Shahid Beheshti University Journal of Industrial Management Perspective 2251-9874 2 3 2012 11 21 Scheduling Working Shifts for Multi-skilled Workforces with Genetic algorithm Approach Scheduling Working Shifts for Multi-skilled Workforces with Genetic algorithm Approach 103 124 87339 FA Ezatollah Asgharizade Associate Professor, Tehran of University. Ahmad Reza Ghasemi Assistant Professor, Tehran University. Mohammad Taghi Jafarzadeh PhD, National Petrochemical Company HSEQ Research Unit. Mohammad Sadegh Behrooz M.A, Tehran University. Journal Article 2019 10 01 Overall goal of our research is to incorporate human factors engineering into scheduling theory in order to exploit optimized human performance. Tour scheduling problem (in which full-time employees have variable performance) have been studied in this paper. Objective function of proposed staff scheduling model is minimizing staffing cost to provide efficient workforces. The unique characteristic of this study is consideration of ergonomic aspect (fatigue, learning and forgetting rate of employees) in staff scheduling problem. We used genetic algorithm to conquest difficulty of our model and to find desirable solution in a reasonable running time. In order to show effectiveness and efficiency of our algorithm we compared the results of genetic algorithm with LINGO and lower bound. The results showed that proposed model is capable to model human factors and find suitable shift schedules. Also this study showed that considered human parameters impact on workforce output and shift scheduling. Hence we recommend that managers study impacts of human factors on output of workforces and provide productive shift schedules using proposed model. Overall goal of our research is to incorporate human factors engineering into scheduling theory in order to exploit optimized human performance. Tour scheduling problem (in which full-time employees have variable performance) have been studied in this paper. Objective function of proposed staff scheduling model is minimizing staffing cost to provide efficient workforces. The unique characteristic of this study is consideration of ergonomic aspect (fatigue, learning and forgetting rate of employees) in staff scheduling problem. We used genetic algorithm to conquest difficulty of our model and to find desirable solution in a reasonable running time. In order to show effectiveness and efficiency of our algorithm we compared the results of genetic algorithm with LINGO and lower bound. The results showed that proposed model is capable to model human factors and find suitable shift schedules. Also this study showed that considered human parameters impact on workforce output and shift scheduling. Hence we recommend that managers study impacts of human factors on output of workforces and provide productive shift schedules using proposed model. https://jimp.sbu.ac.ir/article_87339_18c4c7d288810f27e8197b880e4ea460.pdf