研究了航班地面等待模型中延误成本的计算问题。建立了单元受限地面等待问题的数学模型,分析了航班延误成本的构成,给出了航班延误显性成本的计算方法,并将其应用于数学模型中目标函数的计算。最后,用分段排序和定步长排序对模型进行了...研究了航班地面等待模型中延误成本的计算问题。建立了单元受限地面等待问题的数学模型,分析了航班延误成本的构成,给出了航班延误显性成本的计算方法,并将其应用于数学模型中目标函数的计算。最后,用分段排序和定步长排序对模型进行了仿真,并与先到先服务(F irst com e,first served,FCFS)排序进行了比较。仿真结果表明,对航班延误成本进行定量分析,能够更有效地控制航班延误的地面等待成本。展开更多
This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining...This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.展开更多
文摘研究了航班地面等待模型中延误成本的计算问题。建立了单元受限地面等待问题的数学模型,分析了航班延误成本的构成,给出了航班延误显性成本的计算方法,并将其应用于数学模型中目标函数的计算。最后,用分段排序和定步长排序对模型进行了仿真,并与先到先服务(F irst com e,first served,FCFS)排序进行了比较。仿真结果表明,对航班延误成本进行定量分析,能够更有效地控制航班延误的地面等待成本。
文摘This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.
