### Approximate Solutions for the Projects Revenues Assignment Problem

#### Abstract

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.

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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.

DOI: http://dx.doi.org/10.26713%2Fcma.v10i3.1238

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