Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WC73

3 - Dynamic Return and Resale Policies for Heterogeneous Strategic Customers with Uncertain Valuations Lan Lu, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, Hong Kong, Qian Liu This paper develops a model of return and resale policies with heterogeneous strategic customers who have uncertain valuations prior to purchase. The seller faces a two-period selling season, where the returned products in the first period can be resold in the second period. We characterize the seller’s optimal return policies in both periods and compare two resale policies: differentiate the returned products with new products or not. We demonstrate that the seller tends to differentiate the returned products when customers are highly differentiated. Moreover, we find that the seller does not always benefit from the increase of customer valuation. 4 - Robust Optimization Approach to Process Flexibility Designs with Price Differentials Shixin Wang, New York University, New York, NY, 10012, United States, Xuan Wang, Jiawei Zhang We study process flexibility designs when products exhibit price differentials. We introduce the Profit Plant Cover Index (PPCI) and prove that a general class of robust measures can be expressed as functions of a design’s PPCIs and the given uncertainty set, which leads to a method to compare the worst-case performance of different designs. Applying these results, we prove that under a broad class of uncertainty sets and robust measures, the alternate long chain is optimal among all long chains with an equal number of high-profit products and low-profit products. Finally, we develop a heuristic based on the PPCIs to generate effective flexibility designs when products exhibit price differentials. 5 - In-house vs. Outsourcing: The Effect of Volume-based Learning on Quality Competition Yanni Ping, Drexel University, 3220 Market Street, Philadelphia, PA, 19104, United States, Seung-Lae Kim This paper considers an original equipment manufacturer (OEM) who outsources finished products to a contract manufacturer (CM), where the CM, by adopting existing technology, achieves cost reduction and quality improvement through learning-by-doing. Besides the role of upstream partner, the CM also acts like a downstream competitor. We study the OEM’s outsourcing strategy dynamically from both cases when competition exists and does not exist by constructing a two-period model and explore the interplay of learning, quality and cost. 6 - Block Ownership in Vertical Relationships in the Presence of Downstream Competition Fang Fang, Assistant Professor, California State University, Los Angeles, CA, 90032, United States, Baojun Jiang, Jiong Sun Block ownership (i.e., partial ownership) plays an important role in aligning the incentives of firms involved in vertical relationships. This paper examines the impacts of block ownership on pricing decisions, firm profitability, as well as consumer and social surplus. We show that such impacts may depend on the nature of downstream competition. n WC73 West Bldg 211B Practice- Robust Optimization & Applications I Contributed Session Chair: Jonathan David Lonski, Clemson University, Clemson, SC, 29631, United States 1 - Robust Portfolio Optimization Based on Stochastic Dominance and Empirical Likelihood Peng Xu, Aalto University School of Business, Runeberginkatu 22-24, Helsinki, FIN-00100, Finland Identifying Second-order Stochastic Dominance (SSD)-efficient portfolios is of great interest to finance research where investors are routinely assumed to be rationally risk-averse in financial decision making. This paper seeks to (i) evaluate some most recent SSD optimization approaches and their out-of-sample performance, and (ii) examine if robust optimization based on SSD and Empirical Likelihood (EL) improves out-of-sample performance of these approaches. We report the results from an empirical application analyzing how robust diversification among industry portfolios using SSD and EL increases the likelihood of obtaining out-of-sample dominance over a given benchmark portfolio 2 - Robust Multi-criteria Decision Making for Hesitant Behavior Gevorg Stepanyan, PhD Candidate, University of Michigan - Dearborn, Dearborn, MI, United States, Jian Hu We presents a robust multi-criteria decision making model for hesitant behavior.

3 - Robust Repositioning for Vehicle Sharing Long He, National University of Singapore, Mochtar Riady Building, BIZ1 8-73, 15 Kent Ridge Drive, Singapore, 119245, Singapore, Zhenyu Hu, Meilin Zhang Our paper discusses the operational decision of dynamic fleet repositioning for vehicle sharing. We first formulate the problem as a stochastic dynamic program to minimize the expected total repositioning cost and lost sales penalty. To solve for a multi-region system, we deploy the distributionally robust approach that can incorporate demand temporal dependence, motivated by observations from real trip data. In a real-world case study, we quantify the “value of repositioning” and compare with several benchmarks to demonstrate that the proposed solutions are computationally scalable and in general result in lower cost with less frequent repositioning. 4 - Mitigating Disaster-induced Transportation System Losses via Robust Optimization Jonathan David Lonski, Clemson University, Clemson, SC, 29631, United States, Scott J. Mason Recent natural disasters like Hurricane Sandy (2012) and Tropical Storm Harvey (2017) have caused $20B+ in economic losses and necessitated $35B+ in restoration efforts. During such disasters, it is often too late for decision makers to spend time and effort analyzing information, and weighing potential outcomes. We present our research into developing robust plans for minimizing the total cost of economic losses and reparations incurred by transportation systems during natural or human-caused disasters. We seek to improve transportation system plan resiliency via robust optimization techniques and establishing an improved, cloud-based method of data collection for use in our models. Theory and Application of the ANP Sponsored: Multiple Criteria Decision Making Sponsored Session Chair: Orrin Cooper, University of Memphis, Memphis, TN, 38152, United States 1 - An AHP Consensus Reaching Model with Both Individual and Aggregated Opinions Qingxing Dong, Central China Normal University, Wuhan, China, Qi Sheng, Gaohui Cao The Analytic Hierarchy Process (AHP) is an effective tool and has been widely used in group decision making. In this paper a new consensus reaching model based on the AHP is proposed, which considers both individual and aggregated opinions. The compatibility index can be used to determine both the individual consensus level (ICI) and the central consensus level (CCI). Then this model provides feedback suggestions to the most incompatible decision makers so they can adjust their opinions adaptively depending on their ICI and CCI in each round. The integrated adaptive consensus reaching model is constructed. Finally, a numerical example is given to verify the feasibility and effectiveness of the model. 2 - Coherency and Reducing Comparisons in the ANP Orrin Cooper, University of Memphis, Memphis, TN, 38152, United States One of the perceived challenges of using the ANP is number of comparisons that need to be made to ensure the model will converge without absorbing states. There are also data quality checks that one can use to increase the confidence she has in the decision. Coherency testing can be balanced to not only test for coherency but also reduce the number of needed comparisons in an ANP model. 3 - The Added Value of a Team-based Model in Preventive Healthcare Services M Gabriela Sava, Clemson University, College of Business, 145 Sirrine Hall, Clemson, SC, 29634, United States, Luis Vargas, Jerrold H. May, Jennifer Shang, James Dolan The role and importance of preventive healthcare services has increased significantly due to the health benefits to the patient. The design of such services becomes a challenge due to the increased number of patients that would like to get more involved in the preventive decision-making process, but they don’t possess all the necessary information. We propose a team-based model for designing the process of choosing the most appropriate colorectal cancer screening option. The value-added in the model is the result of the contribution of the healthcare provider to achieve beneficial patient outcomes. n WC74 West Bldg 212A Joint Session MCDM/Practice Curated:

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