Abstract Motivated by batteryless IoT devices, we consider the following scheduling problem. The input includes n unit time jobs $$\mathcal{J}= \left\{ J_1, \ldots, J_n \right\} $$ , where each job $$J_i$$ has a release time $$r_i$$ , due date $$d_i$$ , energy requirement $$e_i$$ , and weight $$w_i$$ . We consider time to be slotted; hence, all time related job values refer to slots. Let $$T=\max _i\left\{ d_i \right\} $$ . The input also includes an h ( t ) value for every time slot t $$\left( 1 \le t \le T \right) $$ , which is the energy harvestable on that slot. Energy is harvested at time slots when no job is executed. The objective is to find a feasible schedule that maximizes the weight of the scheduled jobs. A schedule is feasible if for every job $$J_j$$ in the schedule and its corresponding slot $$t_j$$ , $$t_{j} \ne t_{j'}$$ if $${j} \ne {j'}$$ , $$r_j \le t_j \le d_j$$ , and the available energy before $$t_j$$ is at least $$e_j$$ . To the best of our knowledge, we are the first to consider the theoretical aspects of this problem. In this work we show the following. (1) A polynomial time algorithm when all jobs have identical $$r_i, d_i$$ and $$w_i$$ . (2) A $$\frac{1}{2}$$ -approximation algorithm when all jobs have identical $$w_i$$ but arbitrary $$r_i$$ and $$d_i$$ . (3) An FPTAS when all jobs have identical $$r_i$$ and $$d_i$$ but arbitrary $$w_i$$ . (4) Reductions showing that all the variants of the problem in which at least one of the attributes $$r_i$$ , $$d_i$$ , or $$w_i$$ are not identical for all jobs are $$\textsf{NP-Hard}$$ .

We investigate the problem of finding winner(s) given a large number of users' (voters') preferences casted as ballots, one from each of the m users, where each ballot is a ranked order of preference of up to ℓ out of n items (candidates). Given a group protected attribute with k different values and a priority that imposes a selection order among these groups, the goal is to satisfy the priority order and select a winner per group that is most representative. It is imperative that at times the original users' preferences may require further manipulation to meet these fairness and priority requirement. We consider manipulation by modifications and formalize the margin finding problem under modification problem. We study the suitability of Instant Run-off Voting (IRV) as a preference aggregation method and demonstrate its advantages over positional methods. We present a suite of technical results on the hardness of the problem, design algorithms with theoretical guarantees and further investigate efficiency opportunities. We present exhaustive experimental evaluations using multiple applications and large-scale datasets to demonstrate the effectiveness of IRV, and efficacy of our designed solutions qualitatively and scalability-wise.