Dynamic job scheduling method with real-time production information for manufacturing cell

A dynamic job scheduling method is proposed, where radio frequency identification technology is adopted to collect the real-time production information related to workpieces, operations and facilities produced at the manufacturing spots; on the basis, to deal with the three types of uncertain events including new jobs arrival, facility breakdown and delivery-time change occurred in the manufacturing cell production, taking the shortest finishing time of jobs as the scheduling objective, a dynamic job scheduling mathematical model is established and solved with hybrid genetic algorithm designed by introducing hill-climbing searching method. Four evolution operators consisting of selection, crossover, mutation and hill-climbing are designed to effectively improve the convergence speed of the algorithm. A prototype system of dynamic job scheduling based on real-time production information is developed. The job scheduling case study is carried out and the results show that the proposed job scheduling method enables to deal with the dynamic job scheduling problems for uncertain events efficiently to improve the consistency between scheduling solutions and practical requirements of manufacturing cell production.