Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WD78

3 - Dynamic Tracking of Software Errors: A Big Data Approach John G. Wilson, Professor, Ivey Business School, 1255 Western Road, London, ON, N6G ON1, Canada, Dov Te’eni In the internet age, a proliferation of services appear on the web. Errors in using the internet service or app are dynamically introduced as new devices/interfaces/software are produced. The number of users who can detect various errors changes dynamically. Allowing new users and errors to enter dynamically poses considerable modeling and estimation difficulties. In the era of Big Data, methods for dynamically updating as new observations arise are important. We provide a general model that allows for a procedure for finding maximum likelihood estimators of key parameters where the number of errors and the number of users can change. 4 - Aggregate Production Planning in Stochastic Manufacturing Systems: A Simulation Study Gerd J. Hahn, Professor, German Graduate School of Management and Law, Bildungscampus 2, Heilbronn, 74076, Germany This paper studies the issue of integrated capacity and batch planning in stochastic manufacturing systems. Classical mathematical programming-based approaches to production planning typcially omit the impact of variability on the performance of manufacturing systems. Therefore, a hierarchical planning approach is presented that integrates a queuing network model to anticipate the stochastic behavior of the manufacturing system. A simulation study is conducted to show the benefit of this approach using a real-life case example. 5 - A Queueing System with Disaster Events under Different Policies George C. Mytalas, CUNY, New York, NY, 11209, United States We consider an M/G/1 queueing system with batch arrivals subject to disasters and server breakdowns under N-policy. The server is turned off as soon as the system empties. When the queue length reaches or exceeds a value N (threshold), the server is turned on and begins to serve the customers. When a disaster occurs the system is cleared of all customers and the server initiates a repair period. During the repair period arriving batches of customers accumulate in the queue without receiving service. Besides, the server has an exponential lifetime in addition to the catastrophe process.

n WD78 West Bldg 213B Humanitarian Operations Management Sponsored: Public Sector OR Sponsored Session Chair: Alfonso Pedraza Martinez Co-Chair: Telesilla Olympia Kotsi, Bloomington, IN, 47401, United States 1 - Food Aid Modality Selection Problem Feyza Guliz Sahinyazan, McGill University, Desautels Faculty of Management, Bronfman Building, 1001 Rue Sherbrooke O, Montreal, QC, H3A 1G5, Canada, Marie-Eve Rancourt, Vedat Verter Delivering food aid to rural areas requires significant operational effort by the aid agencies. Empirical evidence shows that implementing cash and voucher programs can decrease the logistics costs, improve the nutritional outcomes, and contribute to the local economy. In this paper, we develop a model that selects the aid modality by measuring the improvements in these three program objectives. We also incorporate the consumption behaviour of the beneficiaries in a bilevel optimization model structure to capture their cash spending preferences and solve the problem for Garissa county of Kenya. 2 - Allocation of Nonprofits’ Funds among Program, Fundraising, and Administration Telesilla Olympia Kotsi, Bloomington, IN, 47401, United States, Goker Aydin, Alfonso J. Pedraza-Martinez Nonprofits allocate their budget among three types of expenses: program spending to deliver services to beneficiaries; fundraising spending to raise donations; and administration spending to build infrastructure. We analyze how a nonprofit can balance the immediate reward of program spending versus the future reward of fundraising and administration spending. We determine when program spending becomes relatively more attractive, perhaps at the expense of fundraising and administration spending, and vice versa. Our case study uses IRS data to show that budget allocation decisions reflect nonprofits’ choices about the most appropriate type of assistance to help beneficiaries.

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