Authors: Andre Krumel Portella, Catherine Paquet, Adrianne Rahde Bischoff, Roberta Dalle Molle, Aida Faber, Spencer Moore, Narendra Arora, Robert Levitan, Patricia Pelufo Silveira, Laurette Dubé

Publication: PLoS ONE, 14(2): February 2019, e0212290

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Authors: Dapeng Liang, Chenxuan Hou, Myung-Soo Jo, and Emine Sarigollu

Publication: Journal of Cleaner Production, Volume 210, 10 February 2019, Pages 1301-1310

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Authors: Divinus Oppong-Tawiah, Jane Webster, Sandy Staples, Ann-Frances Cameron, Ana Ortiz de Guinea, Tam Y. Hung

Publication: Journal of Business Research, Forthcoming

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Authors: Angelos Georghiou, Angelos Tsoukalas, Wolfram Wiesemann

Publication: Operations Research, Forthcoming

Abstract: 

Multi-stage robust optimization problems, where the decision maker can dynamically react to consecutively observed realizations of the uncertain problem parameters, pose formidable theoretical and computational challenges. As a result, the existing solution approaches for this problem class typically determine suboptimal solutions under restrictive assumptions. In this paper, we propose a robust dual dynamic programming (RDDP) scheme for multi-stage robust optimization problems. The RDDP scheme takes advantage of the decomposable nature of these problems by bounding the costs arising in the future stages through lower and upper cost to-go functions. For problems with uncertain technology matrices and/or constraint right-hand sides, our RDDP scheme determines an optimal solution in finite time. If also the objective function and/or the recourse matrices are uncertain, our method converges asymptotically (but deterministically) to an optimal solution. Our RDDP scheme does not require a relatively complete recourse, and it offers deterministic upper and lower bounds throughout the execution of the algorithm. We demonstrate the promising performance of our algorithm in a stylized inventory management problem.

Authors: Senay Solak, Armagan Bayram, Mehmet Gumus, Yueran Zhuo

Publication: Operations Research, Forthcoming

Abstract:

A dramatic increase in U.S. mortgage foreclosures during and after the great economic recession of 2007-2009 had devastating impacts on the society and the economy. In response to such negative impacts, non-profit community development corporations (CDCs) throughout the U.S. utilize various resources, such as grants and lines of credit, in acquiring and redeveloping foreclosed housing units to support neighborhood stabilization and revitalization. Given that the cost of all such acquisitions far exceeds the resources accessible by these non-profit organizations, we identify socially optimal policies for CDCs in dynamically selecting foreclosed properties to target for potential acquisition as they become available over time. We evaluate our analytical results in a numerical study involving a CDC serving a major city in the U.S, and specify social return based thresholds defining selection decisions at different funding levels. We also find that for most foreclosed properties CDCs should not offer more than the asking price, and should typically consider overbidding only when the total available budget is low. Overall, comparisons of optimal policies with historical acquisition data suggest a potential improvement of around 20% in expected total impacts of the acquisitions on nearby property values. Considering a CDC with annual fund availability of $4 million for investment, this corresponds to an estimated additional value of around $280,000 for the society.

Professor Patricia Hewlin’s co-authored article entitled, “How do callings relate to job performance? The role of organizational commitment and ideological contract fulfillment,” was nominated by the editors of Human Relations as one of the top papers published in 2018.

The article is co-authored in part by Desautels PhD alumni Sung Soo Kim and Donghoon Shin.

Human Relations is among the FT 50 journal list.

Angelos Georghiou, Assistant Professor in Operations Management, was recently appointed Associate Editor of Energy Systems - Optimization, Modeling, Simulation, and Economic Aspects.

The journal Energy Systems presents mathematical programming, control, and economic approaches towards energy systems related topics, and is especially relevant in light of the major worldwide challenges confronting humanity in this century.