Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Online Proactive Multi-Task Assignment with Resource Availability Anticipation (2310.02353v1)

Published 3 Oct 2023 in cs.MA and cs.PF

Abstract: With the emergence of services and online applications as taxi dispatching, crowdsourcing, package or food delivery, industrials and researchers are paying attention to the online multi-task assignment optimization field to quickly and efficiently met demands. In this context, this paper is interested in the multi-task assignment problem where multiple requests (e.g. tasks) arrive over time and must be dynamically matched to (mobile) agents. This optimization problem is known to be NP-hard. In order to treat this problem with a proactive mindset, we propose to use a receding-horizon approach to determine which resources (e.g. taxis, mobile agents, drones, robots) would be available within this (possibly dynamic) receding-horizon to meet the current set of requests (i.e. tasks) as good as possible. Contrarily to several works in this domain, we have chosen to make no assumption concerning future locations of requests. To achieve fast optimized online solutions in terms of costs and amount of allocated tasks, we have designed a genetic algorithm based on a fitness function integrating the traveled distance and the age of the requests. We compared our proactive multi-task assignment with resource availability anticipation approach with a classical reactive approach. The results obtained in two benchmark problems, one synthetic and another based on real data, show that our resource availability anticipation method can achieve better results in terms of costs (e.g. traveled distance) and amount of allocated tasks than reactive approaches while decreasing resources idle time.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. Saeed Alaei, MohammadTaghi Hajiaghayi & Vahid Liaghat (2012): Online Prophet-Inequality Matching with Applications to Ad Allocation. In: Proceedings of the 13th ACM Conference on Electronic Commerce, EC ’12, Association for Computing Machinery, New York, NY, USA, p. 18–35, 10.1145/2229012.2229018.
  2. Proceedings of the National Academy of Sciences 114(3), pp. 462–467, 10.1073/pnas.1611675114.
  3. Niv Buchbinder, Kamal Jain & Joseph Seffi Naor (2007): Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue. In: Proceedings of the 15th Annual European Conference on Algorithms, ESA’07, Springer-Verlag, Berlin, Heidelberg, p. 253–264, 10.1007/978-3-540-75520-324.
  4. Proceedings of the International Conference on Automated Planning and Scheduling 22(1), pp. 333–337, 10.1609/icaps.v22i1.13533.
  5. Omar Cheikhrouhou & Ines Khoufi (2021): A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy. Computer Science Review 40, p. 100369, 10.1016/j.cosrev.2021.100369.
  6. Han-Lim Choi, Luc Brunet & Jonathan P. How (2009): Consensus-Based Decentralized Auctions for Robusttask Allocation. Trans. Rob. 25(4), p. 912–926, 10.1109/TRO.2009.2022423.
  7. Rutger Claes, Tom Holvoet & Danny Weyns (2011): A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems. IEEE Transactions on Intelligent Transportation Systems 12(2), pp. 364–373, 10.1109/TITS.2011.2105867.
  8. Allegra De Filippo, Michele Lombardi & Michela Milano (2019): How to Tame Your Anticipatory Algorithm. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI’19, AAAI Press, p. 1071–1077, 10.24963/ijcai.2019/150.
  9. Proceedings of the AAAI Conference on Artificial Intelligence 32(1).
  10. Management Science 68(7), pp. 4772–4785, 10.1287/mnsc.2021.4134.
  11. CoRR abs/1912.03263, 10.48550/arXiv.1912.03263.
  12. Wesam Herbawi & Michael Weber (2011): Ant Colony vs. Genetic Multiobjective Route Planning in Dynamic Multi-Hop Ridesharing. In: Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI ’11, IEEE Computer Society, USA, p. 282–288, 10.1109/ICTAI.2011.50.
  13. Wesam Herbawi & Michael Weber (2012): The ridematching problem with time windows in dynamic ridesharing: A model and a genetic algorithm. pp. 1–8, 10.1109/CEC.2012.6253001.
  14. In Lud De Raedt, editor: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, International Joint Conferences on Artificial Intelligence Organization, pp. 1825–1833, 10.24963/ijcai.2022/254. Main Track.
  15. John H. Holland (1992): Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge, MA, USA, 10.7551/mitpress/1090.003.0009.
  16. Ahmed Hussein & Alaa Khamis (2013): Market-based approach to Multi-robot Task Allocation. pp. 69–74, 10.1109/ICBR.2013.6729278.
  17. 604, 10.1007/978-3-319-18299-52.
  18. Annu Lambora, Kunal Gupta & Kriti Chopra (2019): Genetic Algorithm- A Literature Review. pp. 380–384, 10.1109/COMITCon.2019.8862255.
  19. Nixie S Lesmana, Xuan Zhang & Xiaohui Bei (2019): Balancing Efficiency and Fairness in On-Demand Ridesourcing. 32, Curran Associates, Inc.
  20. Aranyak Mehta (2013): Online Matching and Ad Allocation. Found. Trends Theor. Comput. Sci. 8(4), p. 265–368, 10.1561/0400000057.
  21. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES ’20, Association for Computing Machinery, New York, NY, USA, p. 131, 10.1145/3375627.3375818.
  22. Nirav Patel, N. S. Narayanaswamy & Alok Joshi (2020): Hybrid Genetic Algorithm for Ridesharing with Timing Constraints: Efficiency Analysis with Real-World Data. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO ’20, Association for Computing Machinery, New York, NY, USA, p. 1159–1167, 10.1145/3377930.3389804.
  23. S. Rangriz, M. Davoodi & J. Saberian (2019): A NOVEL APPROACH TO OPTIMIZE THE RIDESHARING PROBLEM USING GENETIC ALGORITHM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18, pp. 875–878, 10.5194/isprs-archives-XLII-4-W18-875-2019.
  24. M. Schreckenberg & J. Wahle (2001): A Multi-Agent System for On-Line Simulations Based on Real-World Traffic Data. In: 2014 47th Hawaii International Conference on System Sciences, 4, IEEE Computer Society, Los Alamitos, CA, USA, p. 3037, 10.1109/HICSS.2001.926332.
  25. Proceedings of the AAAI Conference on Artificial Intelligence 36(5), pp. 5199–5207, 10.1609/aaai.v36i5.20455.
  26. Hao Wang & Xiaohui Bei (2022): Real-Time Driver-Request Assignment in Ridesourcing. Proceedings of the AAAI Conference on Artificial Intelligence 36(4), pp. 3840–3849.
  27. Yang Wang, Chenxi Zhao & Shanshan Xu (2020): Method for Spatial Crowdsourcing Task Assignment Based on Integrating of Genetic Algorithm and Ant Colony Optimization. IEEE Access 8, pp. 68311–68319, 10.1109/ACCESS.2020.2983744.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now