# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 2 Related Work
### Task Matching in SC  
Some studies have focused on an SC task matching technique to solve diverse objectives. For instance, [PERSON] et al. [6] presented a framework in the SAT mode, and they assumed all workers are reliable in SC. The main objective of [6] was to solve the maximum  
Figure 2: A spatial crowdsourcing (SC) scenario of the multi-objective task scheduling optimization (MOTSO) model. task assignment (MTA). The researchers formulated MTA as a matching problem, and then they tried to solve MTA by reducing the maximum flow problem. In more detail, the worker's reliability was integrated into the framework mentioned in [6] by [PERSON] et al. [9]. They tried to maximize the number of assigned tasks that require many confident workers to achieve these tasks [9]. They focused on the maximum correct task assignment (MCTA) problem and tried to solve it by proposing three approaches based on the 3D matching technique. Furthermore, other studies such as [8; 31] associated each worker to a task using a skill score under different constraints. [PERSON] et al. [31] extended the framework of [6] by considering the expertise score of a worker and the maximum score assignment (MSA). For instance, when online workers are ready, they send their inquiry--which includes their region, capacity, and expertise--to the SC server. Then, the SC server matches each task to one worker under satisfying constraints. This framework proposes three heuristic algorithms based on MWBM that aim to maximize the score of the assignment task. In [8], multi-skill SC (MS-SC) was considered as an approach for finding suitable workers under multi-skill task, budget, and time/distance constraints with the maximum score. Then, three approximate approaches were proposed, i.e., the greedy-based matching, greedy divide-and-conquer (g-D&C), and cost model-based adaptive algorithms. Real-time matching is considered in SC as mentioned in [3; 4; 12; 32]. For instance, maximizing the total utility score is the main objective of both [3; 4]. The authors in [3] addressed the global online micro-task allocation problem in SC. They proposed the two-phase global online allocation algorithm (TGOA) to solve the proposed problem, by dividing a set of tasks and workers into groups based on their order of arrival order. The first group was allocated a greedy assignment to find the highest utility value between the workers and the tasks, while the second group adopted an optimal strategy to find the optimal match between the workers and the tasks. Then, the authors proposed the greedy TGOA algorithm to improve the efficiency and scalability of TGOA. Meanwhile, [PERSON] et al. [4] introduced a trichromatic online matching problem in real-time SC. They considered three sides or objects, i.e., task, worker, and workplace, during the assignment. They reduced the problem to the 3D matching problem and then solved it by introducing the greedy and threshold-based randomized algorithm to improve the performance of the greedy algorithm in terms of maximizing the utility score. This model is suitable only for SC applications that require a workplace. Moreover, the real-time framework proposed in [32] can solve the hyper-local SC problem, where the task is assigned to the closest worker within a circular region. The main challenge in [32] was to maximize the assigned task under budget constraints. The authors investigated the problem under two variants; the first variant was a fixed budget, under which the authors proposed two heuristic algorithms. The second variant was a dynamic budget, under which they proposed two algorithms to solve the problem. Factually, the hyper-local approach discussed in [32] means the worker might not need to travel to complete a spatial task, which is not applicable to most SC applications such as home delivery services and Uber. Therefore, we did not focus on the hyper-local SC in our study.