Joint Capacity Allocation and Job Assignment Under Uncertainty
提出一种状态依赖的分配决策规则，用于多周期随机资源分配中同时决定容量分配和任务指派，在仿真中比流体近似和近似动态规划方法改进1%至15%。
We propose a state-dependent “distributive decision rule” for simultaneous capacity allocation and job assignment decisions in a multiperiod stochastic resource allocation context with random supply replenishment, random demand, job waiting and abandonment. Our decision rule can be reformulated into a convex optimization problem with polynomial number of constraints and decision variables. Our framework can be applied in many service management settings such as ride-sharing fleet repositioning and patient management in healthcare. In simulations, our framework records 1%–15% improvements over alternative paradigms such as fluid approximations and approximate dynamic programming.