Energy and labor aware production scheduling for industrial demand response using adaptive multi-objective memetic algorithm

Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices to reduce cost. This paper introduces a multi-objective optimization model which schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to the trade-off between energy and labor costs. An adaptive multi-objective memetic algorithm is proposed to leverage feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. Thus, adaptive balance remains between exploration of the NSGA-II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer demonstrate the effectiveness and efficiency of the algorithm. The proposed scheduling method enables intelligent production systems, where production loads and human workers are mutually matched and jointly adapted to real-time electricity pricing for cost-efficient production