Informs Annual Meeting 2017

WB19

INFORMS Houston – 2017

WB19

WB20

342A Production and Scheduling Contributed Session Chair: Zhengyang Hu, Iowa State University, Ames, IA, United States, zhengya@iastate.edu 1 - Optimal Integration of Solar Power to Minimize Operational Costs and Carbon Dioxide Footprint in a Production System Samuel Trevino, PhD Graduate Research Assistant, University of Tennessee, 525 John D. Tickle Engineering Building, 851 Neyland Dr., Knoxville, TN, 37996, United States, strevin1@vols.utk.edu Production scheduling problems have generally been studied from a lean manufacturing perspective, however, energy consumption and environmental footprint have not been completely considered. To fill this gap, an optimal production planning framework for a production system is presented utilizing on- site solar power generation to minimize its energy consumption costs and CO2 footprint. Integrating uncertainty in both equipment reliability and customer demand, the multi-objective optimization model presents insights into the economically feasible solar power capacity to be installed and how production inventory planning can offset solar source uncertainty. 2 - A Client Server Framework for a Job Shop Scheduling System to Minimize Tardiness and Labor Overtime Costs under Labor and Machine Constraints Simultaneous scheduling of machine and labor in a job shop is a NP hard problem. In this study, we developed a system for minimizing tardiness and overtime labor costs in a job shop style service manufacturing facility, while incorporating client server framework for schedule visibility. The core of the optimization engine is a double sequenced simulated annealing algorithm. The model includes various machine and labor constraints including sequence dependent setups, labor availability and overtime regulations, and dispatching rules for the different parallel machine centers. 3 - Integrated Ordering and Scheduling Optimization for Make-to-order Supply Chains under Uncertainty: A Stochastic Programming Approach Yue Sha, Tsinghua University, Tsinghua University, Beijing, Beijing, 10084, China, shay15@mails.tsinghua.edu.cn, Hui Cao Customer driven supply chain management is recognized as a viable approach to the rapid changing market demand. Yet the coordination strategies have been little studied to align supply chain operations on a make-to-order basis. This work tries to integrate the material ordering and job scheduling decisions considering uncertain processing times and resource consumptions. The problem is formulated as a stochastic integer program incorporating decision dependent uncertainties. With the proposed approximation algorithm, numerical experiments show that the integrated policy significantly reduces the total cost. 4 - Robust Project Scheduling Integrated with Materials Ordering under Uncertain Environment Yan Zhang, Doctoral Candidate, Huazhong University of Science and Technology, Hongshan District, 1037 Luoyu Road, Wuhan, China, zhangyan0621@hust.edu.cn, Nanfang Cui Project Scheduling and Materials Ordering Problem (PSMOP) generates not only the project schedule but also the corresponding martials ordering plan. In our study, we consider the uncertainty of activity durations and propose a robust project schedule. We first get a baseline schedule with deterministic activity durations via genetic algorithm, basing on which a creative robust schedule is obtained by an iteration algorithm under uncertain circumstances. Finally, under different project uncertainties, computational experiments are conducted to compare the two schedules and we show that the robust schedule performs better. 5 - Hybrid Lot-sizing and Scheduling Production under Uncertainties Zhengyang Hu, Iowa State University, 3020 Regency Ct, Unit 68, Ames, IA, 50010-2701, United States, zhengya@iastate.edu, Guiping Hu Lot-sizing and scheduling, one of the medium-term production problems in assembly manufactories, is universally challenged with the need to reduce cost and improve customer satisfaction. A hybrid decision making model is proposed to address the combination of uncertainties on the shop floor considering real time information. Stochastic programming is adopted for random machine failure time and robust optimization is utilized to address the uncertain demand. Sample average approximation is introduced to generate discrete scenarios. The model is able to minimize the overall production cost as well as adjust satisfaction of customers based on the market environment. Weihang Zhu, Associate Professor, Lamar University, PO Box 10032, Beaumont, TX, 77710, United States, weihang.zhu@lamar.edu, Sujay Mahale, Alberto Marquez

342B Information Systems Contributed Session Chair: Lifei Sheng, University of British Columbia, Vancouver, BC, Canada, fay.sheng@sauder.ubc.ca 1 - Cloud Deployment Model and Impact on Provider’s and Users’ Incentives to Invest in Security Mingwen Yang, University of Texas-Dallas, Richardson, TX, United States, mxy131030@utdallas.edu, Varghese S. Jacob, Srinivasan Raghunathan Cloud computing has emerged as a key strategy to meet the IT needs of firms. Despite its benefits, cloud computing’s adoption and growth is hindered by information security concerns. The security of a cloud is the joint responsibility of the cloud provider and cloud users, but the extent to which the provider and users can affect the cloud security through their efforts depends on the cloud deployment model. In this study, we focus on how the cloud deployment model affects the incentives of the cloud provider and users to invest in security, which, in turn, affects the price charged by the cloud provider, the demand, and the provider’s profit. 2 - Understanding Determinants of Idea Quality in On-line Communities Orcun Temizkan, Assistant Proffesor, Ozyegin University, Cekmekoy Campus, Nisantepe Mah. Orman Sok., Istanbul, 34794, Turkey, orcun.temizkan@ozyegin.edu.tr, Ram Kumar This research proposes a model to understand determinants of Idea Quality in on- line communities. The proposed model integrates multiple types of variables including creator (idea generator) characteristics, contributor (idea developer) characteristics and idea characteristics. Theories from the organization and innovation literature are used to develop the model. Empirical results using data from Stack Overflow will be presented. 3 - Once Bitten, Twice Shy? Examining Customer Responses to Stolen Customer Data John N. Angelis, Assistant Professor, Elizabethtown College, 1 Alpha Drive, Elizabethtown, PA, United States, angelisj@etown.edu, Joseph C.Miller Data breaches represent an increasing nuisance for companies. Nonetheless, the impact on future business remains underinvestigated. We surveyed 400 individuals with four unique real-world data breaches. The nuances of each case differed in two dimensions: 1) localization of the breach and 2) individualization of affected customers. We use critical components of both the Theory of Reasoned Action (TRA) and Technology Acceptance Model (TAM) in our model of business revisits. Localization has a significant effect on company opinions, while individualization has a significant effect on attitudes about the crime. We find that mainly opinions about the company have a significant effect on revisits. 4 - Innovation vs. Security: Customer Demand Reaction to Adverse Events and Industry Innovation Melanie Lisa Yeo, Assistant Professor, University of California- We examine the tradeoff between product innovation and the risk of security breaches in digitally-enabled products. The demand for a product, such as a mobile phone, is often influenced by innovations in features. We explore the trade-off between investing in product features and security. Using a continuous- time markov chain model, we explore the impact of differing customer demand reactions to security breaches on feature investment. 5 - Designing Services in the Presence of Customer Churn Lifei Sheng, University of British Columbia, Sauder School of Business, 2053 Main Mall, Vancouver, BC, V6T.1Z2, Canada, fay.sheng@sauder.ubc.ca, Christopher Ryan We explore how to design experiential services, in particular determine the quality and sequence of activities, to maximize customer satisfaction. We assume that customers are subject to memory decay and are free to quit the service at any time. This model fits experiential goods that must first be experienced by users before their value is understood. Examples include freemium software where the goal is maximize customer utility by the end of some initial phase subject to the possibility of early quitting. We show that the quality of the experience should be scheduled in a U-shape. The quitting option and memory decay lead to high service quality at the beginning and at the end of the trial period. Merced, Merced, CA, 95340, United States, melanie.yeo@gmail.com, Raymond Patterson, Jacquelyn Rees Ulmer, Erik Rolland

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