Traditional methods model the billion-dollar airline crew scheduling problem as deterministic and do not explicitly include information on potential disruptions. Instead of modelling the crew scheduling problem as deterministic, we consider a stochastic crew scheduling model and devise a solution methodology for integrating disruptions in the evaluation of crew schedules. The goal is to use that information to find robust solutions that better withstand disruptions. Such an approach is important because we can proactively consider the effects of certain scheduling decisions. By identifying more robust schedules, cascading delay effects will be minimized. In this paper we describe our stochastic integer programming model for the airline crew scheduling problem and develop a branching algorithm to identify expensive flight connections and find alternative solutions. The branching algorithm uses the structure of the problem to branch simultaneously on multiple variables without invalidating the optimality of the algorithm.We present computational results demonstrating the effectiveness of our branching algorithm.

We develop stochastic integer programming techniques tailored toward solving a Synchronous Optical Network (SONET) ring design problem with uncertain demands.Our approach is based on an L-shaped algorithm, whose (integer) master program prescribes a candidate network design, and whose (continuous) subproblems relay information regarding potential shortage penalty costs to the ring design decisions. This naive implementation performs very poorly due to two major problems: (1) the weakness of the master problem relaxations, and (2) the limited information passed to the master problem by the optimality cuts. Accordingly, we enforce certain necessary conditions regarding shortage penalty contributions to the objective function within the master problem, along with a corresponding set of valid inequalities that improve the solvability of the master problem. We also detail how a nonlinear reformulation of the model can be used to capture an exponential number of optimality cuts generated by the linear model. We augment these techniques with a powerful upper-bounding heuristic to further accelerate the convergence of the algorithm, and demonstrate the effectiveness of our methodologies on a test bed of randomly generated stochastic SONET instances.

Purpose The advancement of equity, diversity and inclusion in higher education is dependent on institutional culture changes in academia. Faculty equity, diversity and inclusion efforts must engage departmental leadership. The purpose of this paper is to describe the growth and expansion of the ADVANCE leadership program at the University of Washington (UW) for department chairs that was designed to provide department chairs the skills, community and information needed to be agents of change within the academy. Design/methodology/approach The paper chronicles the program’s growth from a campus-based workshop program to national workshops (LEAD) to a web-based toolkit (LiY!) to support institutions in running their own UW ADVANCE-inspired leadership workshops. Findings The paper demonstrates the success of each growth stage and the expansion of program impact. Practical implications The paper offers recommendations for growing a model from a local to national scale and adapting the described leadership development model at other institutions. Originality/value The paper shares a successful model for equipping department chairs to be advocates of gender equity, diversity and inclusion in STEM and to be change agents in higher education.

BRAINS: Broadening the Representation of Academic Investigators in NeuroScience is a national program designed to diversify neuroscience by increasing retention of early-career neuroscientists from underrepresented groups. This paper highlights particular programmatic innovations and discusses recommendations to broaden participation in the life sciences.

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