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