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We address a family of hard benchmark instances for the Simple Plant LocationProblem (also known as the Uncapacitated Facility Location Problem). The recentattempt by Fischetti et al. to tackle the K\"orkel-Ghosh instances resulted inseven new optimal solutions and 22 improved upper bounds. We use automatedgeneration of heuristics to obtain a new algorithm for the Simple PlantLocation Problem. In our experiments, our new algorithm matched all theprevious best known and optimal solutions, and further improved 12 upperbounds, all within shorter time budgets compared to the previous efforts.  Our algorithm design process is split into two phases: (i) development ofalgorithmic components such as local search procedures and mutation operators,and (ii) composition of a metaheuristic from the available components. Phase(i) requires human expertise and often can be completed by implementing severalsimple domain-specific routines known from the literature. Phase (ii) isentirely automated by employing the Conditional Markov Chain Search (CMCS)framework. In CMCS, a metaheuristic is flexibly defined by a set of parameters,called configuration. Then the process of composition of a metaheuristic fromthe algorithmic components is reduced to an optimisation problem seeking thebest performing CMCS configuration.  We discuss the problem of comparing configurations, and propose a newefficient technique to select the best performing configuration from a largeset. To employ this method, we restrict the original CMCS to a simpledeterministic case that leaves us with a finite and manageable number ofmeaningful configurations.

Conditional Markov Chain Search for the Simple Plant Location Problem improves upper bounds on twelve K\"orkel-Ghosh instances