Abstract
Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.
Highlights
Crowdsourcing is an outsourcing platform that facilitates the achievement of timeconsuming and costly tasks
The main procedure of our model is illustrated in Figure 3; the model starts with the ranking strategy algorithm, utilizing the multi-objective particle swarm optimization (MOPSO) algorithm
In order to unify the direction of all objectives in terms of “minimizing”, in Table 9 we display the results of 1 − NV, which is the number of uncompleted tasks, as well as the normalized total travel costs (TTCs) (NTTCs) and the normalized workload balance (NWLB)
Summary
Crowdsourcing is an outsourcing platform that facilitates the achievement of timeconsuming and costly tasks. 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 Other studies such as [8,31] associated each worker to a task using a skill score under different constraints. 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. 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 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. In this work, we highlight the task scheduling problem based on MOO, and we present some of the most common studies focused on task scheduling in SC
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