Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

TB48

4 - Efficiently Solving Linear Bilevel Programming Problems using Off-the-Shelf Optimization Software Salvador Pineda, University of Malaga, Málaga, Spain, Henrik Bylling, Juan Miguel Morales Many optimization models are formulated as bilevel problems. Most solution methods reformulate the bilevel problem as a mathematical program with complementarity conditions (MPCC). MPCCs are single-level non-convex optimization problems that do not satisfy the standard constraint qualifications and therefore, nonlinear solvers may fail to provide even local optimal solutions. In this paper we propose a method that first solves iteratively a set of regularized MPCCs using NL solvers to find a local optimal solution. Local optimal information is then used to reduce the computational burden of solving the Fortuny-Amat reformulation of the MPCC to global optimality using MIP solvers. n TB46 North Bldg 228B Enhance Energy Infrastructure Resilience with Operations Research Sponsored: Energy, Natural Res & the Environment/Energy Sponsored Session Chair: Feng Qiu, Argonne National Laboratory, Lemont, IL, 60439, United States Co-Chair: Matteo Spada, Paul Scherrer Institut, Villigen PSI, 5232, Switzerland 1 - A Bayesian Framework for Spatial Multi-criteria Risk Assessment: An Application to Oil Tankers Matteo Spada, Paul Scherrer Institut, OHSA/D19, Villigen PSI, 5232, Switzerland, Valentina Ferretti In recent years, increasing attention has been paid to the spatial nature of risk, mainly in the environmental decision-making domain since it commonly involves spatial impacts, spatial vulnerabilities and spatial risk-mitigation alternatives. In this context, spatial risk assessment procedures have been developed but they generally neglect the multi-dimensional nature of spatial impacts. In this study, we propose a Bayesian framework for spatial multi-criteria risk assessment to integrate uncertainty, model probabilities and multiple impacts. The presented model is applied and tested for tanker oil spills due to their international relevance and strong spatial dimension. 2 - Resilient Power Distribution Grids from Planning to Restoration Zhaoyu Wang, Harpole-Pentair Assistant Professor, Iowa State University, 1113 Coover Hall, Ames, IA, 50011, United States, Shanshan Ma, Anmar Arif This talk will present our recent works on enhancing power distribution grid resilience. Specifically, we will firstly propose a two-stage stochastic model for long-term resilience-oriented design via infrastructure hardening and adding smart grid technologies. Then we will develop a co-optimization model for real- time coordination of crew repairs and remedial operations to accelerate the service restoration. 3 - Recovery Logistics Co-optimization of Power Systems Against Natural Disasters Yunhe Hou, University of Hong Kong, CYC Building, Room 508, Hong Kong, Hong Kong, Shunbo Lei Efficient service restoration and infrastructure recovery after a natural disaster are one of the most critical requirements. First, repair crews should be optimally scheduled to recover the damaged components. Second, the system may need to be reconfigured for service restoration. Third, in some cases, mobile power sources are available to supply critical loads. There are also some other resources or strategies. We propose a framework, models, and algorithms to co-optimize major resources and strategies involved in power system recovery logistics. We show that such co-optimization based on our proposed method leads to more effective restoration of power systems. 4 - A Heuristic Approach for Integrated Infrastructure Network Restoration Crew Routing Problem Nazanin Morshedlou, University of Oklahoma, Norman, OK, 73071, United States, Kash Barker, Andres David Gonzalez In this research, we consider the problem of restorative capacity enhancement problem in an infrastructure network which is interconnected with a routing network through which restoration crews are dispatched, from the originate depots, towards disrupted locations. Noting the complicated nature of resilience routing problems, we further propose a constructive heuristic algorithm which use a heuristic algorithm to improve the coordinated routes obtained from the relaxed form of the original formulations.

n TB47 North Bldg 229A How to Influence and Improve Decisions Through Optimization Models Emerging Topic Session Chair: Jorge A. Sefair, Arizona State University, 699 S. Mill Ave., Tempe, AZ, 85287-8809, United States 1 - How to Influence and Improve Decisions Through Optimization Models Jeffrey D. Camm, Wake Forest University, School of Business, P.O. 7897, Winston-Salem, NC, 27109, United States Industry’s recent increased focus on data driven-decision making and the use of analytics in all sectors from sports to financial services to technology and healthcare has led to a resurgence in the interest in traditional operations research tools such as optimization, simulation, and decision analysis. As organizations mature analytically, it seems likely that we will see a further increase in interest in prescriptive analytics, including optimization modeling, which is the focus of this tutorial. With massive amounts of data being routinely collected in real time and an increased awareness on the part of management of the value of data, the availability of data is typically no longer the bottleneck in the optimization modeling process. Increased computing speed, improved algorithms, parallel processing, and cloud computing have increased the size of optimization problems that can be solved to optimality. Considering better data availability and the dramatic increase in our ability to solve problems, what are the impediments to keeping us from having significant influence and impact on decision making? Going forward, it is possible that our ability to (1) structure a messy decision problem into a useful optimization framework, (2) properly use the model to deliver valuable insights for management, and (3) communicate to management the value proposition of our insights, will become the new reasons we might fail to have the impact we know is possible. In this tutorial, we review types of optimization models and the art of modeling, that is, the process of going from mess to model. We discuss how to use an optimization model to provide not simply “the answer but insights that will be useful to managers and influence their decision making. We discuss the importance of communication in influencing, and provide examples and best practices relevant for optimization. We conclude with thoughts on how optimization modeling is important to the bustling fields of data science and artificial intelligence. Supply Chain Information Disclosure Workshop II Sponsored: Energy, Natural Res & the Environment Environment & Sustainability Sponsored Session Chair: Donna Marshall, University College-Dublin, Dublin, Ireland Co-Chair: Steve New, University of Oxford, Oxford, OX1 3BW, United Kingdom 1 - The Missing Link? The Strategic Role of Procurement in Building Sustainable Supply Networks Veronica H. Villena, Penn State University, 412 Business Building, Supply Chain and Information Systems, State College, PA, 16802, United States Some multinational companies (MNCs) require their (tier-one) suppliers to comply with their sustainability requirements and often to “cascade such requirements to their own (second-tier) suppliers. This research investigates why this cascading effect often fails and how procurement processes are deployed to engage tier-one and tier-two suppliers in MNCs’ sustainability agendas. 2 - Collective Disclosure in Complex Supply Chains Jury Gualandris Considering supply chains as complex networks of firms that get organized to achieve (and benefit from) interlocked behaviours, we theorize supply chain structures under which collective disclosure is more likely to occur. Collective disclosure happens when most firms in a supply chain publicly disclose large amounts of information to describe social and environmental practices and performance. Supply chain structures are analyzed in terms of diverse complexities such as numerosity, heterogeneity, interconnectedness and dynamism. We test our hypotheses using a novel data set of 226 supply chains that, overall, involve 5118 manufacturing firms and 20680 buyer-supplier contracts. n TB48 North Bldg 229B

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