Projects Distribution Algorithms for Regional Development
Abstract This paper aims to find an efficient method to assign different projects to several regions seeking an equitable distribution of the expected revenue of projects. The solutions to this problem are discussed in this paper. This problem is NP-hard. For this work, the constraint is to suppose that all regions have the same socio-economic proprieties. Given a set of regions and a set of projects. Each project is expected to elaborate a fixed revenue. The goal of this paper is to minimize the summation of the total difference between the total revenues of each region and the minimum total revenue assigned to regions. An appropriate schedule of projects is the schedule that ensures an equitable distribution of the total revenues between regions. In this paper, we give a mathematical formulation of the objective function and propose several algorithms to solve the studied problem. An experimental result is presented to discuss the comparison between all implemented algorithms.
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Jemmali, M., & Alquhayz, H. (2020). Equity data distribution algorithms on identical routers. International Conference on Innovative Computing and Communications,
Jemmali, M., Melhim, L. K. B., & Alharbi, M. (2019a). Randomized-variants lower bounds for gas turbines aircraft engines. World Congress on Global Optimization,
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Kavoosi, M., Dulebenets, M. A., Pasha, J., Abioye, O. F., Moses, R., Sobanjo, J., & Ozguven, E. E. (2020). Development of algorithms for effective resource allocation among highway–rail grade crossings: a case study for the State of Florida. Energies, 13(6), 1419.
Khanizad, R., & Montazer, G. A. (2021). A model for optimal allocation of human resources based on the operational performance of organisational units by multi-agent systems. International Journal of Operational Research, 40(1), 32-51.
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Priya, V., Kumar, C. S., & Kannan, R. (2019). Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, 416-424.
Research Group on Innovative Development of Public Services in Beijing, I. o. M. S., Beijing Academy of Social Sciences zhaoran@ ssap. cn. (2021). More Balanced Distribution and Overall Quality Improvement of Public Services in Beijing. Analysis of the Development of Beijing, 2019, 99-126.
Rudek, R., & Heppner, I. (2020). Efficient algorithms for discrete resource allocation problems under degressively proportional constraints. Expert Systems with Applications, 149, 113293.
Tapale, M. T., Goudar, R., & Birje, M. N. (2021). Load Balancing Using Firefly Approach. In Progress in Advanced Computing and Intelligent Engineering (pp. 483-492). Springer.
Tseng, J.-H., Chen, Y.-F., & Wang, C.-L. (2020). User selection and resource allocation algorithms for multicarrier NOMA systems on downlink beamforming. IEEE Access, 8, 59211-59224.
Walter, R., Wirth, M., & Lawrinenko, A. (2017). Improved approaches to the exact solution of the machine covering problem. Journal of Scheduling, 20(2), 147-164.
Yeo, S., Naing, Y., Kim, T., & Oh, S. (2021). Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers. Electronics, 10(2), 162.
Alharbi, M., & Jemmali, M. (2020). Algorithms for Investment Project Distribution on Regions. Computational Intelligence and Neuroscience, 2020.
Alquhayz, H., Jemmali, M., & Otoom, M. M. (2020). Dispatching-rule variants algorithms for used spaces of storage supports. Discrete Dynamics in Nature and Society, 2020.
Arulkumar, V., & Bhalaji, N. (2021). Performance analysis of nature inspired load balancing algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3735-3742.
de Sena, D. C., Soares, E. F., de Paiva, I. V. L., & do Carmo, B. B. T. (2013). Queue balancing of load and expedition service in acement industry in Brazil. Independent Journal of Management & Production, 4(2), 452-462.
Ebadifard, F., & Babamir, S. M. (2018). A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment. Concurrency and Computation: Practice and Experience, 30(12), e4368.
Ehmann, M. R., Zink, E. K., Levin, A. B., Suarez, J. I., Belcher, H. M., Biddison, E. L. D., Doberman, D. J., D’Souza, K., Fine, D. M., & Garibaldi, B. T. (2021). Operational recommendations for scarce resource allocation in a public health crisis. Chest, 159(3), 1076-1083.
Gupta, A., Bhadauria, H., & Singh, A. (2020). Load balancing based hyper heuristic algorithm for cloud task scheduling. Journal of Ambient Intelligence and Humanized Computing, 1-8.
Haouari, M., & Jemmali, M. (2008). Maximizing the minimum completion time on parallel machines. 4OR, 6(4), 375-392.
Ho, G. T., Ip, W., Lee, C. K., & Mou, W. (2012). Customer grouping for better resources allocation using GA based clustering technique. Expert Systems with Applications, 39(2), 1979-1987.
Jemmali, M. (2019a). Approximate solutions for the projects revenues assignment problem. Communications in Mathematics and Applications, 10(3), 653-658.
Jemmali, M. (2019b). Budgets balancing algorithms for the projects assignment. International Journal of Advanced Computer Science and Applications (IJACSA), 10(11), 574-578.
Jemmali, M. (2021). An optimal solution for the budgets assignment problem. RAIRO: Recherche Opérationnelle, 55, 873.
Jemmali, M., & Alquhayz, H. (2020). Equity data distribution algorithms on identical routers. International Conference on Innovative Computing and Communications,
Jemmali, M., Melhim, L. K. B., & Alharbi, M. (2019a). Randomized-variants lower bounds for gas turbines aircraft engines. World Congress on Global Optimization,
Jemmali, M., Melhim, L. K. B., Alharbi, S. O. B., & Bajahzar, A. S. (2019b). Lower bounds for gas turbines aircraft engines. Communications in Mathematics and Applications, 10(3), 637-642.
Kavoosi, M., Dulebenets, M. A., Pasha, J., Abioye, O. F., Moses, R., Sobanjo, J., & Ozguven, E. E. (2020). Development of algorithms for effective resource allocation among highway–rail grade crossings: a case study for the State of Florida. Energies, 13(6), 1419.
Khanizad, R., & Montazer, G. A. (2021). A model for optimal allocation of human resources based on the operational performance of organisational units by multi-agent systems. International Journal of Operational Research, 40(1), 32-51.
Li, G., & Wu, Z. (2019). Ant colony optimization task scheduling algorithm for SWIM based on load balancing. Future Internet, 11(4), 90.
Maguluri, S. T., Srikant, R., & Ying, L. (2014). Heavy traffic optimal resource allocation algorithms for cloud computing clusters. Performance Evaluation, 81, 20-39.
Naha, R. K., Garg, S., Chan, A., & Battula, S. K. (2020). Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Future Generation Computer Systems, 104, 131-141.
Ospina López, J. P. (2019). A omputational justice model for resources distribution in Ad Hoc Networks.
Priya, V., Kumar, C. S., & Kannan, R. (2019). Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, 416-424.
Research Group on Innovative Development of Public Services in Beijing, I. o. M. S., Beijing Academy of Social Sciences zhaoran@ ssap. cn. (2021). More Balanced Distribution and Overall Quality Improvement of Public Services in Beijing. Analysis of the Development of Beijing, 2019, 99-126.
Rudek, R., & Heppner, I. (2020). Efficient algorithms for discrete resource allocation problems under degressively proportional constraints. Expert Systems with Applications, 149, 113293.
Tapale, M. T., Goudar, R., & Birje, M. N. (2021). Load Balancing Using Firefly Approach. In Progress in Advanced Computing and Intelligent Engineering (pp. 483-492). Springer.
Tseng, J.-H., Chen, Y.-F., & Wang, C.-L. (2020). User selection and resource allocation algorithms for multicarrier NOMA systems on downlink beamforming. IEEE Access, 8, 59211-59224.
Walter, R., Wirth, M., & Lawrinenko, A. (2017). Improved approaches to the exact solution of the machine covering problem. Journal of Scheduling, 20(2), 147-164.
Yeo, S., Naing, Y., Kim, T., & Oh, S. (2021). Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers. Electronics, 10(2), 162.
Jemmali, M. (2021). Projects Distribution Algorithms for Regional Development. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(3), 293–305. https://doi.org/10.14201/ADCAIJ2021103293305
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