An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performanc...The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performance of manufacturing systems.However,only a few studies have specifically addressed energy-efficient scheduling for remanufacturing.Considering the uncertain processing time and routes and the operation characteristics of remanufacturing,we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing.An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines,batch machines,and uncertain processing routes and time.The algorithm demonstrated superior performance in terms of optimal value,run time,and convergent generation in comparison with other algorithms.Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size.In addition,the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%.This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.展开更多
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
基金The authors highly appreciate the investigation opportunities provided by SINOTRUK,Jinan Fuqiang Power Co.,Ltd.We are also grateful for the financial support from the National Natural Science Foundation of China(Grant Nos.51775086 and 51605169)Natural Science Foundation of Guangdong Province China(Grant No.2014A030310345).
文摘The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process,and scheduling could exert significant effects on the energy performance of manufacturing systems.However,only a few studies have specifically addressed energy-efficient scheduling for remanufacturing.Considering the uncertain processing time and routes and the operation characteristics of remanufacturing,we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing.An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines,batch machines,and uncertain processing routes and time.The algorithm demonstrated superior performance in terms of optimal value,run time,and convergent generation in comparison with other algorithms.Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size.In addition,the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%.This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.
