workers scheduling in production logistics in a job shop production system

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.