Study On Hybrid Energy-efficient Scheduling Measures For Dual-flexible Job Shop With Variable Machining Speeds

Production scheduling,as a scientific decision-making tool,plays a crucial role in manufacturing industry.It is the key for companies to coordinate resource allocations,ensure production operations and improve production efficiency.However,as the manufacturing focuses on the quality improvement,energy-saving and emission reduction become an essential requirement for the industry’s future development.For this reason,enterprises have to take resource utilization and environmental impact into consideration,with production efficiency together for the scheduling optimization.As a typical organization of production shops,flexible job shop scheduling has been richly researched in terms of its modeling and solving.However,the existing literature have not discussed the dual-flexible processing capability of machines and the auxiliary operations related to jobs and machines setting at the same time,and there is still room for progress on the research of hybrid energy-saving scheduling measures.In this regard,this paper considers both multi-tasking and multi-speed processing capability of machines to study the green scheduling problem of the dual-flexible job shop,in which the auxiliary settings for jobs to be executed on machines and the energy consumption model on dualflexible processing machines are also considered.The main content is as follows.Firstly,based on the multi-task and multi-speed machining capability,an energy consumption model is proposed for the dual-flexible machines containing three states(processing,no-load,and standby)and two operations(shifting speeds up/down,turning machines off/on),and a set of suggestions are given to generate machines’ power consumption and energy consumption data.Meanwhile,a bi-objective scheduling optimization model including maximum completion time and total energy consumption is developed by taking the production-assisted auxiliary operations into account.Then,in order to solve this problem,a clever encoding and decoding mode is designed by combining the problem characteristics,as also the encoding operation operator so that it can be flexibly applied on various evolutionary algorithms.At the same time,a heuristic operator is improved to optimize maximum completion time so that the solutions set can be as close to the frontier on the objective as possible,and the hybrid energy-efficient measures is introduced to reduce total energy consumption on dualflexible processing machines under different conditions.Finally,the problem is solved on a multi-objective optimization platform with three algorithms,e-MOEA,MOEA/D,and NSGA-II.The results show that different algorithms can complement each other in terms of convergence and diversity,to capture a widely distributed set of historical solutions and a well-pleasing set of Pareto solutions.As for this problem,maximum completion time and total energy consumption can be optimized simultaneously and converge consistently on the global trend,and the hybrid energy-efficient measures can achieve maximum energy-savings by reducing energy consumptions on different states,further verifying its feasibility and effectiveness.