{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:35:42Z","timestamp":1762522542059,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T00:00:00Z","timestamp":1614384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004919","name":"King Abdulaziz City for Science and Technology","doi-asserted-by":"publisher","award":["1-17-00-009-0030"],"award-info":[{"award-number":["1-17-00-009-0030"]}],"id":[{"id":"10.13039\/501100004919","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2013temporal 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.<\/jats:p>","DOI":"10.3390\/a14030077","type":"journal-article","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T03:44:31Z","timestamp":1614397471000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing"],"prefix":"10.3390","volume":"14","author":[{"given":"Afra A.","family":"Alabbadi","sequence":"first","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7817-8947","authenticated-orcid":false,"given":"Maysoon F.","family":"Abulkhair","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e3789","DOI":"10.1002\/cpe.3789","article-title":"Mobile crowdsourcing: Framework, challenges, and solutions","volume":"29","author":"Wang","year":"2016","journal-title":"Concurr. Comput. Pr. Exp."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Song, S., and Tong, Y. (2016). Efficient and Load Balancing Strategy for Task Scheduling in Spatial Crowdsourcing. Web-Age Information Management, Springer.","DOI":"10.1007\/978-3-319-47121-1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tong, Y., She, J., Ding, B., Wang, L., and Chen, L. (2016, January 16\u201320). Online mobile Micro-Task Allocation in spatial crowdsourcing. Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland.","DOI":"10.1109\/ICDE.2016.7498228"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Song, T., Tong, Y., Wang, L., She, J., Yao, B., Chen, L., and Xu, K. (2017, January 19\u201322). Trichromatic Online Matching in Real-Time Spatial Crowdsourcing. Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE), San Diego, CA, USA.","DOI":"10.1109\/ICDE.2017.147"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MCOM.2016.7509386","article-title":"Spatial crowdsourcing: Current state and future directions","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kazemi, L., and Shahabi, C. (2012, January 6\u20139). GeoCrowd: Enabling Query Answering with Spatial Crowdsourcing. Proceedings of the 20th International Conference on Intelligent User Interfaces, Redondo Beach, CA, USA.","DOI":"10.1145\/2424321.2424346"},{"key":"ref_7","first-page":"14","article-title":"Spatial Crowdsourcing: Challenges and Opportunities","volume":"39","author":"Chen","year":"2016","journal-title":"IEEE Data Eng. Bull."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1109\/TKDE.2016.2550041","article-title":"Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing","volume":"28","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kazemi, L., Shahabi, C., and Chen, L. (2013, January 5\u20138). GeoTruCrowd: Trustworthy Query Answering with Spatial Crowdsourcing. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA.","DOI":"10.1145\/2525314.2525346"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deng, D., Shahabi, C., and Zhu, L. (2015, January 3\u20136). Task matching and scheduling for multiple workers in spatial crowdsourcing. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, DC, USA.","DOI":"10.1145\/2820783.2820831"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, D., Shahabi, C., and Demiryurek, U. (2013, January 5\u20138). Maximizing the number of worker\u2019s self-selected tasks in spatial crowdsourcing. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA.","DOI":"10.1145\/2525314.2525370"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3078853","article-title":"A Real-Time Framework for Task Assignment in Hyperlocal Spatial Crowdsourcing","volume":"9","author":"Tran","year":"2018","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1016\/j.cor.2013.06.012","article-title":"Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm","volume":"40","author":"Tsai","year":"2013","journal-title":"Comput. Oper. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1007\/978-3-642-38703-6_24","article-title":"Deadline Constrained Task Scheduling Based on Standard-PSO in a Hybrid Cloud","volume":"Volume 7928","author":"Tan","year":"2013","journal-title":"Advances in Swarm Intelligence"},{"key":"ref_15","first-page":"525","article-title":"A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment","volume":"Volume 742","author":"Ray","year":"2019","journal-title":"Soft Computing: Theories and Applications"},{"key":"ref_16","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: An overview","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Comput."},{"key":"ref_18","unstructured":"Coello, C.C., and Lechuga, M. (2003, January 12\u201317). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, 2002. CEC \u201902, Honolulu, HI, USA."},{"key":"ref_19","first-page":"45","article-title":"Task-Scheduling Based on Multi-Objective Particle Swarm Optimization in Spatial Crowdsourcing","volume":"8","author":"Alabbadi","year":"2019","journal-title":"J. King Abdulaziz Univ. Comput. Inf. Technol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.14778\/2733004.2733047","article-title":"gMission: A General Spatial Crowdsourcing Platform","volume":"7","author":"Chen","year":"2014","journal-title":"Proc. VLDB Endow."},{"key":"ref_21","unstructured":"(2018, November 22). Uber. Available online: https:\/\/www.uber.com\/\/."},{"key":"ref_22","unstructured":"(2018, November 22). Google Maps. Available online: https:\/\/www.google.com\/maps."},{"key":"ref_23","unstructured":"(2018, November 22). Free Driving Directions, Traffic Reports & GPS Navigation App by Waze. Available online: https:\/\/www.waze.com\/."},{"key":"ref_24","unstructured":"(2018, November 22). Restaurants, Dentists, Bars, Beauty Salons, Doctors\u2014Yelp. Available online: https:\/\/www.yelp.com\/."},{"key":"ref_25","unstructured":"(2018, November 22). TaskRabbit Connects You to Safe and Reliable Help in Your Neighborhood. Available online: https:\/\/www.taskrabbit.com\/."},{"key":"ref_26","unstructured":"(2018, November 22). Gigwalk: We\u2019ve Got Your Brand\u2019s Back\u2014Gigwalk. Available online: http:\/\/www.gigwalk.com\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"To, H. (2016, January 31). Task Assignment in Spatial Crowdsourcing: Challenges and Approaches. Proceedings of the 3rd ACM SIGSPATIAL PhD Symposium, Burlingame, CA, USA.","DOI":"10.1145\/3003819.3003820"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cheng, P., Jian, X., and Chen, L. (2016). Task Assignment on Spatial Crowdsourcing (Technical Report). arXiv.","DOI":"10.1109\/ICDE.2017.146"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3291933","article-title":"A Survey of Spatial Crowdsourcing","volume":"44","author":"Gummidi","year":"2019","journal-title":"ACM Trans. Database Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00778-019-00568-7","article-title":"Spatial crowdsourcing: A survey","volume":"29","author":"Tong","year":"2019","journal-title":"VLDB J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2729713","article-title":"A Server-Assigned Spatial Crowdsourcing Framework","volume":"1","author":"To","year":"2015","journal-title":"ACM Trans. Spat. Algorithms Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"To, H., Fan, L., Tran, L., and Shahabi, C. (2016, January 14\u201319). Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, NSW, Australia.","DOI":"10.1109\/PERCOM.2016.7456507"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alfarrarjeh, A., Emrich, T., and Shahabi, C. (2015, January 15\u201318). Scalable Spatial Crowdsourcing: A Study of Distributed Algorithms. Proceedings of the 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, USA.","DOI":"10.1109\/MDM.2015.55"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.eswa.2016.03.022","article-title":"Efficient task assignment for spatial crowdsourcing: A combinatorial fractional optimization approach with semi-bandit learning","volume":"58","author":"Hassan","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1109\/TMC.2017.2771259","article-title":"Multi-Objective Optimization Based Allocation of Heterogeneous Spatial Crowdsourcing Tasks","volume":"17","author":"Wang","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_36","unstructured":"(2017, January 17). SNAP: Network Datasets: Gowalla. Available online: http:\/\/snap.stanford.edu\/data\/loc-gowalla.html."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/3\/77\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:29:44Z","timestamp":1760160584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/3\/77"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,27]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["a14030077"],"URL":"https:\/\/doi.org\/10.3390\/a14030077","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,2,27]]}}}