Assignment problem

About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.

On earliest deadline first scheduling for temporal consistency maintenance

Abstract: A real-time object is one whose state may become invalid with the passage of time. A temporal validity interval is associated with the object state, and the real-time object is temporally consistent if its temporal validity interval has not expired. Clearly, the problem of maintaining temporal consistency of data is motivated by the need for a real-time system to track its environment correctly. Hence, sensor transactions must be able to execute periodically and also each instance of a transaction should perform the relevant data update before its deadline. Unfortunately, the period and deadline assignment problem for periodic sensor transactions has not received the attention that it deserves. An exception is the More-Less scheme, which uses the Deadline Monotonic (DM) algorithm for scheduling periodic sensor transactions. However, there is no work addressing this problem from the perspective of dynamic priority scheduling. In this paper, we examine the problem of temporal consistency maintenance using the Earliest Deadline First (EDF) algorithm in three steps: First, the problem is transformed to another problem with a sufficient (but not necessary) condition for feasibly assigning periods and deadlines. An optimal solution for the problem can be found in linear time, and the resulting processor utilization is characterized and compared to a traditional approach. Second, an algorithm to search for the optimal periods and deadlines is proposed. The problem can be solved for sensor transactions that require any arbitrary deadlines. However, the optimal algorithm does not scale well when the problem size increases. Hence, thirdly, we propose a heuristic search-based algorithm that is more efficient than the optimal algorithm and is capable of finding a solution if one exists.

Particle Swarm Optimization for Resource Allocation in UAV Cooperative Control

Abstract: Multiple Unmanned Aerospace Vehicle (UAV) resource allocation is one of the core steps to effectively exploit the capabilities of cooperative control of UAV teams. Although many multi -UAV resource allocation models can be found in the literature, relatively little has been said regarding soft weapon systems such as de coys and jammers, designed make the threat ineffective. In this paper, we formulate the resource allocation problem as an optimal assignment problem and present a new algorithm that is based on the principles of Particle Swarm Optimization (PSO). PSO follo ws a collaborative population based search, which models the social behavior of bird flocks and fish schools. PSO combines local search methods with global search, attempting to balance exploration and exploitation. We discuss the adaptation and implementa tion of the PSO search strategy to the resource allocation problem in the cooperative control of multiple UAVs. Simulation results indicate that the PSO based algorithm is a feasible approach for resource allocation problems in multiple UAV teams.

A Discrete Global Minimization Algorithm for Continuous Variational Problems

Abstract: In this paper, we apply the ideas from combinatorial optimization to find globally optimal solutions to continuous variational problems. At the heart of our method is an algorithm to solve for globally optimal discrete minimal surfaces. This discrete surface problem is a natural generalization of the planar-graph shortest path problem.

An adaptive large neighborhood search heuristic for solving a robust gate assignment problem

Abstract: Robust gate assignment problem with transfer passengers and tows is considered.A large neighborhood search heuristic is proposed to solve the integrated problem.Extensive experiments are conducted to justify the performance of our approach. With the rapid growth of air traffic demand, airport capacity becomes a major bottleneck within the air traffic control systems. Minor disturbances may have a large impact on the airport surface operations due to the overly tight schedules, which results in frequent gate conflict occurrences during airports daily operations. A robust gate schedule that is resilient to disturbances is essential for an airport to maintain a good performance. Unfortunately, there is no efficient expert system available for the airport managers to simultaneously consider the traditional cost (the aircraft tow cost, transfer passenger cost) and the robustness. To fill this gap, in this paper, we extend the traditional gate assignment problem and consider a wider scope, in which the traditional costs and the robustness are simultaneously considered. A mathematical model is first built, which leads to a complex non-linear model. To efficiently solve this model, an adaptive large neighborhood search (ALNS) algorithm is then designed. We novelly propose multiple local search operators by exploring the characteristics of the gate assignment problem. The comparison with the benchmark algorithm shows the competitiveness of proposed algorithm in solving the considered problem. Moreover, the proposed methodology also has great potential from the practical perspective since it can be easily integrated into current expert systems to help airport managers make satisfactory decisions.