Research on energy-saving production planning of periodic forging resistance furnace

This paper addresses the problem of energy-saving production plan for periodic forging heating furnaces in production systems. First, the heating furnace is divided into four stages and the causes of energy loss in each stage are analyzed. Based on the energy balance of the heating furnace, a general energy consumption model is built, which takes into account the energy income of the heating furnace, energy loss and effective energy of forging heating. On this basis, the optimization model of energy consumption cost, time cost minimization and furnace tolerance minimization, in the single machine production planning process for heating furnace, are put forward. Finally, considering the use of the heating furnace, at the level of decision-making, the heating processing task production order, the idle time and the heating furnace allowed, to retain the temperature time, are determined. In order to obtain the “near” optimal solution of the problem, a heuristic genetic algorithm is used to settle the dispute. First,First, the strength Pareto evolutionary algorithm 2 (SPEA2) is used to optimize the energy and time consumption. Following, the hierarchical solution method is used to further optimize furnace tolerance, thus deriving the better solution results, which are compared to the results obtained by the other four methods. Thus, the production scheme, under the three modes of high efficiency, energy-saving and comprehensive, is obtained. This model enables the operation manager to implement the production plan in different modes, during the production shift, while the decision-maker can make the choice according to the actual situation. [Display omitted] •The working stages of the resistance furnace are determined and the respective energy loss is analyzed.•A universal energy dissipation model based on energy balance of resistance furnace is established.•A multi-objective optimization model is proposed for the single machine production planning of resistance furnace.•A heuristic genetic algorithm is used to solve this problem.