An Automated Task Assignment System for University Lecturers
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Abstract
The aim of this research study was to develop an automated course assignment system for university lecturers using Mixed-Integer Linear Programming (MILP) with the objective of maximizing the planning score, which is derived from satisfaction scores and the total number of lecturers for all subjects. The study was conducted at the Faculty of Logistics, Burapha University, The study compared the model with the current approach over a period of two semesters. Three indicators were used to evaluate the performance: accuracy of allocation, teaching workload in credit allocation, and planning time. The results showed that the model outperformed the current approach in terms of accuracy of allocation and teaching workload in credit allocation. Specifically, it reduced workloads exceeding the minimum requirements by 2.92% and 14.78% per semester, respectively. Moreover, the model provided faster answers, with each iteration taking only 0.5-1 minute. The usability and satisfaction derived from using the model might not be explicitly clarified, but the model's results adhere to university regulations while also considering satisfaction as an important element within the objective function. Finally, this study successfully developed a model that effectively allocates tasks and reduces excessive workloads for university lecturers.
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เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสารวิชาการปทุมวันถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรง ซึ่งกองบรรณาธิการวารสารไม่จำเป็นต้องเห็นด้วยหรือร่วมรับผิดชอบใดๆ
บทความ ข้อมูล เนื้อหา ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารวิชาการปทุมวันถือเป็นลิขสิทธิ์ของวารสารวิชาการปทุมวัน หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะต้องได้รับอนุญาตเป็นลายลักษณ์อักษรจากวารสารวิชาการปทุมวันก่อนเท่านั้น
References
Bouajaja, S., & Dridi, N. (2017). A Survey on Human Resource Allocation Problems and its Applications. Operational Research, 17(2), 339-369.
Xian-Ying, M. (2012). Application of Assignment Model in PE Human Resources Allocation. Energy procedia, 16, 1720-1723.
Azimi, M., et al. (2013). Optimal Allocation of Human Resources by using Linear Programming in the Beverage Company. Universal Journal of Management and Social Sciences, 3(5), 48-54.
Güler, M. G., et al. (2015). On Teaching Assistant-Task Assignment Problem: A Case Study. Computers & Industrial Engineering, 79, 18-26.
Moreira, M. C. O., et al. (2012). Simple Heuristics for the Assembly Line Worker Assignment and Balancing Problem. Journal of heuristics, 18, 505-524.
Moreira, M. C. O., et al. (2015). Model and Heuristics for the Assembly Line Worker Integration And Balancing Problem. Computers & Operations Research, 54, 64-73.
Araujo, F. F., et al. (2015). Balancing Parallel Assembly Lines with Disabled Workers. European Journal of Industrial Engineering, 9(3), 344-365.
Gunawan, A., & Ng, K. M. (2011). Solving the Teacher Assignment Problem by Two Metaheuristics. International Journal of Information and Management Sciences, 22(1), 73-86.
Odeniyi, O. A., et al. (2015). Development of a Modified Simulated Annealing to School Timetabling Problem. International Journal of Applied Information Systems, 8(2), 16-24.
Thepphakorn, T., et al. (2014). An Ant Colony Based Timetabling Tool. International Journal of Production Economics, 149, 131-144.
Swangnop, S., & Chaovalitwongse, P. (2014). A Two-Step Tabu Search Heuristic for Multi-Period Multi-Site Assignment Problem with Joint Requirement of Multiple Resource Types. Engineering Journal, 18(3), 83-98.
Ammar, A., et al. (2012). Resolution of Multi-Skilled Workers Assignment Problem using Simulation Optimization: A Case Study. In 2012 IEEE International Conference on Computer Science and Automation Engineering (p. 748-754). 25-27 May, 2012, Zhangjiajie, China.
Fan, K., et al. (2013). An Effective Modified Binary Particle Swarm Optimization (mBPSO) Algorithm for Multi-Objective Resource Allocation Problem (MORAP). Applied Mathematics and Computation, 221, 257-267.
Fikri, M., et al. (2011). Assignment Staff with Dynamic Competencies in Multi-Projects & Multi-Periods: Modeling and Solving by a Hybridization of Ant Colony Algorithm. International Journal of Computer Science and Network Security, 11(3), 227.
Kyriklidis, C., et al. (2014). Hybrid Nature-Inspired Intelligence for the Resource Leveling Problem. Operational Research, 14, 387-407.
Costa Filho, C. F. F., et al. (2012). Using Constraint Satisfaction Problem Approach to Solve Human Resource Allocation Problems in Cooperative Health Services. Expert Systems with Applications, 39(1), 385-394.
Ağralı, S., et al. (2017). Employee Scheduling in Service Industries with Flexible Employee Availability and Demand. Omega, 66, 159-169.
Chiang, H. Y., & Lin, B. M. (2020). A Decision Model for Human Resource Allocation in Project Management of Software Development. IEEE Access, 8, 38073-38081.
Naveh, Y., et al. (2007). Workforce Optimization: Identification and Assignment of Professional Workers using Constraint Programming. IBM Journal of Research and Development, 51(3.4), 263-279.
Chen, P. S., et al. (2016). A Two-Stage Method to Determine the Allocation and Scheduling of Medical Staff in Uncertain Environments. Computers & Industrial Engineering, 99, 174-188.
Domenech, B., & Lusa, A. (2016). A MILP Model for the Teacher Assignment Problem Considering Teachers’ Preferences. European Journal of Operational Research, 249(3), 1153-1160.
Caselli, G., et al. (2022). Integer Linear Programming for the Tutor Allocation Problem: A Practical Case in a British University. Expert Systems with Applications, 187, 115967.
Mason, A. J. (2011). Opensolver-An Open Source Add-In to solve Linear and Integer Progammes in Excel. In International Conference on Operations Research (p.401-406), 30 August - 2 September, 2011, Zurich, Switzerland.