Integer programs with a fixed number of constraints are solvable in pseudo-polynomial time in the largest coefficient of any constraint. We give a new algorithm which improves the running time of the state of the art. Moreover, we show that improving on our algorithm for any number of constraints is equivalent to improving over the quadratic time algorithm for (min, +)-convolution. This is strong evidence that our algorithm’s running time is the best possible. We also present a specialized algorithm for testing the feasibility of an integer program and give a tight lower bound, which is based on the strong exponential time hypothesis in this case. Funding: This work was supported by the German Research Foundation [Grants JA 612/16-1, JA 612/20-1].

Integer linear programs of configurations, or configuration IPs, are a classical tool in the design of algorithms for scheduling and packing problems where a set of items has to be placed in multiple target locations. Herein, a configuration describes a possible placement on one of the target locations, and the IP is used to choose suitable configurations covering the items. We give an augmented IP formulation, which we call the module configuration IP. It can be described within the framework of n-fold integer programming and, therefore, be solved efficiently. As an application, we consider scheduling problems with setup times in which a set of jobs has to be scheduled on a set of identical machines with the objective of minimizing the makespan. For instance, we investigate the case that jobs can be split and scheduled on multiple machines. However, before a part of a job can be processed, an uninterrupted setup depending on the job has to be paid. For both of the variants that jobs can be executed in parallel or not, we obtain an efficient polynomial time approximation scheme (EPTAS) of running time $$f(1/\varepsilon )\cdot \mathrm {poly}(|I|)$$f ( 1 / ε ) · poly ( | I | ) . Previously, only constant factor approximations of 5/3 and $$4/3 + \varepsilon $$4 / 3 + ε , respectively, were known. Furthermore, we present an EPTAS for a problem where classes of (non-splittable) jobs are given, and a setup has to be paid for each class of jobs being executed on one machine.