Variations in parameters such as processing times, yields, and availability of materials and utilities can have a detrimental effect in the optimality and/or feasibility of an otherwise "optimal" production schedule. In this article, we propose a multi-stage adjustable robust optimization approach to alleviate the risk from such operational uncertainties during scheduling decisions. We derive a novel robust counterpart of a deterministic scheduling model, and we show how to obey the observability and non-anticipativity restrictions that are necessary for the resulting solution policy to be implementable in practice. We also develop decision-dependent uncertainty sets to model the endogenous uncertainty that is inherently present in process scheduling applications. A computational study reveals that, given a chosen level of robustness, adjusting decisions to past parameter realizations leads to significant improvements, both in terms of worst-case objective as well as objective in expectation, compared to the traditional robust scheduling approaches. (C) 2016 American Institute of Chemical Engineers