İŞ-YAŞAM DENGESİ AÇISINDAN PERSONEL ÇİZELGELEMENİN İNCELENMESİ

Year 2020, Volume: 8 Issue: 5, 149 - 155, 29.12.2020

Halil Koruca , Esra Gulmez , Samia Gamoura , Emine Kocaer

https://doi.org/10.21923/jesd.829168

Abstract

İş-yaşam dengesi (WLB), çalışanların aile ve iş sorumluluklarını sorunsuz bir şekilde yerine getirme becerisi olarak tanımlanabilir. Buna göre, bireylerin iş ve iş dışı yaşamlarında üstlendikleri görevler arasındaki çatışmayı minimumda tutabilmek için bireyin belirleyebileceği bir ortam oluşturularak iş-yaşam dengesi sağlanabilir. Önceki yıllarda yapılan bir araştırmada, çalışanların% 30'unun uzun çalışma saatleri,% 21'inin yüksek iş yükü ve% 16'sının işle ilgili stres nedeniyle işlerinden memnun olmadığı tespit edilmiştir. Çalışanların% 11'inin sosyal yaşantısına (ailesine) yeterince zaman ayıramadığı ve bu nedenle iş-yaşam dengesini kurmakta zorlandığı görülmüştür. Çalışanların iş başarısı ve sosyal yaşamlarındaki denge için vardiya planlaması ile en uygun çözümlerin geliştirilmesi gereklidir. Bu nedenle geliştirilen yazılım çalışanların iş-yaşam dengesine uygun olarak geliştirilmiş ve yazılımda farklı yöntemlerle programlama algoritmaları uygulanmıştır.

Bu çalışmada, karınca kolonisi algoritması ve parçacık sürüsü algoritması literatür araştırması, hastane çalışanlarının iş-yaşam dengesine uygun çizelgeleme problemleri için yapılmıştır. Algoritmalar için gerekli parametreler dikkate alınarak geliştirilen yazılım ile çizelgeleme akışları çıkarılmıştır.

Keywords

İş-yaşam dengesi, Vardiya planlaması, Personel Çizelgeleme, Algoritma

REVIEW OF PERSONNEL SCHEDULE IN TERM OF WORK-LIFE BALANCE

Abstract

Work-life balance (WLB) can be defined as the ability of employees to fulfill their family and work responsibilities smoothly. Accordingly, WLB can be achieved by creating an environment that the individual can determine to keep the conflict between the tasks undertaken by individuals in their work and non-work lives to a minimum. In a study, it was found that 30% of the employees were not satisfied with their job due to long working hours, 21% due to high workload and 16% due to work-related stress. It was observed that 11% of the employees could not devote enough time to their social life (family) and therefore had difficulty in establishing the work-life balance. It is necessary to develop the most appropriate solutions with shift planning for the work success and social life of the employees. For this reason, the software developed was developed in accordance with the WLB of the employees, and programming algorithms were applied in the software with different methods.
In this study, the ACO and the PSO literature research were conducted for scheduling problems suitable for the WLB of hospital staff. Scheduling flows were created with the software developed by considering the parameters required for the algorithms.


In this study, the ant colony algorithm and the particle swarm algorithm literature research were conducted for scheduling problems suitable for the work-life balance of hospital staff. Scheduling flows were created with the software developed by considering the parameters required for the algorithms.

Keywords

Work Life Balance, Shift planning, Personnel scheduling, Algorithm

References

• A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian, 1994. “Ant system for job-shop scheduling,” Belgian J. Oper. Res., Statist. Comp. Sci. (JORBEL), vol. 34, no. 1, pp. 39–53.
• Ann. Oper. Res., 1999. “An improved ant system algorithm for the vehicle routing problem,” vol. 89, pp. 319–328.
• Bauer, B. Bullnheimer, R. F. Hartl, and C. Strauss, 2000. “Minimizing total tardiness on a single machine using ant colony optimization,” Central Eur. J. Oper. Res., vol. 8, no. 2, pp. 125–141.
• D. Costa and A. Hertz, 1997. “Ants can color graphs,” J. Oper. Res. Soc., vol. 48, pp. 295–305.
• D. E. Fyffe, W. W. Hines, and N. K. Lee, 1968. “System reliability allocation and a computational algorithm,” IEEE Trans. Rel., vol. R-17, no. 2, pp. 64–69.
• D. W. Coit and A. E. Smith, 1996. “Reliability optimization of series-parallel systems using a genetic algorithm,” IEEE Trans. Rel., vol. 45, no. 2, pp. 254–260.
• Eberhart RC, Shi Y., 2001. Particle swarm optimization: developments, applications, and resources; Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pp. 81–86.
• F. A. Tillman, C. L. Hwang, and W. Kuo, 1977. “Optimization techniques for system reliability with redundancy—a review,” IEEE Trans. Rel., vol. R-26, no. 3, pp. 148–155.
• H.I. Koruca, G. Boşgelmez; 2018. Evaluation of the Effect of Work-Life Balance and FlexibleWorking System on Employee Satisfaction; pp. 34–35.
• I. A. Wagner and A. M. Bruckstein, 1999. “Hamiltonian(t)—an ant inspired heuristic for recognizing Hamiltonian graphs,” in Proc. 1999 Congress on Evolutionary Computation, pp. 1465–1469.
• M. den Besteb, T. Stützle, and M. Dorigo, 2000. “Ant colony optimization for the total weighted tardiness problem,” in Proc. 6th Int. Conf. Parallel Problem Solving From Nature (PPSN VI), pp. 611–620.
• M. Dorigo and G. Di Caro, 1999. “The ant colony optimization metaheuristic,” in New Ideas in Optimization, pp. 11–32.
• M. Dorigo and L. M. Gambardella, 1997. “Ant colonies for the travelling salesman problem,” BioSystems, vol. 43, pp. 73–81.
• M. Dorigo, G. Di Caro, and L. M. Gambardella, 1999. “Ant algorithms for discrete optimization,” Artificial Life, vol. 5, no. 2, pp. 137–172.
• M. Dorigo, V. Maniezzo, and A. Colorni, 1996. “Ant system: optimization by a colony of cooperating agents,” IEEE Trans. Syst., Man, and Cybernetics—Part B: Cybernetics, vol. 26, no. 1, pp. 29–41.
• M. S. Chern, 1992. “On the computational complexity of reliability redundancy allocation in a series system,” Oper. Res. Lett., vol. 11, pp. 309–315.