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[04/25]Expectation and Chance-constrained Models and Algorithms for Insuring Critical Paths

Topic: Expectation and Chance-constrained Models and Algorithms for Insuring Critical Paths

Reporter: Dr. Siqian Shen

Assistant Professor of Industrial and Operations Engineering, University of Michigan

Time: 14:00-15:00, April 25th (Wed)

Location: Meeting Room, 608 Founder Building

Host: Xi Zhang


In this paper, we consider a class of two-stage stochastic optimization problems arising in the protection of vital arcs in a critical path network. A project is completed after a series of dependent tasks are all finished. We analyze a problem in which task finishing times are uncertain, but can be insured a priori to mitigate potential delays. A decision maker must trade off costs incurred in insuring arcs with expected penalties associated with late project completion times, where lateness penalties are assumed to be lower semi-continuous nondecreasing functions of completion time. We provide decomposition strategies to solve this problem with respect to either convex or nonconvex penalty functions. In particular, for the nonconvex penalty case, we employ the Reformulation-Linearization Technique to make the problem amenable to solution via Benders decomposition. We also consider a chance-constrained version of this problem, in which the probability of completing a project on time is sufficiently large. We demonstrate the computational efficacy of our approach by testing a set of size-and-complexity diversified problems, using the Sample Average Approximation method to guide our scenario generation

About the speaker:

Dr. Siqian Shen earned her BS from Tsinghua University in China in 2007 and her Ph.D. from the University of Florida in 2011. She joined the Department of Industrial and Operations Engineering at the University of Michigan as a faculty in Fall 2011. Shen's theoretical interests lie in integer programming, stochastic programming, robust optimization, network optimization, and has implemented the methodologies in applications of network interdiction, epidemic control, power systems and energy management. The models she consider usually feature stochastic parameters and discrete decision variables. In general, optimal solutions are desired for trading off between the immediate benefits and the long-run gains, or between cost effectiveness and operational risk. Decomposition strategies are employed to tackle these problems, in which decision variables are categorized into two sets: the first-stage decisions made a priori and the second-stage responding ones. For optimally solving the decomposed models, Siqian develops cutting-plane algorithms via duality analysis and polyhedron studies. Siqian was named one of the two runners-up of the 2010 INFORMS Computing Society best student paper award and the 1st Place winner of the 2012 IIE Pritsker Doctoral Dissertation Award. She has also received 2011 IBM Smarter Planet Innovation Faculty Award, 2010 Chinese Government Award for Outstanding Self-financed Students Abroad, 2010 Mixed Integer Programming Workshop Student Travel Award, and 2011 ISE Graduate Award for Excellence in Research from the University of Florida.


Welcome all to the lecture!