  Efficient resource allocation is challenging when privacy of users is
important. Distributed approaches have recently been used extensively to find a
solution for such problems. In this work, the efficiency of distributed AIMD
algorithm for allocation of subsidized goods is studied. First, a suitable
utility function is assigned to each user describing the amount of satisfaction
that it has from allocated resource. Then the resource allocation is defined as
a total utilitarianism problem that is an optimization problem of sum of users
utility functions subjected to capacity constraint. Recently, a stochastic
state-dependent variant of AIMD algorithm is used for allocation of common
goods among users with strictly increasing and concave utility functions. Here,
the stochastic AIMD algorithm is derandomized and its efficiency is compared
with the stochastic version. Moreover, the algorithm is improved to allocate
subsidized goods to users with concave and non-monotone utility functions as
well as users with Sigmoidal utility functions. To illustrate the effectiveness
of the proposed solutions, simulation results is presented for a public
renewable-energy powered charging station in which the electric vehicles (EV)
compete to be recharged.
