Joyce W. Yen

A Stochastic Programming Approach to the Airline Crew Scheduling Problem

Traditional methods model the billion-dollar airline crew scheduling problem as deterministic and do not explicitly include information on potential disruptions. Instead of modeling 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 are 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.

A stochastic integer programming approach to solving a synchronous optical network ring design problem

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 improves the solvability of the master problem. We also show 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.

Addressing capacity uncertainty in resource-constrained assignment problems

Resource-constrained assignment problems typically assume capacities are known. We focus on the situation when capacities are uncertain. In addition to the well-known generalized assignment problem (GAP) and the assignment problem with side-constraints (APSC), we discuss two other resource-constrained generalizations of the assignment problem. We identify two alternative approaches to utilize deterministic solution strategies while addressing capacity uncertainty, and illustrate how these approaches can be applied to a specific generalization. We also report the performance of these alternatives on a number of random test problems.

A stochastic programming approach to resource-constrained assignment problems

We address the resource-constrained generalizations of the assignment problem with uncertain resource capacities, where the resource capacities have an unknown distribution that can be sampled. We propose three stochastic programming-based formulations that can be used to solve this problem, and provide exact and approximate solution techniques for the resulting models. We also present numerical results for a large set of numerical problems. The results indicate that the solutions obtained using the stochastic programming approaches perform significantly better than those obtained using expected values of capacities in a deterministic solution strategy. In addition, stochastic-programming-based approximations are computationally as efficient as deterministic techniques.

Adaptive probabilistic branch and bound for level set approximation

We present a probabilistic branch-and-bound (PBnB) method for locating a subset of the feasible region that contains solutions in a level set achieving a user-specified quantile. PBnB is designed for optimizing noisy (and deterministic) functions over continuous or finite domains, and provides more information than a single incumbent solution. It uses an order statistics based analysis to guide the branching and pruning procedures for a balanced allocation of computational effort. The statistical analysis also prescribes both the number of points to be sampled within a sub-region and the number of replications needed to estimate the true function value at each sample point. When the algorithm terminates, it returns a concentrated sub-region of solutions with a probability bound on their optimality gap and an estimate of the global optimal solution as a by-product. Numerical experiments on benchmark problems are presented.

Learning to Thrive: Building Diverse Scientists’ Access to Community and Resources through the BRAINS Program

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.

A stochastic intra-ring synchronous optimal network design problem

We develop a stochastic programming approach to solving an intra-ring Synchronous Optical Network (SONET) design problem. This research differs from pioneering SONET design studies in two fundamental ways. First, while traditional approaches to solving this problem assume that all data are deterministic, we observe that for practical planning situations, network demand levels are stochastic. Second, while most models disallow demand shortages and focus only on the minimization of capital Add-Drop Multiplexer (ADM) equipment expenditure, our model minimizes a mix of ADM installations and expected penalties arising from the failure to satisfy some or all of the actual telecommunication demand. We propose an L-shaped algorithm to solve this design problem, and demonstrate how a nonlinear reformulation of the problem may improve the strength of the generated optimality cuts. We next enhance the basic algorithm by implementing powerful lower and upper bounding techniques via an assortment of modeling, valid inequality, and heuristic strategies. Our computational results conclusively demonstrate the efficacy of our proposed algorithm as opposed to standard L-shaped and extensive form approaches to solving the problem.

Beyond Traditional Scientific Training: The Importance of Community and Empowerment for Women in Ecology and Evolutionary Biology

While the biological sciences have achieved gender parity in the undergraduate and graduate career stages, this is not the case at the faculty level. The WEBS (Women Evolving the Biological Sciences) symposia go beyond traditional scientific training and professional development to address factors critical to women’s persistence in faculty careers: community and empowerment. Through a series of panel discussions, personal reflections and skills workshops, WEBS creates a community-based professional development experience and a space for participants to grapple with central issues affecting their scientific careers. Longitudinal qualitative survey data suggest that WEBS bolsters the participants’ confidence and empowerment, in addition to providing concrete skills for addressing a range of issues necessary to navigating scientific careers, leading to increased career satisfaction and career self-efficacy (i.e., the belief in one’s capacity to pursue their chosen career). These results highlight the importance and need for programs and opportunities for women in STEM that go beyond training in scientific skills and traditional professional development to include those that create a sense of community and empowerment.