2015 Informs Annual Meeting

TA78

INFORMS Philadelphia – 2015

TA77 77-Room 300, CC Green Supply Chain Management Contributed Session

4 - Assessing the Impact of Product and Service Quality on Consumer Returns: A Data Analytics Study Necati Ertekin,Texas A&M University, Mays Business School, College Station TX 77840, United States of America, nertekin@mays.tamu.edu, Gregory Heim, Michale Ketzenberg We contribute to the understanding of consumer return behavior by examining the association between in-store customer experience during a purchase and a subsequent return. We demonstrate that retail efforts such as increasing salesper- son competence and improving store environment that are so long believed to prevent returns may indeed induce returns.

Chair: Vinay Gonela, Assistant Professor Of Management, Southwest Minnesota State University, CH 214, 1501 State Street, Marshall, MN, 56258, United States of America, vinay.gonela@smsu.edu 1 - The Impact of Contracts on Environmental Innovation in a Supply Chain Seyoun Jung, PhD Student, KAIST (Korea Advanced Institute of Science and Technology), 85 Hoegiro, Dongdaemun-gu, Seoul, Korea, Republic of, ssebea@business.kaist.ac.kr, Bosung Kim, Kun Soo Park We examine the impact of contracts between a supplier and a manufacturer on the supplier’s environmental innovation. We calculate and compare the equilibrium outcomes under three types of contract such as wholesale-price, revenue-sharing, and quality-dependent contracts. 2 - Producer-dominated Green Supply Chain Collaboration under Trade-in Programs Chih-Tien Chiu, Doctoral Student, National Taiwan University, This paper aims to address new-product/used-product pricing in a green logistics. We adopt the dynamic programming approach integrated with the logit model to formulate the n-period trade-in pricing-logistics problem, where the logit model is utilized for trade-in service channels choice. Data collected via stated preference experiments are used for the parameter estimation of the logit model, followed by conducting quantitative analyses to provide important findings and managerial insights. 3 - Metrics for Sustainable Operations: Current State and Path to Improvement Remi Charpin, Clemson University, 100 Sirrine Hall, Clemson, United States of America, rcharpi@g.clemson.edu, Aleda Roth From an operations and supply chain management lens, we examine sustainability metrics currently being reported by firms. We propose that certain metrics are ‘attractors,’ as they are apt to lead the business towards sustainability, whereas others are deemed to be ‘detractors’ that are likely to be used for ‘greenwashing.’ 4 - Stochastic Optimization of Sustainable Industrial Symbiosis Based Hybrid Generation Bioethanol Supply Chain Vinay Gonela, Assistant Professor Of Management, Southwest Minnesota State University, CH 214, 1501 State Street, Marshall, MN, 56258, United States of America, vinay.gonela@smsu.edu, Atif Osmani, Jun Zhang, Joseph Szmerekovsky This paper focuses on designing a new industrial symbiosis based hybrid generation bioethanol supply chain (ISHGBSC). A SMILP model is proposed to design the optimal ISHGBSC under different sustainability standards. The result provides guidelines for policy makers to determine the appropriate standard to use under different sustainable concerns. In addition, it provides investors a guideline to invest in different technologies under different sustainability standards. No.1,Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan - ROC, d03741001@ntu.edu.tw, Mu-chen Chen, Jiuh-biing Sheu

TA76 76-Room 204C, CC Advances in Simulation-based Optimization I

Sponsor: Simulation Sponsored Session

Chair: Jie Xu, George Mason University, 4400 University Dr., MS 4A6, Engr Bldg, Rm 2100, Fairfax, VA, 22030, United States of America, jxu13@gmu.edu 1 - Estimating the Probability of Convexity of a Function Observed with Noise Nanjing Jian, PhD Student, Operations Research and Information Engineering, 288 Rhodes Hall, Cornell University, Ithaca, NY, 14850, United States of America, nj227@cornell.edu, Shane Henderson Given estimates of the values of a function observed with noise from simulation on a finite set of points, we wish to sequentially estimate the probability that the function is convex. By updating a Bayesian posterior on the function values, we iteratively estimate the posterior probability of convexity by solving certain linear programs in a Monte Carlo simulation. We discuss a variety of variance reduction methods for the estimation and the linear programs associated with each. 2 - Adaptive Sampling Trust Region Optimization Sara Shashaani, Associate Professor, Department of Statistics, Purdue University, 250 N University Street, West Lafayette, IN, 47907, United States of America, pasupath@purdue.edu, Raghu Pasupathy We develop derivative free algorithms for optimization contexts where the objective function is observable only through a stochastic simulation. The algorithms we develop follow the trust-region framework where a local model is constructed, used, and updated as the iterates evolve through the search space. We incorporate adaptive sampling to keep the variance and the squared bias of the local model in lock step, in a bid to ensure optimal convergence rates. 3 - Parallel Empirical Stochastic Branch & Bound Sajjad Taghiye, George Mason University, 4400 University Dr., To efficiently solve problems with time-consuming high-fidelity simulations, we develop a new parallel algorithm known as parallel empirical stochastic branch & bound (PESBB) to exploit the power of high performance computing. We will discuss synchronous and asynchronous versions of PESBB and present initial numerical results to demonstrate the scalability of PESBB. 4 - Finding the Best using Multivariate Brownian Motion Seong-hee Kim, Professor, Georgia Institute of Technology, 755 Ferst Dr NW, Atlanta, GA, 30332, United States of America, skim@isye.gatech.edu, Ton Dieker, Seunghan Lee We present a new fully sequential procedure based on multivariate Brownian motion when variances are known but unequal. The procedure uses an ellipsoid as a continuation region, and a system with the worst sample mean is eliminated whenever the procedure’s statistic exits the ellipsoid. The size of the ellipsoid changes as the number of survivors decreases. Experimental results are provided for both equal and unequal variances. MS 4A6, Engr Bldg, Rm 2100, Fairfax, VA, 22030, United States of America, staghiy2@gmu.edu, Jie Xu

TA78 78-Room 301, CC Big Data and Energy Contributed Session

Chair: Feng Gao, SGRI North America, 5451 Great America Parkway, Santa Clara, CA, 95054, United States of America, feng.gao@sgrina.com 1 - Resilient Power System State Estimation using Compressive Sensing Hanif Livani, Assistant Professor, University of Nevada Reno, Electrical & Computer Engineering, MC 0111, 1185 Perry St, / Room 302, Reno, NV, 89557, United States of America, hlivani@unr.edu Phasor Measurement Units (PMU) have become widely used for power system monitoring and control. However, they are not installed on all the buses in a network. Therefore, PMU-only state estimation encounters problems arising from a limited number of installed PMUs and probable data losses as the results of congestion or disconnection in communications.In this study, we propose power system state estimation using Compressive Sensing (CS) algorithm which is resilient to loss of data.

283