Workers Scheduling in Production Logistics in a Job Shop Production System
Subject Areas : Mathematical Optimization
Ameneh Farahani
1
*
,
Mohammadhossein Ladan-Moghadam
2
1 - Department of Industrial Engineering, Ooj Institute of Higher Education, Qazvin, Iran
2 - Department of Industrial Engineering, ST.C, Islamic Azad University, Tehran, Iran
Keywords: Genetic algorithm, Mixed integer programming, Multi-objective, Optimization, Worker’s scheduling, Un-paced asynchronous assembly lines,
Abstract :
This paper presents a Genetic algorithm for solving a multi-objective deterministic mixed integer nonlinear programming modeling of the workers scheduling problem in production logistics in a job shop production system. The proposed model integrates multiple decisions into the workers scheduling problem. In this model, production planning is independent of the influence of human resources management. In the other word, production planning is fixed and workers scheduling is based on it. This model explicitly taking into consideration the individual competencies and preferences of each worker, also the workers and competency requirements relate to each assembly activity. The objective of the introduced model is to allocate employees to assembly activities while minimizing human resources costs and workers dissatisfactions. To benchmark the performance of this Genetic algorithm, optimal solutions obtained through a mathematical model resolution using commercial solver CPLEX are used. Results show that this Genetic algorithm can produce high-quality and efficient solutions in a short computational time.
[1] Sabar, M., Montreuil, B., Frayret, J-M. (2008). Competency and preference-based workers scheduling in large assembly lines. International Journal of Computer Integrated Manufacturing, 21, 468-479. https://doi.org/10.1080/09511920701574842.
[2] M. A. O. Louly, “A goal programming model for staff scheduling at a telecommunications center,”. J. Math. Model. Algorithms Oper Res, vol. 12, no. 2, pp. 167-178.2013. https://doi.org/10.1007/s10852-012-9200-x.
[3] Bailey, J. and Field, J. (1985). Workers scheduling with flexible shift models. Journal of Operations management, 5, 327–338.
[4] Aickelin, U., &Dowsland, K. A. (2004). An indirect Genetic Algorithm for a nurse-scheduling problem. Computers & Operations Research, 31, 761-778. https://doi.org/10.1016/S0305-0548(03)00034-0.
[5] Asgeirsson, E. I. (2014). Bridging the gap between self-schedules and feasible schedules in staff scheduling. Annals of Operations Research, 218, 51-69. https://doi.org/10.1007/s10479-012-1060-2.
[6] Awadallah, M., Khader, A., Al-Betar, M., &Bolaji, A. (2011). Nurse Rostering Using Modified Harmony Search Algorithm.Second international conference on Swarm, Evolutionary, and Memetic Computing, Springer Berlin Heidelberg, Lecture Notes in Computer Science, 7077, 27-37. https://doi.org/10.1007/978-3-642-27242-4_4.
[7] Bard, J., &Purnomo, H. (2005). Short-Term Nurse Scheduling in Response to Daily Fluctuations in Supply and Demand.Health Care Management Science, 8, 315-324. https://doi.org/10.1007/s10729-005-4141-9.
[8] Beliën, J., &Demeulemeester, E. (2008). A branch-and-price approach for integrating nurse and surgery scheduling.European Journal of Operational Research, 189, 652-668. https://doi.org/10.1016/j.ejor.2006.10.060.
[9] Bester, M. J., Nieuwoudt, I., & Van Vuuren, J. H.(2007). Finding good nurse duty schedules: A case study. Journal of Scheduling, 10, 387-405. https://doi.org/10.1007/s10951-007-0035-7.
[10] Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., VandenBerghe, G., &Wauters, T. (2010). A hyper-heuristic combined with a greedy shuffle approach to the nurse rostering competition. International Conference on Practice and Theory of Automated Timetabling Viii.
[11] Brucker, P., Burke, E. K., Curtois, T., Qu, R., &VandenBerghe, G. (2010). A shift sequence-based approach for nurse scheduling and a new benchmark dataset. Journal of Heuristics, 16, 559-573. https://doi.org/10.1007/s10732-008-9099-6
[12] Burke, E., &Curtois, T. (2010). An ejection chain method and a branch and price algorithm applied to the instances of the first international nurse rostering competition, 2010. International Conference on Practice and Theory of Automated Timetabling Viii.
[13] Burke, E. K., Curtois, T., Qu, R., &VandenBerghe, G. (2010). A scatter search methodology for the nurse rostering problem. Journal of the Operational Research Society, 61, 1667-1679. https://doi.org/10.1057/jors.2009.118.
[14] Burke, E. K., Curtois, T., van Draat, L. F., van Ommeren, J. K., & Post, G. (2011). Progress control in iterated local search for nurse rostering.Journal of the Operational Research Society, 62, 360-367. https://doi.org/10.1057/jors.2010.86.
[15] Fahle, T., &Bertels, S. (2006). A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem. Computers & Operations Research, 33, 2866-2890. https://doi.org/10.1016/j.cor.2005.01.015.
[16] Gutjahr, W. J., &Rauner, M. S. (2007). An ACO algorithm for a dynamic regional nurse scheduling problem in Austria. Computers & Operations Research, 34, 642-666. https://doi.org/10.1016/j.cor.2005.03.018.
[17] Li, J. P., &Aickelin, U. (2004). The application of Bayesian optimization and classifier systems in nurse scheduling. Parallel Problem Solving from Nature - Ppsn Viii, Lecture Notes in Computer Science, 3242, 581-590.
[18] Lu, Z., &Hao, J.-K. (2012). Adaptive neighborhood search for nurse rostering. European Journal of Operational Research, 218, 865-876. https://doi.org/10.1016/j.ejor.2011.12.016.
[19] Maenhout, B., &Vanhoucke, M. (2011). An evolutionary approach for the nurse rerostering problem. Computers & Operations Research, 38, 1400-1411. https://doi.org/10.1016/j.cor.2010.12.012.
[20] Stolletz, R., & Brunner, J. O. (2012). Fair optimization of fortnightly physician schedules with flexible shifts. European Journal of Operational Research, 219, 622-629. https://doi.org/10.1016/j.ejor.2011.10.038.
[21] Thompson, J. M., & Parr, D. (2007). Solving the multi-objective nurse scheduling problem with a weighted cost function. Annals of Operations Research, 155, 279-288. https://doi.org/10.1007/s10479-007-0202-4.
[22] Topaloglu, S., &Ozkarahan, I. (2004). An implicit goal programming model for the tour scheduling problem considering the employee work preferences. Annals of Operations Research, 128, 135-158. https://doi.org/10.1023/B:ANOR.0000019102.68222.df.
[23] Wright, P. D., &Bretthauer, K. M. (2010). Strategies for Addressing the Nursing Shortage: Coordinated Decision Making and Workforce Flexibility. Decision Sciences, 41, 373-401. https://doi.org/10.1111/j.1540-5915.2010.00269.x.
[24] Wright, P. D., Bretthauer, K. M., & Cote, M. J. (2006). Reexamining the nurse scheduling problem: Staffing ratios and nursing shortages. Decision Sciences, 37, 39-70. https://doi.org/10.1111/j.1540-5414.2006.00109.x.
[25] Maenhout, B., &Vanhoucke, M. (2010). A hybrid scatter search heuristic for personalized crew rostering in the airline industry. European Journal of Operational Research, 206, 155-167. https://doi.org/10.1016/j.ejor.2010.01.040.
[26] [26] Sabar, M., Montreuil, B., &Frayret, J. M. (2009). A multi-agent-based approach for workers scheduling in assembly centres. Engineering Applications of Artificial Intelligence, 22, 1080-1088. https://doi.org/10.1016/j.engappai.2009.02.00009.
[27] Boysen, N., Fliedner, M., Scholl, A. (2008). Assembly line balancing: Which model to use when. International Journal of Producyion Economics, 111, 509-528. https://doi.org/10.1016/j.ijpe.2007.02.026.
[28] Van den Bergh, J., De Bruecker, P., De Boeck, L. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226, 367-385. https://doi.org/10.1016/j.ejor.2012.11.029.
[29] Farahani, A., Farahani, H., Farahani, G., & Mousavi, S. (2022, September). Flexible personnel scheduling in large multi-product unpaced asynchronous assembly lines. In 2022 27th International Conference on Automation and Computing (ICAC) (pp. 1-6). IEEE. DOI: 10.1109/ICAC55051.2022.9911088.