Approximate Solutions for the Projects Revenues Assignment Problem
Keywords:Scheduling, Approximate solution, Heuristic, Optimization, Mathematic model
The aim of this research work is to find algorithms solving an NP-hard problem by elaborating several heuristics. This problem is to find an appropriate schedule to assign different projects, which will be expected to generate fixed revenues, to several cities. For this work, we assume that all cities have the same socio-economic and strategic characteristics. The problem is as follow. Given a set of projects which represented by its expected revenues. The objective is to distribute on several cities all projects with a minimum expected revenues gap between cities. Thus, our objective is to minimize the expected revenue gap. The suitable assignment is searching equity between cities. In this paper, we formulate mathematically the studied problem to find an approximate solutions and apply some methods to search resolution of the studied problem.
C. Boutilier and T. Lu, Budget allocation using weakly coupled, constrained Markov decision processes, in Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI-16), 52 – 61, New York (2016), https://ai.google/research/pubs/pub45291.
M. Bronfenbrenner, Income Distribution Theory, Routledge (2017), DOI: 10.4324/9780203788721.
C.-H. Chen, S. E. Chick, L. H. Lee and N. A. Pujowidianto, Ranking and selection: efficient simulation budget allocation, in Handbook of Simulation Optimization, pp. 45 – 80, Springer (2014), DOI: 10.1007/978-1-4939-1384-8_3.
Y. A. Gonczarowski and N. Nisan, Efficient empirical revenue maximization in single-parameter auction environments, in Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017, pp. 856 – 868, DOI: 10.1145/3055399.3055427.
M. Haouari and M. Jemmali, Maximizing the minimum completion time on parallel machines, 4OR 6(4) (2008), 375 – 392, DOI: 10.1007/s10288-007-0053-5.
S. Hart and N. Nisan, Approximate revenue maximization with multiple items, Journal of Economic Theory 172 (2017), 313 – 347, DOI: 10.1016/j.jet.2017.09.001.
C. Neelima and S. Sarma, Load balancing approach of genetic algorithm for balancing and equal distribution of budget asset values in finance transactions, in 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, 2017, pp. 1 – 6, DOI: 10.1109/ICECCT.2017.8117863.
R. Walter, M. Wirth and A. Lawrinenko, Improved approaches to the exact solution of the machine covering problem, Journal of Scheduling 20(2) (2017), 147 – 164, DOI: 10.1007/s10951-016-0477-x.
How to Cite
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.