A Review of the Multitasking Allocation Model for Vertical Farming System
Abstract
Vertical Farming System (VFS) can produce food efficiently in times of dramatic climate change, regional conflicts that affect food transportation, or space exploration. The high degree of automation in the system, a large number of sensors and robots work together, so an efficient task allocation model can significantly enhance operational efficiency, reduce energy consumption, and elevate the overall economic performance of VFS.With this background, the purpose of this review is to describe the current available multi-task allocation models applicable to VFS or related contexts. Through a literature analysis over the past 5 years, researchers have developed and analyzed numerous multi-task allocation models in the relevant fields, such as Multi-Objective Knapsack Problem (MOKP) models and Vehicle Routing Problem (VRP) models. Depending on the nature of the problem and the constraints of the environment, diverse types of multi-task allocation models have been proposed. Tasks generated within the VFS must be completed within specified time limits, and certain tasks require collaborative efforts among different robots. Taking into consideration these task requirements and drawing upon existing multi-task allocation models proposed by other researchers, this review propose the VFSRTP (VFS Robot Task Planning) model. It is expected that this work will have a positive impact on the research of VFS multi-task allocation model, and provide some model descriptions and guidelines.
References
https://mdpi-res.com/d_attachment/agronomy/agronomy-11-00667/article_deploy/agronomy-11-00667.pdf?version=1617939104. doi:10.3390/agronomy11040667
Bock, S., & Klamroth, K. (2019). Combining Traveling Salesman and Traveling Repairman Problems: A multi-objective approach based on multiple scenarios. Computers & Operations Research, 112. Retrieved from
Demir, H. B., Ozmen, E. P., & Esnaf, S. (2022). Time-Windowed Vehicle Routing Problem: Tabu Search Algorithm Approach. Adcaij-Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 179-189. Retrieved from
Denysiuk, R., Gaspar-Cunha, A., & Delbem, A. C. B. (2019). Neuroevolution for solving multiobjective knapsack problems. Expert Systems with Applications, 116, 65-77. Retrieved from
Dhiman, G., Soni, M., Pandey, H. M., Slowik, A., & Kaur, H. (2021). A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization. Engineering with Computers, 37(4), 3017-3035. Retrieved from
George, T., & Amudha, T. (2019, Apr 13-14). Genetic Algorithm Based Multi-objective Optimization Framework to Solve Traveling Salesman Problem. Paper presented at the 1st International Conference on Advancements in Computing and Management (ICACM), Jagannath Univ, Jaipur, INDIA.
Gupta, A., Saini, S., & Ieee. (2017, Dec 14-16). An Enhanced Ant Colony Optimization Algorithm for Vehicle Routing Problem with Time Windows. Paper presented at the 9th International Conference on Advanced Computing (ICoAC), Chennai, INDIA.
He, Y. C., Zhang, X. L., Li, W. B., Wang, J. H., & Li, N. (2021). An efficient binary differential evolution algorithm for the multidimensional knapsack problem. Engineering with Computers, 37(1), 745-761. Retrieved from
Hien, V. Q., Dao, T. C., & Binh, H. T. T. (2023). A greedy search based evolutionary algorithm for electric vehicle routing problem. Applied Intelligence, 53(3), 2908-2922. Retrieved from
Iori, M., de Lima, V. L., Martello, S., Miyazawa, F. K., & Monaci, M. (2021). Exact solution techniques for two-dimensional cutting and packing. European journal of operational research, 289(2), 399-415. Retrieved from
Kong, J., & Chen, C. (2020). Research Review of Green Vehicle Routing Problem. Journal of Beijing University of Posts Telecommunications, 43(3), 77-82. Retrieved from
Liu, Y. P., Xu, L. T., Han, Y. Y., Masuyama, N., Nojima, Y., Ishibuchi, H., . . . Ieee. (2021, Oct 17-20). Multi-Modal Multi-Objective Traveling Salesman Problem and its Evolutionary Optimizer. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Electr Network.
Oh, S., & Lu, C. G. (2023). Vertical farming-smart urban agriculture for enhancing resilience and sustainability in food security. Journal of Horticultural Science & Biotechnology, 98(2), 133-140. Retrieved from
Organization, W. H. (2021). The State of Food Security and Nutrition in the World 2021: Transforming food systems for food security, improved nutrition and affordable healthy diets for all (Vol. 2021): Food & Agriculture Org.
Sajid, M., Singh, J., Haidri, R. A., Prasad, M., Varadarajan, V., Kotecha, K., & Garg, D. (2021). A Novel Algorithm for Capacitated Vehicle Routing Problem for Smart Cities. Symmetry-Basel, 13(10). Retrieved from
van Delden, S. H., SharathKumar, M., Butturini, M., Graamans, L. J. A., Heuvelink, E., Kacira, M., . . . Marcelis, L. F. M. (2021). Current status and future challenges in implementing and upscaling vertical farming systems. Nature Food, 2(12), 944-956. Retrieved from
Xu, S. S., Chen, X. L., Pi, X. D., Joe-Wong, C., Zhang, P., & Noh, H. Y. (2019, Mar 04-07). Incentivizing Large-scale Vehicular Crowdsensing System For Smart City Applications. Paper presented at the Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Denver, CO.
Yeh, W. C., & Tan, S. Y. (2021). Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function. Electronics, 10(15). Retrieved from
