A Knowledge-Based Cooperative Differential Evolution Algorithm for Energy-Efficient Distributed Hybrid Flow-Shop Rescheduling Problem

Due to the increasing level of customization and globalization competition, rescheduling for distributed manufacturing is receiving more attention. In meantime, environmentally friendly production becoming a force be reckoned with in intelligent industries. this paper, energy-efficient hybrid flow-shop problem (EDHFRP) addressed knowledge-based cooperative differential evolution (KCDE) algorithm proposed minimize makespan both original newly arrived orders total energy consumption (simultaneously). First, two heuristics were designed used cooperatively initialization. Next, three-dimensional knowledge base was employed record information carried out by elite individuals. A novel DE three different mutation strategies generate offspring. local intensification strategy further enhancement exploitation ability. The effects major parameters investigated extensive experiments out. numerical results prove effectiveness each specially-designed strategy, while comparisons four existing algorithms demonstrate efficiency KCDE solving EDHFRP.