Improved Approximation Algorithms for Non-Preemptive Throughput Maximization
This provides better theoretical guarantees for scheduling jobs with time windows, which is incremental but important for algorithm design in operations research.
The paper tackles the strongly NP-hard Non-Preemptive Throughput Maximization scheduling problem, improving the approximation factor from about 1.551 to 4/3+ε in polynomial time and to 5/4+ε in pseudo-polynomial time, with extensions to multiple machines.
The (Non-Preemptive) Throughput Maximization problem is a natural and fundamental scheduling problem. We are given $n$ jobs, where each job $j$ is characterized by a processing time and a time window, contained in a global interval $[0,T)$, during which~$j$ can be scheduled. Our goal is to schedule the maximum possible number of jobs non-preemptively on a single machine, so that no two scheduled jobs are processed at the same time. This problem is known to be strongly NP-hard. The best-known approximation algorithm for it has an approximation ratio of $1/0.6448 + \varepsilon \approx 1.551 + \varepsilon$ [Im, Li, Moseley IPCO'17], improving on an earlier result in [Chuzhoy, Ostrovsky, Rabani FOCS'01]. In this paper we substantially improve the approximation factor for the problem to $4/3+\varepsilon$ for any constant~$\varepsilon>0$. Using pseudo-polynomial time $(nT)^{O(1)}$, we improve the factor even further to $5/4+\varepsilon$. Our results extend to the setting in which we are given an arbitrary number of (identical) machines.