| Manufacturing industry constitutes a crucial part in promoting national economic growth and driving social progress,and serves as an important industry that reflects a country’s innovation capacity,competitiveness,and comprehensive strength.In 2015,the Chinese government put forward the “Made in China 2025” action plan,which provides significant guidance and support for the development of the manufacturing industry.In recent years,China’s manufacturers have continuously expanded their scale,achieving a series of remarkable accomplishments.However,as globalization advances,the manufacturing industry faces increasingly intense market competition.Consequently,China’s manufacturing industry must consistently undergo reforms and innovations to keep pace with the global economic development.Production scheduling is an effective management method in the manufacturing industry which directly influences a manufacturing enterprise’s production efficiency and core competitiveness.In the growingly competitive market environment,building rational production scheduling models and developing efficient optimization methods has become an important way for many enterprises to improve production efficiency and quality,and reduce operating costs and risks.Therefore,studying the optimization problems of production scheduling has significant practical significance.Distributed manufacturing is an intelligent production pattern based on modern information technology.Its emergence is to address many challenges and problems faced by the traditional centralized production pattern.With the rapid development of global manufacturing industry and increasing competition,manufacturing enterprises need to adopt more efficient and flexible production patterns to satisfy the changing market needs.Distributed manufacturing provides a new solution that can effectively integrate and coordinate resources located in different areas,e.g.,raw materials,machines,and workers,and fully utilize them to help enterprises improve their core competitiveness.Hence,distributed manufacturing has become one of the important trends for the transformation and upgrading of the manufacturing industry.Besides,equipment maintenance is a core content of the operational management of a manufacturing company and is directly related to production costs,quality,and delivery times.Therefore,production scheduling optimization considering equipment maintenance has been widely concerned in recent years.Preventive maintenance is a method of protecting equipment from malfunctions through taking measures such as regular inspection,maintenance,and replacement of parts even before any problems occur.This maintenance method can greatly reduce the probability of sudden machine failures and downtime,improve the reliability and stability of equipment,and thus improve the operational efficiency of manufacturing enterprises.Based on the above background,this work proposes a distributed flexible job shop scheduling problem considering preventive maintenance,and its contents mainly include:(1)This work studies a single-objective distributed flexible job shop scheduling problem considering machine preventive maintenance,and formulates a mixed integer mathematical programming model with the objective of minimizing makespan.Since the investigated problem has NP-hard nature,a coevolutionary algorithm is proposed to handle it.Numerical experiments are conducted with two classical meta-heuristic algorithms on different test instances,and the experimental results demonstrate that the proposed algorithm can effectively solve the problem.(2)This work presents a multi-objective distributed flexible job shop scheduling problem considering machine preventive maintenance.To clearly define the problem,a mixed integer mathematical programming model with minimizing makespan and maximum machine load is built.A multi-objective artificial bee colony algorithm is designed to tackle the problem.To verify the effectiveness of the proposed approach,two well-known meta-heuristic algorithms are selected as comparison methods,and numerical experiments are performed on a group of test instances.The results suggest that the proposed method exhibits outstanding performance in handling the problem. |
