, J., 2017. Harmony Search for the Layout Design of an Unequal Area Facility. Expert Systems with Applications, 79, pp.269–281. 168. Kasirzadeh, A., Saddoune, M., and Soumis, F., 2017. Airline crew scheduling: models, algorithms, and data sets. EURO Journal on Transportation and Logistics, 6 (2), pp.111–137. 169. Kaya, Y., Uyar, M., and Tekđn, R., 2011. A Novel Crossover Operator for Genetic Algorithms : Ring Crossover, pp.1–4. 170. Kazarlis, S., Petridis, V., and Fragkou, P., 2005. Solving University Timetabling Problems Using Advanced Genetic Algorithms. 5th International Conference on Technology and Automation. 171. Keikha, M.M., 2011. Improved Simulated Annealing using momentum terms. Proceedings - 2011 2nd International Conference on Intelligent Systems, Modelling and Simulation, ISMS 2011, pp.44–48. 172. Kennedy, J. and Eberhart, R., 1995. Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, pp.1942–1948. 173. Khalili, M., Kharrat, R., Salahshoor, K., and Sefat, M.H., 2014. Global Dynamic Harmony Search algorithm: GDHS. Applied Mathematics and Computation, 228, pp.195–219. 174. Khazali, A.H. and Kalantar, M., 2011. Optimal Reactive Power Dispatch based on Harmony Search Algorithm. International Journal of Electrical Power & Energy Systems, 33 (3), pp.684–692. 175. Kirkpatrick, S., Gelatt, C.D., and M. P. Vecchi Science, 1983. Optimization by Simulated Annealing, 220 (4598), pp.671–680. 176. Koo, J. and Kim, B.I., 2016. Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency. International Journal of Advanced Manufacturing Technology, 86 (9-12), pp.2803–2806. 177. Koulamas, C., Antony, S., and Jaen, R., 1994. A survey of simulated annealing applications to operations research problems. Omega. 178. Kuang, E., 2012. A 2-opt-based Heuristic for the Hierarchical Traveling Salesman Problem, (May), pp.1–22. 179. Kumar, V., 1992. Algorithms for Constraint-Satisfaction Problems: A Survey. AI Magazine, 13 (1), pp.32. 180. Kumar, V., Chhabra, J.K., and Kumar, D., 2014. Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. Journal of Computational Science, 5 (2), pp.144–155. 181. Laha, D., 2008. Heuristics and Metaheuristics for Solving Scheduling Problems. 182. Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Salcedo-Sanz, S., Manjarres, D., and Portilla-Figueras, J.A., 2011. A Grouping Harmony Search approach for the Citywide WiFi deployment problem. 2011 11th International Conference on Intelligent Systems Design and Applications, pp.1026–1031. 183. Larabi Marie-Sainte, S., 2015. A survey of Particle Swarm Optimization techniques for solving university Examination Timetabling Problem. Artificial Intelligence Review, 44 (4), pp.537–546. 184. Laurent, B. and Hao, J.K., 2007. Simultaneous vehicle and driver scheduling: A case study in a limousine rental company. Computers and Industrial Engineering. 185. Lavygina, A., Welsh, K., and Crispin, A., 2019. On Fairness as a Rostering Objective. GECCO ’19 Companion, July 13–17, 2019, Prague, Czech Republic, pp.217–218. 186. Lawrie, N.L., 1969. An integer linear programming model of a school timetabling problem. The Computer Journal, 12 (4), pp.307–316. 187. Lee, J.-H., Hsu, Y.-C., and Lin, Y.-L., 1989. A new integer linear programming formulation for the scheduling problem in data path synthesis. International Conference on Computer-Aided Design. Digest of Technical Papers, 1989. 188. Lee, K.-M., Yamakawa, T., and Keon-Myung Lee, 2002. A genetic algorithm for general machine scheduling problems, (April), pp.60–66. 189. Lee, K.S. and Geem, Z.W., 2005. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 194 (36-38), pp.3902–3933. 190. Legrain, A., Bouarab, H., and Lahrichi, N., 2015. The Nurse Scheduling Problem in Real-Life. Journal of Medical Systems, 39 (1). 191. Lei, Y., Gong, M., Jiao, L., Li, W., Zuo, Y., and Cai, Q., 2014. A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems. Mathematical Problems in Engineering, 2014, pp.1–13. 192. Lenstra, J. and Rinnooy Kan, a, 1981. Complexity of Vehicle Routing and Scheduling Problems. Networks. 193. Leone, R. De, Festa, P., and Marchitto, E., 2009. Solving a bus driver scheduling problem with randomized multistart heuristics Randomized heuristics, pp.1–6. 194. Lewis, R., 2008. A survey of metaheuristic-based techniques for University Timetabling problems. OR Spectrum, 30 (1), pp.167–190. 195. Li, J., 2005. A Self-Adjusting Algorithm for Driver Scheduling. Journal of Heuristics, 11 (4), pp.351–367. 196. Li, J. and Kwan, R.S.K., 2003a. A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research, 147 (2), pp.334–344. 197. Li, J. and Kwan, R.S.K., 2003b. A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research. 198. Li, J.Q., Pan, Q.K., Suganthan, P.N., and Chua, T.J., 2011. A hybrid tabu search algorithm with an efficient neighborhood structure for the flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 52 (5-8), pp.683–697. 199. Li, S., 2014. Hyper-heuristic cooperation based approach for bus Thèse de Doctorat. 200. Li, X., Gao, L., Pan, Q., Wan, L., and Chao, K.M., 2018. An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 201. Limlawan, V., Kasemsontitum, B., and Jeenanunta, C., 2011. Airline crew rostering problem using particle swarm optimization. 2011 IEEE International Conference on Quality and Reliability, pp.501–505. 202. Lin, S., 1965. Computer Solutions of the Traveling Salesman Problem. Bell System Technical Journal, 44 (10), pp.2245–2269. 203. Lin, W., Cheng, F., and Tsay, M., 2002. An Improved Tabu Search for Economic Dispatch. Power, 17 (1), pp.108–112. 204. Liu, B., Wang, L., and Jin, Y.H., 2007. An effective hybrid particle swarm optimization for no-wait flow shop scheduling. International Journal of Advanced Manufacturing Technology, 31 (9-10), pp.1001–1011. 205. Liu, S.Q. and Ong, H.L., 2004. Metaheuristics for the Mixed Shop Scheduling Problem. Asia-Pacific Journal of Operational Research, 21, pp.97–115. 206. Lourenço, H.R., 2005. Metaheuristics for The Bus-Driver Scheduling Problem. Economic Working Papers Series, no. 304, Universitat Pompeu Fabra, pp.1–26. 207. Low, C., 2005. Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers and Operations Research, 32 (8), pp.2013–2025. 208. Lü, Z. and Hao, J.K., 2010. Adaptive Tabu Search for course timetabling. European Journal of Operational Research. 209. Mauro, D. and Marco, T., 1993. Applying Tabu Search to the Job-Shop Scheduling Problem. Annals of Operations Research, 41, pp.231–252. 210. Ma, J., Liu, T., and Zhang, W., 2014. A Genetic Algorithm Approach to the Balanced Bus Crew Rostering Problem. Journal of Traffic and Logistics Engineering, 2 (1), pp.13–20. 211. Ma, J., Song, C., Ceder, A., Liu, T., and Guan, W., 2017. Fairness in optimizing bus-crew scheduling process. PLoS ONE, 12 (11), pp.1–20. 212. Ma, Z., Liu, L., and Sukhatme, G.S., 2016. An Adaptive k -opt Method for Solving Traveling Salesman Problem. 2016 IEEE 55th Conference on Decision and Control (CDC), (Cdc), pp.6537–6543. 213. Maenhout, B. and Vanhoucke, M., 2010. A hybrid scatter search heuristic for personalized crew rostering in the airline industry. European Journal of Operational Research, 206 (1), pp.155–167. 214. Mahdavi, M., Fesanghary, M., and Damangir, E., 2007. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188 (2), pp.1567–1579. 215. Mansor, N.F., Abal Abas, Z., Abdul Rahman, A.F.N., Shibghatullah, A.S., and Sidek, S., 2014. An analysis of the parameter modifications in varieties of harmony search algorithm. International Review on Computers and Software, 9 (10), pp.1736–1749. 216. Mansor, N.F., Abas, Z.A., Shibghatullah, A.S., and Rahman, A.F.N.A., 2018. Bus Driver Scheduling and Rostering : Maximise Fairness using Enhanced Harmony Search Algorithm with Varieties of Step Functions and Fret Spacing Formula. International Journal of Engineering & Technology, 7, pp.136–143. 217. Martello, S. and Toth, P., 1986. A heuristic approach to the bus driver scheduling problem. European Journal of Operational Research, 24 (1), pp.106–117. 218. Matayoshi, M. and Nakamura, M., 2004. A Genetic Algorithm with the improved 2-opt method*. 2004 IEEE International Conference on Systems, Man and Cybernetics A, pp.3652–3658. 219. Maurya, A., 2018. Calibration of Inertial Sensor by using Particle Swarm Optimization and Human Opinion Dynamics Algorithm, (August). 220. May, J.F., 2011. Driver fatigue. Handbook of Traffic Psychology. Elsevier. 221. Meilton, M., 2001. Selecting and Implementing a Computer Aided Scheduling System for a Large Bus Company. Computer-Aided Scheduling of Public Transport, pp.203–214. 222. Mikaeil, R., Ozcelik, Y., Ataei, M., and Shaffiee Haghshenas, S., 2016. Application of harmony search algorithm to evaluate performance of diamond wire saw. Journal of Mining and Environment, 10 (1). 223. Minton, S., Johnston, M.D., Philips, A.B., and Laird, P., 1992. Minimizing conflicts: aheuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58 (1-3), pp.161–205. 224. Montalva, S., Muñoz, J.C., and Paredes, R., 2010. Assignment of work shifts to public transit drivers based on stated preferences. Public Transport, 2 (3), pp.199–218. 225. Moon, Y.Y., Geem, Z.W., and Han, G.T., 2018. Vanishing point detection for self-driving car using harmony search algorithm. Swarm and Evolutionary Computation, 41 (January 2017), pp.111–119. 226. Moz, M., Respício, A., and Pato, M.V., 2009. Bi-objective evolutionary heuristics for bus driver rostering. Public Transport, 1 (3), pp.189–210. 227. Moz, M. and Vaz Pato, M., 2007. A genetic algorithm approach to a nurse rerostering problem. Computers and Operations Research, 34 (3), pp.667–691. 228. Muramudalige, S.R. and Bandara, H.M.N.D., 2018. Automated Driver Scheduling for Vehicle Delivery. Springer International Publishing. 229. Musliu, N., Schaerf, A., and Slany, W., 2001. Local Search for Shift Design. Proc.\ of the 4th Metaheuristics International Conference, pp.465–470. 230. Naji Azimi, Z., 2005. Hybrid heuristics for examination timetabling problem. Applied Mathematics and Computation, 163 (2), pp.705–733. 231. Nie, Y., Wang, B., and Zhang, X., 2016. Hybrid Harmony Search Algorithm for Nurse Rostering Problem. Proceedings of the 2nd International Conference on Harmony Search Algorithm (ICHSA2015), pp.1–12. 232. Nishi, T., Sugiyama, T., and Inuiguchi, M., 2014. Two-level decomposition algorithm for crew rostering problems with fair working condition. European Journal of Operational Research, 237 (2), pp.465–473. 233. Niu, Q., Jiao, B., and Gu, X., 2008. Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time. Applied Mathematics and Computation, 205 (1), pp.148–158. 234. Nothegger, C., Mayer, A., Chwatal, A., and Raidl, G.R., 2012. Solving the post enrolment course timetabling problem by ant colony optimization. Annals of Operations Research, 194 (1), pp.325–339. 235. Nurmi, K., Kyngäs, J., and Post, G., 2011. Driver Rostering for Bus Transit Companies, (May). 236. Omran, M.G.H. and Mahdavi, M., 2008. Global-best harmony search. Applied Mathematics and Computation, 198 (2), pp.643–656. 237. Osman, I.H. and Laporte, G., 1996. Metaheuristics: A bibliography, 63. 238. Osogami, T. and Imai, H., 2000. Classification of various neighborhood operations for the 239. nurse scheduling problem. Lecture Notes in Computer Science, 1969, pp.72–83. 240. Oughalime, A., Ismail, W.R., Liong, C., and Ayob, M., 2009. Vehicle and Driver Scheduling Modelling : A Case Study in UKM, (October), pp.53–59. 241. Ouyang, H., Gao, L., Liz, S., Kong, X., and Zou, D., 2016. Improved Harmony Search Algorithm : LHS. Applied Soft Computing Journal. 242. Pan, Q.K., Suganthan, P.N., Tasgetiren, M.F., and Liang, J.J., 2010. A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216 (3), pp.830–848. 243. Pezzella, F., Morganti, G., and Ciaschetti, G., 2008. A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers and Operations Research, 35 (10), pp.3202–3212. 244. Pillay, N. and Banzhaf, W., 2010. An informed genetic algorithm for the examination timetabling problem. Applied Soft Computing, 10 (2), pp.457–467. 245. Pillay, N. and Qu, R., 2018. Hyper-Heuristics: Theory and Applications, pp.61–66. 246. Poli, R., Kennedy, J., and Blackwell, T., 2018. Particle swarm optimization: An overview. Natural Computing Series, (November), pp.97–102. 247. Portugal, R., Ramalhinho-Lourenço, H., and Paixão, J.P., 2009. Driver Scheduling Problem Modelling. Public Transport, pp.1–17. 248. Qarouni-Fard, D., Najafi-Ardabifio, A., Moeinzadeht, M., Sharifian-Re, S., Asgarianv, E., and Mohammadzadeht, J., 2008. Finding Feasible Timetables with Particle Swarm Optimization Danial. Optimization, pp.387–391. 249. Ramli, M.R., Hussin, B., Abas, Z.A., and Ibrahim, N.K., 2016a. Solving complex nurse scheduling problems using particle swarm optimization. International Review on Computers and Software, 11 (9), pp.834–841. 250. Ramli, M.R., Hussin, B., Abas, Z.A., and Ibrahim, N.K., 2016b. Solving complex nurse scheduling problems using particle swarm optimization. International Review on Computers and Software, 11 (9), pp.834–841. 251. Ramli, M.R., Hussin, B., and Ibrahim, N.K., 2013a. Utilizing Particle Swarm Optimisation Techniques in Solving Unfair Nurse Scheduling Problem. International Review on Computers and Software(IRECOS), 8 (August), pp.2205–2212. 252. Ramli, R., Ibrahim, H., and Shung, L.T., 2013b. Innovative Crossover and Mutation in a Genetic Algorithm Based Approach to a Campus Bus Driver Scheduling Problem with Break Consideration and Embedded Overtime. Applied Mathematics & Information Sciences, 7 (5), pp.1921–1928. 253. Rinnooy Kan, A.H.G., 1976. Machine scheduling problems : classification, complexity and computations. 254. Robert R., M. and James C., M., 1978. Effects of Hours of Service Regularity of Schedules, and Cargo Loading on Truck. 255. Rocha, M., 2012. Quantitative Approaches on Staff Scheduling and Rostering in Hospitality Management: An Overview. American Journal of Operations Research, 02 (01), pp.137–145. 256. Rocha, M., Oliveira, J.F., and Carravilla, M.A., 2012. Cyclic staff scheduling: optimization models for some real-life problems. Journal of Scheduling, 16 (2), pp.231–242. 257. Rohani, M.M., Wijeyesekera, D.C., and Karim, A.T.A., 2013. Bus operation, quality service and the role of bus provider and driver. Procedia Engineering, 53, pp.167–178. 258. Rossi-Doria, O. and Paechter, B., 2012. An hyperheuristic approach to course timetabling problem using an evolutionary algorithm. Evolutionary Computation. 259. Rothlauf, F., 2011. Design of Modern Heuristics: principles and Application. Design of Modern Heuristics: Principles and Application. 260. Lokare, P.S. and Mahind, R.N., 2014. Solving Nurse Rostering Problem Using Ant Colony Optimization Approach. International Journal of Modern Trends in Engineering and Research. 261. Sadeh, N., 1991. Look-Ahead Techniques for Micro- Opportunistic Job Shop Scheduling, (March). 262. Saidi-Mehrabad, M. and Bairamzadeh, S., 2018. Design of a Hybrid Genetic Algorithm for Parallel Machines Scheduling to Minimize Job Tardiness and Machine Deteriorating Costs with Deteriorating Jobs in a Batched Delivery System. Journal of Optimization in Industrial Engineering. 263. Saji, Y., Riffi, M.E., and Ahiod, B., 2013. Multi-Objective Ant Colony Optimization Algorithm to Solve a Nurse Scheduling Problem. International Journal of Advanced Research in Computer Science and Software Engineering, 3 (8), pp.311–320. 264. Salminen, S., 2016. Long Working Hours and Shift Work as Risk Factors for Occupational Injury. The Ergonomics Open Journal, 9 (1), pp.15–26. 265. Shabani, M., Abolghasem Mirroshandel, S., and Asheri, H., 2017. Selective Refining Harmony Search: A new optimization algorithm. Expert Systems with Applications, 81, pp.423–443. 266. Shaffiei, Z.A., Abas, Z.A., Fadzli, A., and Abdul, N., 2014. Optimization in Driver ’s Scheduling for University. International Symposium on Research in Innovation and Sustainability 2014 (ISoRIS ’14) 15-16 October 2014, Malacca, Malaysia, 2014 (October), pp.15–16. 267. Shaffiei, Z.A., Abas, Z.A., Shibghatullah, A.S., Fadzli, A., and Abdul, N., 2016. An 268. Optimized Intelligent Automation for University Shuttle Bus Driver Scheduling Using Mutual Swapping and Harmony Search. International Journal of Computer Science and Information Security, 14 (8), pp.875–884. 269. Shaffiei, Z.A., Abas, Z.A., Yunos, N.M., Amir Hamzah, A.S.S.S., Abidin, Z.Z., and Eng, C.K., 2018. Constrained Self-Adaptive Harmony Search Algorithm with 2-opt Swapping for Driver Scheduling Problem of University Shuttle Bus. Arabian Journal for Science and Engineering. 270. Sheau Fen Ho, I., Safaai, D., and Siti Zaiton, M.H., 2009. A study on PSO-based university course timetabling problem. Proceedings - International Conference on Advanced Computer Control, ICACC 2009, (Figure 1), pp.648–651. 271. Shi, P. and Landa-Silva, D., 2019. Lookahead policy and genetic algorithm for solving nurse rostering problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11331 LNCS, pp.460–471. 272. Shiau, D.F., 2011. A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Systems with Applications, 38 (1), pp.235–248. 273. Shivaie, M., Kazemi, M.G., and Ameli, M.T., 2015. A modified harmony search algorithm for solving load-frequency control of non-linear interconnected hydrothermal power systems. Sustainable Energy Technologies and Assessments, 10, pp.53–62. 274. Shyu, S.J., Lin, B.M.T., and Yin, P.Y., 2004. Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time. Computers and Industrial Engineering. 275. Skutella, M. and Uetz, M., 2005. Stochastic Machine Scheduling with Precedence Constraints. SIAM Journal on Computing, 34 (4), pp.788–802. 276. Smet, P., Martin, S., Ouelhadj, D., Ozcan, E., and Berghe, G. Vanden, 2012. Investigation of fairness measures for nurse rostering. 277. Socha, K. and Dorigo, M., 2008. Ant colony optimization for continuous domains. European Journal of Operational Research, 185 (3), pp.1155–1173. 278. Socha, K., Sampels, M., and Manfrin, M., 2003. Ant Algorithms for the University Course Timetabling Problem with Regard to the State-of-the-Art, pp.334–345. 279. Solimanpur, M., Vrat, P., and Shankar, R., 2004. A neuro-tabu search heuristic for the flow shop scheduling problem. Computers and Operations Research. 280. Soundarya, A., Murugan, R., and Thesis, M., 2009. University Timetabling using Genetic Algorithm, 46 (0), pp.1–45. 281. Storn, R. and Price, K., 1995. Differential Evolution- A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, pp.1–12. 282. Summala, H. and Mikkola, T., 1994. Fatal accidents among car and truck drivers: Effects of fatigue, age, and alcohol consumption. Human Factors, 36 (2), pp.315–326. 283. Suyabatmaz, A.Ç. and Şahin, G., 2015. Railway crew capacity planning problem with connectivity of schedules [online]. Transportation Research Part E: Logistics and Transportation Review. 284. Taghipour, M., Moradi, A.R., and Yazdani-Asrami, M., 2010. Identification of magnetizing inrush current in power transformers using GSA trained ANN for educational purposes. ICOS 2010 - 2010 IEEE Conference on Open Systems, (January), pp.23–27. 285. Tassopoulos, I.X. and Beligiannis, G.N., 2012. A hybrid particle swarm optimization based algorithm for high school timetabling problems. Applied Soft Computing Journal, 12 (11), pp.3472–3489. 286. Taylor, L.A., 2013. Local Search Methods for the Post Enrolment-based Course Timetabling Problem A thesis submitted for the degree of. 287. Tellier, P. and White, G., 2006. Generating Personnel Schedules in an Industrial Setting Using a Tabu Search Algorithm, pp.293–302. 288. Thiffault, P. and Bergeron, J., 2003. Monotony of road environment and driver fatigue: A simulator study. Accident Analysis and Prevention, 35 (3), pp.381–391. 289. Thompson, J.M. and Dowsland, K.A., 1998. A robust simulated annealing based examination timetabling system. Computers & Operations Research. 290. Tse, J.L.M., Flin, R., and Mearns, K., 2006. Bus driver well-being review : 50 years of research, 9, pp.89–114. 291. Turner, J.F., 1946. Timetable and Duty Schedule Compilation (Road Passenger Transport). 292. Ulrich, W., 2015. Optimizing Crew Schedules with Fairness Preferences. 293. Urquhart, N., 2013. Automated Scheduling and Planning. Berlin, Heidelberg: Springer Berlin Heidelberg. 294. Vallada, E. and Ruiz, R., 2011. A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211 (3), pp.612–622. 295. Valouxis, C. and Housos, E., 2002. Combined bus and driver scheduling. Computers & Operations Research, 29 (January 2000), pp.243–259. 296. Van Laarhoven, P.J.M., Aarts, E.H.L., and Lenstra, J.K., 1992. Job Shop Scheduling by Simulated Annealing. Operations Research, 40 (1), pp.113–125. 297. Vaquerizo, M.B. and Baruque, B., 2012. Combining Metaheuristic Algorithms to Solve a Scheduling Problem. Hybrid Artificial Intelligent Systems, pp.381–391. 298. Wahid, J., 2013. Harmony Search Algorithm for Curriculum-Based Course Timetabling Problem, 3 (3), pp.365–371. 299. Wang, C.M. and Huang, Y.F., 2010. Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications, 37 (4), pp.2826–2837. 300. Widmer, M., Hertz, A., and Costa, D., 2010. Metaheuristics and Scheduling. Production Scheduling, pp.33–68. 301. Wolpert, D.H. and Macready, W.G., 1997. No Free Lunch Theorems for Optimization, 1 (1), pp.67–82. 302. Wren, A., 1995. Scheduling , Timetabling and Rostering - A Special Relationship ? First International Conference on Practice and Theory of Automated Timetabling, pp.46–75. 303. Wren, A. and Rousseau, J., 1993. Bus Driver Scheduling - An Overview, (July). 304. Wu, T.H., Yeh, J.Y., and Lee, Y.M., 2015. A particle swarm optimization approach with refinement procedure for nurse rostering problem. Computers and Operations Research, 54, pp.52–63. 305. Xia, W.J. and Wu, Z.M., 2006. A hybrid particle swarm optimization approach for the job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 29 (3-4), pp.360–366. 306. Xie, L., 2013. Cyclic and non-cyclic crew rostering problems in public bus transit. 307. Xie, L., Kliewer, N., and Suhl, L., 2012. Integrated Driver Rostering Problem in Public Bus Transit. Procedia - Social and Behavioral Sciences, 54, pp.656–665. 308. Xu, Q., Mao, J., and Jin, Z., 2012. Simulated annealing-based ant colony algorithm for tugboat scheduling optimization. Mathematical Problems in Engineering, 2012. 309. Yadav, P., Kumar, R., Panda, S.K., and Chang, C.S., 2012. An Intelligent Tuned Harmony Search algorithm for optimisation. Information Sciences, 196, pp.47–72. 310. Yaghini, M., Karimi, M., and Rahbar, M., 2015. A set covering approach for multi-depot train driver scheduling. Journal of Combinatorial Optimization, 29 (3), pp.636–654. 311. Yang, X.S., 2009. Harmony search as a metaheuristic algorithm. Studies in Computational Intelligence, 191, pp.1–14. 312. Yuce, B., Fruggiero, F., Packianather, M.S., Pham, D.T., Mastrocinque, E., Lambiase, A., and Fera, M., 2017. Hybrid Genetic Bees Algorithm applied to single machine scheduling with earliness and tardiness penalties. Computers and Industrial Engineering, 113, pp.842–858. 313. Zamli, K.Z., 2014. Development of Simulated Annealing Based Scheduling Algorithm for Two Machines Flow Shop Problem. Malaysia University Conference Engineering Technology, (November), pp.10–11. 314. Zamli, K.Z., Din, F., Kendall, G., and Ahmed, B.S., 2017. An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation. Information Sciences, 399, pp.121–153. 315. Zhan, S.H., Lin, J., Zhang, Z.J., and Zhong, Y.W., 2016. List-Based Simulated Annealing Algorithm for Traveling Salesman Problem. Computational Intelligence and Neuroscience, 2016. 316. Zhang, Y. and Wildemuth, B.M., 2005. Unstructured Interviews, (1998), pp.1–10. 317. Zhao, L., 2006. A heuristic method for analyzing driver scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 36 (3), pp.521–531. 318. Zhao, X., 2010. An Enhanced Particle Swarm Optimization Algorithm with Passive Congregation. 2010 International Conference on Machine Vision and Human-machine Interface, (4), pp.432–435. 319. Zhao, F., Liu, Y., Zhang, C., and Wang, J., 2015. A self-adaptive harmony PSO search algorithm and its performance analysis. Expert Systems with Applications, 42 (21), pp.7436–7455. 320. Zhao, X., Liu, Z., Hao, J., Li, R., and Zuo, X., 2017. Semi-self-adaptive harmony search algorithm. Natural Computing, 16 (4), pp.619–636.