制造系统分布式绿色调度技术综述OA

Distributed green manufacturing combines the advantages of distributed manufacturing and green manufacturing.It can reduce production cost and risk by division of work and cooperation among factories,while promoting sustainable development of enterprises through energy saving,emission reduction,and resource recycling.Distributed green scheduling is an important part of distributed green manufacturing and is directly linked to multiple goals,including cost reduction,efficiency improvement,energy saving,consumption reduction,and emission reduction.In recent years,distributed green scheduling has attracted growing research interest.Previous studies,primarily focused on reducing energy consumption,improving production efficiency,and achieving multi-objective coordinated optimization in distributed manufacturing environments,have yielded substantial outcomes.This paper presents a systematic review and analysis of the current research status on green scheduling for distributed flow shops,distributed hybrid flow shops,and distributed flexible job shops,together with optimization techniques.Emerging characteristics of existing studies are summarized,including the incorporation of a wide range of real-world operating conditions,the integration of problem-specific knowledge with energy-saving strategies,and the introduction of learning mechanisms.Meanwhile,the limitations of current research are analyzed,such as relatively limited green objective dimensions,a predominant focus on static scheduling,and insufficient application to real-world manufacturing systems.Finally,future research directions are discussed from both the problem and methodological perspectives.On the problem side,further studies are expected to pay more attention to coordinated modeling of multiple green objectives,distributed green dynamic scheduling,and scheduling problems in real-world manufacturing systems.On the methodological side,further exploration is expected on the deep integration of intelligent optimization algorithms with problem-specific knowledge,the effective synergy between learning mechanisms and intelligent algorithms,and data-driven modeling and optimization for distributed green scheduling methods.