Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Recently, with the development of mobile devices and crowdsourcing platform, spatial (SC) has become more widespread. In SC, workers need to physically travel complete spatial–temporal tasks during a certain period time. The main problem in SC platforms is scheduling set proper achieve based on different objectives. actuality, real-world applications optimize multiple objectives together, these may sometimes conflict one another. Furthermore, there lack research dealing multi-objective optimization (MOO) within an environment. Thus, this work we focused task (TS-MOO) which maximizing number completed tasks, minimizing total costs, ensuring balance workload between workers. To solve previous problem, developed new method, i.e., (MOTSO) model that consists two algorithms, namely, particle swarm (MOPSO) algorithm our fitness function Alabbadi, et al. ranking strategy entropy concept execution duration. purpose improve enhance performance MOPSO. primary goal proposed MOTSO find optimal solution We conducted experiment both synthetic real datasets; experimental results statistical analysis showed effective terms balancing