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.

Efficient task assignment for spatial crowdsourcing

Abstract: We introduce min-cost max-reliability assignment problem in spatial crowdsourcing.We propose a novel distance-reliability ratio approach to address the problem.We extend the proposed approach for dynamic estimation of worker reliabilities.We present the performance of algorithms on synthetic and real-world datasets.The proposed approach achieves lower travel costs while maximizing the reliability. Spatial crowdsourcing has emerged as a new paradigm for solving problems in the physical world with the help of human workers. A major challenge in spatial crowdsourcing is to assign reliable workers to nearby tasks. The goal of such task assignment process is to maximize the task completion in the face of uncertainty. This process is further complicated when tasks arrivals are dynamic and worker reliability is unknown. Recent research proposals have tried to address the challenge of dynamic task assignment. Yet the majority of the proposals do not consider the dynamism of tasks and workers. They also make the unrealistic assumptions of known deterministic or probabilistic workers' reliabilities. In this paper, we propose a novel approach for dynamic task assignment in spatial crowdsourcing. The proposed approach combines bi-objective optimization with combinatorial multi-armed bandits. We formulate an online optimization problem to maximize task reliability and minimize travel costs in spatial crowdsourcing. We propose the distance-reliability ratio (DRR) algorithm based on a combinatorial fractional programming approach. The DRR algorithm reduces travel costs by 80% while maximizing reliability when compared to existing algorithms. We extend the DRR algorithm for the scenario when worker reliabilities are unknown. We propose a novel algorithm (DRR-UCB) that uses an interval estimation heuristic to approximate worker reliabilities. Experimental results demonstrate that the DRR-UCB achieves high reliability in the face of uncertainty. The proposed approach is particularly suited for real-life dynamic spatial crowdsourcing scenarios. This approach is generalizable to the similar problems in other areas in expert systems. First, it encompasses online assignment problems when the objective function is a ratio of two linear functions. Second, it considers situations when intelligent and repeated assignment decisions are needed under uncertainty.

Stochastic optimization models for the airport gate assignment problem

Abstract: Uncertainties inherent in the airport traffic may lead to the unavailability of gates for accommodating scheduled flights. Incorporating random disruptions is crucial in constructing effective flight-gate assignments. We consider the gate assignment problem under uncertainty in flight arrival and departure times and develop stochastic programming models incorporating robustness measures based on the number of conflicting flights, idle and buffer times. The proposed models are formulated as large-scale mixed-integer programming problems and tabu search algorithms are implemented to obtain assignments of reasonable quality. We conduct a computational study to analyze the proposed alternate models and show the effectiveness of the solution methods.

UAV cooperative multiple task assignments using genetic algorithms

Abstract: A multiple task assignment problem for cooperating uninhabited aerial vehicles is posed as a combinatorial optimization problem. A genetic algorithm for assigning the multiple agents to perform multiple tasks on multiple targets is proposed. The algorithm allows efficiently solving this NP-hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows taking into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and flyable trajectories. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm, providing good feasible solutions quickly. Moreover, it converges near to the optimal solution considerably faster than the other methods for some test cases. This makes real-time implementation for high dimensional problems feasible.

Networks and Algorithms: An Introductory Approach

Abstract: Flows in Basic Networks Variations on the Basic Flow Problem Multi-Terminal Flows Paths and Connectivity Longest and Shortest Path Algorithms Trees Physical Networks: Modelling Electrical Networks Matrix Equations Electrical Networks: Solving the Network Equations Matching Problems The Assignment Problem The Transportation Problem Critical Path Analysis Scheduling Packing Problems Location Problems Theory of Network Analysis Algorithms and NP-Completeness Solutions to Problems in the Text