On the Flexible Demand Assignment Problems: Case of Unmanned Aerial Vehicles

Abstract

Multiresource generalized assignment problem (MRGAP) has enormous applications in solving real problems of industries. In recent years, several generalizations of MRGAP have been proposed to tackle very difficult problems. An important generalization is called flexible demand assignment (FDA) problem. In this paper, a generalization of FDA is proposed that has many applications. Two features of our formulation are inclusion of: 1) acceptance of orders from a large set of available orders and 2) consideration of setup time between operations of two consecutive of tasks. We show an interesting application of generalized FDA is unmanned aerial vehicle (UAV) assignment problem. For the UAV assignment problem, we show our formulation considerably reduces the size of the problem compared to some recent results. To test effectiveness of the proposed model, computational experiment with CPLEX for the UAV assignment problem is presented.Note to Practitioners-FDA is an important generalization of multiresource generalized assignment problem, where customers are flexible in their order size. Manufacturers need to determine the optimal size for each item for maximizing the profit. Here, we consider FDA with addition of: 1) choosing a set of orders from a large set of available orders and 2) setup time between operations of two consecutive orders. As a special case, we formulate the assignment of UAVs as an FDA. The formulation significantly reduces the size of the problem compared to some recent published results. It allows more efficient way to solve the problems.Index Terms-Flexible demand assignment (FDA) problem, unmanned aerial vehicles (UAVs).

The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

“…TA with flexible demand (Alidaee et al, 2011), real-time and dynamic assignment (Kim et al, 2007;Lin et al, 2013), time-dependent TA (Kingston & Schumacher, 2005), and finally, TA under uncertainty (Alighanbari & How, 2008;Hu et al, 2015a). However, the UAVTAP differs from the VRP by allowing, for example, multiple visits and subtours.…”

On the concerted design and scheduling of multiple resources for persistent UAV operations

“…They consider payload, maximum range and service level. Alidaee et al ( [13]) improved the tractability of the MILP from [12] used CPLEX for computational comparisons. Persistent operations using service stations are investigated in [3,[14][15].…”

“…Constraints (11)-(12) ensure that every split job in Ω J is served at its pre-determined start time. Constraint (13) forces UAVs to return to a station before its fuel is depleted. Constraint (14) ensures that only selected (purchased) UAVs serve split jobs.…”

A Coalition Formation Game Approach for Efficient Cooperative Multi-UAV Deployment

“…Authors in [8] considered the information consensus under multi-UAVs communication noise, and constructed it as a signal-to-noise ratio model for UAV cooperative target tracking. The authors in [9] proposed a cooperative detection model which consisted of target identification and time control.…”