Informs Annual Meeting 2017

MA54

INFORMS Houston – 2017

Srinivas Peeta This study investigates travelers’ satisfaction with travel experience including the benefits from travel time savings and psychological effects while traveling under real-time travel information provision. A structural equation model is proposed with latent variables for human factor considerations in information perception and multiple dimensions of psychological benefits. Travelers’ behavioral data and stated preference data collected through interactive driving simulator experiments and the associated surveys are used to estimate the model.

It may overestimate its potential of the particular location if simply adding up different energy sources’ potentials. A new approach is proposed to estimate the renewable energy potential of a particular location by considering the influences among different renewable energy sources during their harvesting processes. The new approach also considers possible option of harvesting different types of renewable energy sources at the same time to seek for the best combination and highest potential of a particular location. 5 - Ex-post Stable and Fair Payoff Allocation for Renewable Energy Aggregation Yue Zhao, Stony Brook University, 261 Light Engineering, Stony Brook, NY, 11794, United States, yue.zhao.2@stonybrook.edu, Hossein Khazaei Aggregating statistically diverse renewable power producers (RPPs) is an effective way to reduce the uncertainty of the RPPs. The key question in aggregation of RPPs is how to allocate payoffs among them. In this paper, a payoff allocation mechanism (PAM) with a simple closed-form expression is proposed: It achieves stability (in the core) and fairness both in the “ex-post” sense, i.e., for all possible realizations of renewable power generation. Furthermore, this PAM can in fact be derived from the competitive equilibrium in a market. The proposed PAM is evaluated in a simulation study with ten wind power producers in the PJM interconnection. 361F Traveler Information Provision and its Impacts Invited: TSL, Intelligent Transportation Systems (ITS) Invited Session Chair: Dong Yoon Song, PhD, Purdue University, School of Civil Engineering, West Lafayette, LA, 47907, United States, song50@purdue.edu 1 - A Two-stage Strategy for Improving the Efficiency of a Traffic Network by Providing Value-added Atis Services Jingjing Liang, Miss, Tongji University, Room 1413, Tongji Buliding Block A, Zhangwu Road, Shanghai, 200082, China, gina_liang@tongji.edu.cn, Xiaoning Zhang From the perspective of the traffic management agency, we propose a two-stage strategy for improving the efficiency of a traffic network by providing VATIS services. In the first stage, the traffic management agency selects several ATIS service providers, then offers seed fund to each of them for encouraging VATIS services providing in traffic networks. In the second stage, the traffic management agency announces a fixed target of the total market penetration of VATIS services, and asks service providers to work together to achieve it. If they could complete or partially complete the task, compensations will be paid or partially paid. 2 - Most Reliable Flight Itinerary Problem Michael Redmond, University of Iowa, Iowa City, IA, 52242, United States, michael-a-redmond@uiowa.edu, Ann Melissa Campbell, Jan Ehmke With the high amount of uncertainty in airline travel, our research aims to provide airline customers with information about the reliability of their itineraries and help identify the Most Reliable Flight Itinerary (MRFI). We use data from the U.S. Bureau of Transportation to model the distribution of departure and arrival times for flights. We use these distributions to compute the reliability of flight itineraries, which is the likelihood the itinerary will get the passenger to the destination given a start time and travel time budget. We implement a network search algorithm to find the most reliable itinerary and utilize a number of shortcuts in order to find this itinerary in an efficient way. 3 - Quantifying Impacts of Real-time Travel Information on Route Choice Behavior using Psychophysiological Analysis: A Driving Simulator-based Study Shubham Agrawal, Purdue University, West Lafayette, IN, 47907, United States, shubham@purdue.edu, Irina Benedyk, Dong Yoon Song, Srinivas Peeta This study develops an analytical framework to quantify the impacts of real-time travel information in driver’s route choice decision-making process considering information characteristics (for example, amount, content and source), driver’s cognitive state (for example, engagement level and mental workload), situational factors and individual characteristics (for example, age and gender). The driver’s cognitive state is estimated by performing psychophysiological analysis based on the data collected using electroencephalogram (EEG), electrocardiogram (ECG) and wearable eye tracking glasses while driving in an interactive driving simulator environment. 4 - Understanding Satisfaction with Travel Experience under Real-Time Travel Information Provision - A Latent Variable Modeling Approach Dong Yoon Song, PhD Student, Purdue University, West Lafayette, IN, 47907, United States, song50@purdue.edu, MA53

MA54

362A Food Supply Chain Innovations Invited: Agricultural Analytics Invited Session Chair: Changrui Ren, IBM Research - China, IBM, Beijing, 100193, China, rencr@cn.ibm.com 1 - A Spatiotemporal Approach to Accelerate the Investigation of Food-borne Illness Outbreak using Retail Scanner Data Kun Hu, IBM.Research, 5224 union ave, San Jose, CA, 95124, United States, khu@us.ibm.com, Shanshan Cao, James Kaufman Nowadays, foodborne illness caused by salmonella, E. Coli and norovirus is a global challenge due to an altered worldwide food supply chain and increased emergence of new foodborne pathogens. When prevention efforts fail, it is essential to rapidly identify the contaminated product and alert the public. The aim of this study is to demonstrate a new analytic approach that employed big data from retail scanner. After receiving as few as 10 lab confirmed case reports, it is possible to narrow to a subset of 12 suspect products with 90% accuracy for over 80% of food products in the dataset. 2 - Blockchain Enabled Food Supply Chain Traceability and Safety Management Changrui Ren, IBM.Research - China, Building 19 Zhongguancun Software Park, 8 Dongbeiwang Westroad, Haidian District, Beijing, 100193, China, rencr@cn.ibm.com, Bo Zhang, Dingding Lin, Yongqing Xue, Xin Shi, Mingchao Wan, Miao He Today’s food supply chain is longer and more complex than at any time in history. There is an urgent need for transparency. In our research, we propose a new blockchain enabled approach to construct food supply chain traceability and safety management system. A pilot study has been conducted in China with collaboration from industrial partners, the result of which demonstrate the authenticity, trust and robustness brought by the blockchain technology to the traceability system. 3 - Raw Material Variability in Food Manufacturing Brian Bourquard, Purdue University, West Lafayette, IN, United States, bbourqua@purdue.edu, Gemma Berenguer, Allan W. Gray, Paul V. Preckel We characterize the operational decisions of a food manufacturing firm facing raw material variability, that is uncertainty about the characteristics of the raw material inputs to the production process. We link the operational decisions to the firm’s strategic decisions, such as supply chain management and firm boundaries. Our goal is to provide managerial insights into how firms facing raw material variability consider and select among their strategic options to improve efficiency and reduce waste. We contribute to the literature by showing that firms facing operational challenges need to consider their strategic options as integral to the manufacturing process. 4 - The Application of Lateral Transshipment to Perishable Inventory Management in Fresh Food Industry Dilupa Nakandala, Western Sydney University, Paramatta, Australia, D.Nakandala@westernsydney.edu.au, Henry Lau Product perishability is one of the primary reasons causing more stockouts across the fresh food supply chain and justifies considering lateral transshipment as a managerial response. The proposed model considered the four key cost components of purchasing, backordering, holding, and spoilage costs for minimising the total inventory costs. The optimal balance of lateral transshipment and supplier order can moderate the tendency to over-order. Otherwise, the associated higher level of inventory position to manage any emerging stockout would result in higher spoilage costs as a higher volume of perishable product expires, but without improving the customer service level.

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