Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

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.     

基于神经内分泌调控机制的类生物化制造系统研究

从制造系统、生物系统、控制系统学科交叉的角度出发,借鉴生物有机系统的神经-体液调控机制及规律,提出了一种新型的类生物化制造系统体系结构,研究如何将生物有机体的神经内分泌激素调控机制借用到制造系统调度规划与管理中。首先提出了有机制造单元的概念,然后建立了基于有机制造单元的具有多主体协调与递归控制特点的类生物化制造系统协调模型。基于神经内分泌激素调节规律,建立了类生物化制造系统的多重反馈控制模型,实现车间层在干扰环境下的快速自适应调度与控制。