Modeling and multi-objective optimization for energy-aware scheduling of distributed hybrid flow-shop

With the development of economic globalization and sustainable manufacturing, energy-aware scheduling of distributed manufacturing systems has become a research hot topic. However, energy-aware scheduling of distributed hybrid flow-shop is rarely explored. Thus, this paper is the first attempt to study an energy-aware distributed hybrid flow-shop scheduling problem (DHFSP). We formulate a novel mathematical model of the DHFSP with minimizing makespan and total energy consumption (TEC) criteria. A hybrid multi-objective iterated greedy (HMOIG) approach is proposed to address this energy-aware DHFSP. In this proposed HMOIG, firstly, a new energy-saving method is presented and introduced into the model for reducing TEC criterion. Secondly, an integration initialization scheme is devised to produce initial solutions with high quality. Thirdly, two properties of DHFSP are used to invent a knowledge-based local search operator. Finally, we validate the effectiveness of each improvement component of HMOIG and compare it with other well-known multi-objective evolutionary algorithms on instances and a real-world case. Experimental results manifest that HMOIG is a promising method to solve this energy-aware DHFSP.