Please note that funding to Dr. Simon Hsiang, a critical co-investigator for the development of the Spacecraft Optimization Layout and Volume (SOLV) model, was unavailable to him for 9 months following his transfer to a new university. As such, progress for this year was limited and a no-cost extension will result in an end date in FY18. However, interim work took place as summarized here. At the Year 1 End-of-Year Review (July 2015), SOLV’s Key Driving Requirements (KDRs) were peer-reviewed and concurred. Line-by-line reviews resulted in updates to the requirement text and the proposed verification methods and success criteria. As part of the outcome of this review, the SOLV team worked closely with NASA to focus on the documentation of task volume assumptions, which will be used as inputs to SOLV; and to develop a process to represent varying potential task volumes using a strategy referred to as the gradient cuboid method. In efforts to define the variables and metrics that will drive SOLV, the team began investigating the use of the Analytical Hierarchy Process (AHP) to systematically assign values and priorities to habitat volume drivers. AHP will be used as a decision support technique to identify factors with the greatest probable influence and, used in conjunction with Choquet integrals, will feed the model’s output of a scorecard that provides an at-a-glance assessment of overall costs and benefits associated with layouts generated by SOLV. In addition to efforts to refine the algorithm approach, the SOLV team concentrated on ensuring proper documentation of the modeling efforts. The team finalized a matrix mapping planned SOLV documentation to requirements for NASA-STD-7009. To begin documentation efforts, the team drafted an initial version of a Verification and Validation (V&V) plan, which will be updated throughout model development to reflect the current state of V&V. The team also drafted an outline for the SOLV User Guide, which will document details such as assumptions regarding user qualifications and problem reporting. When funding became available to the University of North Carolina-Charlotte (UNC-C) team members, work resumed on code based bin-packing algorithms that will be used to generate layouts. Gradient cuboid representations of task volumes will feed directly into the bin-packing code, which, based on defined constraints, will generate multiple potential task layouts that will in turn be assessed using criteria driven out by the AHP and Choquet integral process. The bin-packing algorithm currently assesses space in two dimensions, with plans in the near future to expand to three dimensions.