Budget-aware online task assignment in spatial crowdsourcing

Abstract

The prevalence of mobile internet techniques stimulates the emergence of various spatial crowdsourcing applications. Certain of the applications serve for requesters, budget providers, who submit a batch of tasks and a fixed budget to platform with the desire to search suitable workers to complete the tasks in maximum quantity. Platform lays stress on optimizing assignment strategies on seeking less budget-consumed worker-task pairs to meet requesters' demands. Existing research on the task assignment with budget constraint mostly focuses on static offline scenarios, where the spatiotemporal information of all workers and tasks is known in advance. However, workers usually appear dynamically on real spatial crowdsourcing platforms, where existing solutions can hardly handle it. In this paper, we formally define a novel problem Budget-aware Online task Assignment(BOA) in spatial crowdsourcing applications. BOA aims to maximize the number of assigned workertask pairs under a budget constraint where workers appear dynamically on platforms. To address the BOA problem, we first propose an efficient threshold-based greedy algorithm Greedy-RT which utilizes a random generated threshold to prune the pairs with large travel cost. Greedy-RT performs well in adversary model when compared with simple greedy algorithm, but it is unstable in random model for its randomly generated threshold may produce poor quality in matching size. We then propose a revised algorithm Greedy-OT which could learn approximately optimal threshold from historical data, and consequently improves matching size significantly in both models. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Research on Task Allocation Model of Takeaway Platform Based on RWS-ACO Optimization Algorithm

“…Experiments to verify that the algorithm has advantages; Wang et al [5] proposed to transform the combinatorial optimization problem into a task allocation function with load capacity and cooperation constraints, solve by the algorithm and verify effectiveness by experiment; Pan et al [6] proposed to add an adaptive threshold algorithm based on the GH algorithm, and experiments to verify that it has lower cost and higher utility; Chen et al [7] proposed a dynamic grouping allocation method to solve the conflicts generated in a dynamic environment, behaved well; Fu et al [8] proposed a two-stage allocation model to try to distribute tasks fairly and minimize costs, experiments to verify F-Aware is superior than others; Liang et al [9] proposed TWD and design a two-stage competition and cooperation model: competition optimization and negotiation and cooperation, maximizes personal income and gets the best solution. Miao et al [10] proposed an adaptive ant colony algorithm to solve the problem of local optimal solution, through experimental verification, global optimization can be achieved; Xia et al [11] proposed an improved tabu search algorithm to solve a planning model that takes the number of vehicles and cost as dual goals; Liu et al [12] used the greeedy-ot algorithm with optimized threshold to solve the task assignment problem; Song et al [13] adopted an online greedy algorithm to solve the task assignment problem of spatial crowdsourcing, realize distribution with the lowest cost; Li et al [14] adopted a bilateral online matching method to match workers with tasks.…”

Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization

“…Step 10: update the pheromone concentration of the first ant in the sub group, and the pheromone volatilization coefficient ρ(n) is adaptive (14).…”

“…A task assignment scheme based on bilinear matching and encryption privacy awareness had been proposed [13]. Liu and Xu [14] researched the problem of online task assignment under perceived budget in spatial crowdsourcing, aiming to maximize the number of task assignment under budget constraints (workers appear on the platform dynamically), and designed an improved threshold-based greedy algorithm (Greedy-OT), which learns results close to the optimal threshold from historical data. Xingsheng et al [15] proposed an online threshold algorithm based on assignment time to solve the problem of poor assignment effect caused by single consideration of the total effect of task assignment or task effective time in the existing research.…”

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

“…A fundamental problem in spatial crowdsourcing is task assignment. Most of the existing works on task assignment [1][2][3][4][5][6][7] mainly assume that all the tasks are simple, and can be easily completed by a single worker such as delivering packages, taking photos, or reporting hot spots. However, an individual worker cannot complete some complex tasks, e.g., preparing for a party, decorating houses, and general cleaning.…”