2016 INFORMS Annual Meeting Program

SB86

INFORMS Nashville – 2016

SB86 GIbson Board Room-Omni

SB94 5th Avenue Lobby- MCC

Manufacturing II Contributed Session Chair: Gourav Dwivedi, Doctoral Student, Indian Institute of Management, Indian Institute of Management, IIM Road, off Sitapur Road, Lucknow, 226013, India, fpm14013@iiml.ac.in 1 - A Lagrangian Relaxation Approach For A Multiproduct Stochastic Production Planning Problem Reha Uzsoy, North Carolina State University, Dept. of Industrial & Systems Engg, 300 Daniels Hall Camps Box 7906, Raleigh, NC, 27695-7906, United States, ruzsoy@ncsu.edu, Erinc Albey, Karl Kempf We model a single-stage multi item capacitated production-inventory system with stochastic demand. We present a chance-constrained production planning model that considers forecast evolution, which is solve using Lagrangian relaxation. Computational results show that the proposed approach outperforms previous myopic capacity allocation procedures. 2 - Order Scheduling For A Class Of Electronic Ceramic Manufacturers In Make To Order Environments Zhongshun Shi, Peking University, Haidian Chengfu Road 298, Founder Building Room 512, Beijing, 100871, China, zhongshun@pku.edu.cn, Hongqiang Gao, Leyuan Shi Motivated by the applications for a class of electronic ceramic manufacturers, we study the order scheduling on sintering operations in make-to-order environments, where sintering furnaces are modeled as batch processing machines. The order consists of multiple types of jobs with specific demand quantity. We consider the total weighted order completion time as objective function and prove the problem is strongly NP-hard. Efficient heuristics with worst-case analysis and asymptotic performance analysis are also developed. Numerical results demonstrate that the proposed heuristics can give near-optimal solutions for different production scenarios. 3 - A Lagrangian Approach For Coordinating Capacity Negotiations In A Semiconductor Firm Reha Uzsoy, North Carolina State University, Dept. of Industrial & Systems Engg, 300 Daniels Hall Camps Box 7906, Raleigh, NC, 27695-7906, United States, ruzsoy@ncsu.edu, Ankit Bansal, Karl Kempf We model the negotiations between a product development organization and a production organization for access to manufacturing capacity for product development activities in the semiconductor industry. We develop a negotiation framework based on Lagrangian decomposition that maximizes overall firm contribution subject to the resource constraints of both organizations. The approach aims to achieve coordinated decisions between the two organizations, and provides a benchmark for alternative models of negotiations. 4 - Modeling And Solution For Supply Chain Scheduling In Cold Rolling Shengnan Zhao, PhD Candidate, Northeastern University, Shenyang, China, zhaoshengnan_neu@163.com, Lixin Tang, Qingxin Guo This paper studies a supply chain scheduling problem which is derived from steel production. The problem is to make coil schedules with the aim of balancing the capacity of each production line, and minimizing the total setup cost. To describe the problem, we formulate a MILP model with consideration of practical technological requirements. Then we develop an improved discrete differential evolution (DE) algorithm to solve it. The computational experiments show that the proposed DE algorithm outperforms the compared DE algorithms for solving this problem. In addition, the proposed algorithm is also competitive in comparison with the commercial optimization solver CPLEX. 5 - Analysis Of The Barriers To Implement Additive Manufacturing Technology In The Indian Automotive Sector: A Fuzzy-ISM Approach Gourav Dwivedi, Doctoral Student, Indian Institute of Management, Indian Institute of Management, IIM Road, Off Sitapur Road, Lucknow, 226013, India, fpm14013@iiml.ac.in, Rajiv K Srivastava, Samir K Srivastava This paper analyzes the interaction among barriers to implement additive manufacturing (AM) technology in the Indian automotive sector. We use Fuzzy- Interpretive Structural Modeling (Fuzzy-ISM) method to derive hierarchy and direction and to measure the strength of relations among these barriers. Dominant barriers are identified using this approach. The findings may be useful for managers to develop suitable mitigation strategies. This study contributes to AM literature by the structured presentation of the barriers.

Technologoy Tutorial: Palisade Corporation/Bayesia 1 - Palisade: Introduction To Risk And Decision Analysis Using @RISK And The Decision Tools Suite José Raúl Castro, Palisade Corporation, Ithaca, NY, raul.castro.gc@gmail.com This software presentation is designed to provide an entry-level introduction into probabilistic analysis and will show how Monte Carlo simulation and other techniques can be applied to your everyday business analyses. Using Monte Carlo simulation, @RISK will analyze many different scenarios all at once, giving you more insight into what could happen. We’ll look at example models including a basic revenues/cost/profits model, an NPV model, and a Cost Estimation model, to give you an idea of how quickly you can get started in probabilistic modeling in Excel. If you build models in Excel then Palisade solutions can almost certainly help you to make more informed decisions, right from your desktop. Palisade software and solutions have been used to make better decisions. Cost estimation, NPV analysis, operational risk registers, portfolio analysis, insurance loss modeling, reserves estimation, schedule risk analysis, budgeting, sales forecasting, and demand forecasting are just some of the ways in which the tools are applied. This presentation will demonstrate how easy - and necessary - it is to implement quantitative risk analysis in any business. 2 - Bayesian Networks & BayesiaLab: Artificial Intelligence for Research, Analytics, and Reasoning Stefan Conrady, BAYESIA USA, Franklin, TN, Contact: stefan.conrady@bayesia.us The objective of this workshop is to show that “Artificial Intelligence” should not be perceived as a quasi-magic technology that is mostly incomprehensible to normal mortals. We want to illustrate how scientists in any field of study—rather than only computer scientists—can employ AI to explore complex problems. For this purpose, we present Bayesian networks as the framework and BayesiaLab as the software platform. In this context, we demonstrate BayesiaLab’s supervised and unsupervised machine learning algorithms for knowledge discovery in high- dimensional, unknown domains. Also, while AI is commonly associated with another buzzword, “Big Data”, we wish to prove that AI can be useful for dealing with problems for which we possess little or no data. Here, expert knowledge modeling is critical, and we describe how even a minimal amount of expertise can serve as a basis for sound reasoning aided by AI.

Sunday, 1:30PM - 3:00PM

SC01 101A-MCC Supervised and Unsupervised Methods Sponsored: Data Mining Sponsored Session

Chair: Wenjun Zhou, University of Tennessee, 916 Volunteer Blvd, 255 Stokely Management Center, Knoxville, TN, 37996-0525, United States, wzhou7@gmail.com 1 - Group-wise Sufficient Dimension Reduction: With Applications In Forecasting The Equity Risk Premium Haileab Tesfe Hilafu, University of Tennessee, hhilafu@utk.edu When there is prior domain knowledge concerning a grouping structure of the predictors, two different approaches of dimension reduction exist: carry out dimension reduction of predictors in each group separately which ignores the inter-dependence among the groups; ignore the grouping structure and reduce the dimension of the predictors jointly. We present a method that bridges these two approaches in the sense that it, simultaneously, utilizes the prior domain knowledge and accounts for potential inter-dependence among the groups of predictors. The proposed method is applied to forecast the equity risk premium from a set of well known macroeconomic and a set of technical variables. 2 - Sufficient Dimension Reduction For Treatment Effect Estimation Craig Anthony Rolling, Saint Louis University College for Public Health and Social Justice, 1 North Grand Boulevard, Saint Louis, MO, 63103, United States, rollingca@slu.edu, Wenbo Wu For nonparametric methods of estimating the treatment effect, if the dimension of the baseline covariates is large, implementation becomes difficult and sometimes infeasible due to the curse of dimensionality. Hence, sufficient dimension reduction of baseline covariates can be useful before estimating the treatment effect. We refer to such a dimension reduction subspace as a central treatment effect subspace (CTES). We propose methods to estimate the CTES and its structural dimension, investigate the theoretical properties of these estimators, and demonstrate their effectiveness with numerical studies.

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