考虑运输时间的紧前约束下柔性作业车间调度

Flexible job shop scheduling considering tight front constraints and transportation time

针对紧前工序对机床主件制造车间生产调度的影响,结合运输时间分析紧前工序的四种表现形式,建立各形式紧前工序约束模型.引入模糊运输时间的概念,利用三角模糊数确定确切的工件运输时间,构建考虑运输时间的紧前约束下的四目标柔性作业车间调度模型,设计基于勾股模糊前景值的最优觅食算法求解该调度模型.算法中将勾股模糊数与前景理论结合,将目标函数值映射为模糊数,利用基于直角三角形形心的距离测度评估模糊信息,基于距离测度体现个体选择时的偏好,通过双理想参考模糊数前景值函数获取综合前景值判断非劣解质量,引导最优觅食算法进化.通过算例、实例测试和真实生产案例仿真验证了所提算法性能强于四个经典的多目标智能优化算法.

Four forms of pre tightening process were analyzed,and the constraint models of each pre tightening process form were established after the influence of pre tightening process and transportation time in the shop scheduling for machine main parts manufacturing workshop were added. The concept of fuzzy transportation time was introduced,and the triangular fuzzy number was used to determine the exact workpiece transportation time.A four objective flexible job shop scheduling model with the tight front constraint and the transportation time was built.The optimal foraging algorithm(OFA) based on Pythagorean fuzzy prospect value was designed to solve this scheduling model. The algorithm combined the Pythagorean fuzzy number with the prospect theory. The objective function values were mapped to the fuzzy number,and the fuzzy information was evaluated by using the distance measure based on the centroid of the right triangle. Based on the distance measures,the preferences of choosing the individuals in algorithm were embodied. The comprehensive prospect value was obtained by the double ideal reference fuzzy number prospect value function,and the quality of noninferior solutions were judged by this prospect value,meanwhile,the prospect value was used to guide the evolution of OFA.After the numerical examples,case tests and a real production case were simulated.The performances of the proposed algorithm were verified to be better than four classical multi-objective intelligent optimization algorithms.