Significant work has been dedicated to developing methods for communicating reasons for decision-making within au-
 tomated scheduling systems to human users. However, much less focus has been placed on communicating reasons for why
 scheduling systems are unable to arrive at a feasible solution when over-constrained. We investigate this problem in the
 context of task scheduling. We introduce the agent resource-constrained project scheduling problem (ARCPSP), an ex-
 tension of the resource-constrained project scheduling problem which includes a conception of agents that execute tasks
 in parallel. We outline a generic framework, based on efficiently enumerating minimal unsatisfiable sets (MUS) and
 maximal satisfiable sets (MSS), to produce small descriptions of the source of infeasibility. These descriptions are supple-
 mented with potential relaxations that would fix the infeasibility found within the problem instance. We illustrate how
 this method may be applied to the ARCPSP and demonstrate how to generate different types of explanations for an over-
 constrained instance of the ARCPSP.
We develop a framework for generating human-understandable explanations for why infeasibility is occurring in over-constrained instances of a class of resource-constrained scheduling problems.