įrom my point of view, the problem of production scheduling combines the potential impact on real-world applications with exciting research opportunities in the domain of learning methods. The red-striped bar corresponds to the downtime of a machine. The color of a bar corresponds to the jobs and its length defines the processing time. The schedule is presented as a timeline plot. That is why most of the time people in the industry resort to some form of stochastic search combined with domain-specific heuristics. However, the majority of the methods cannot cope with real-world problem sizes or additional constraints that I’ve mentioned above. Though this problem is NP-hard (no exact solution in polynomial time), it is rather well-studied by the CO community, which offers a handful of methods to solve its theoretical (simplified) version. Theoretically, this problem is a complicated instance of the “Job scheduling” problem which together with the “Capacitated vehicle routing” is considered to be a classic of Combinatorial Optimization (CO). jobs of class A cannot be processed on machines of class B, etc. Additionally, the real problem is complicated by a set of imposed constraints, e.g. different throughput for the machines and different required processing time for the jobs, and many more in practice. Heterogeneous means that both, the machines and the jobs can have different properties, e.g. Given a set of heterogeneous machines and a set of heterogeneous production jobs, compute the processing schedule that minimizes specified metrics. To begin with, let me describe the problem of production scheduling, which has motivated SOLO in the first place. Therefore I’ve decided to write a short overview and highlight its main findings. Though the paper is rather applied, it was still written as a scientific publication and might be a tough read for someone who isn’t used to it. Recently I was lucky enough to be a part of the team that has published the paper “SOLO: Search Online, Learn Offline” at SoCS’21.
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