Parallel reactive tabu search for job shop scheduling problems considering peak shift of electric power energy consumption

This paper proposes parallel reactive tabu search (RTS) for job shop scheduling problems (JSPs) considering peak shift of electric power energy consumption. A JSP can be formulated as a combinatorial optimization problem. Up to now, the problem has been solved by various evolutionary computation techniques. Recently, actual production schedules are changing from the single and mass production to the various kinds of small quantity production. In order to cope with the change and for obtaining high quality solutions, parallel RTS is applied. Moreover, in recent years, greenhouse gas emission is one of the grave issues and energy reduction is a major problem all over the world. Therefore, in this paper, purpose of the JSP is not only minimization of total production time but also minimization of electric power energy consumption at the electric power energy consumption peak time. The proposed method has been applied to 6 jobs 6 machines and the results indicate that the proposed method has a possibility to apply to various kinds of small quantity production without adjusting tabu list length and get higher quality schedules.