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Essays On Empirical Operations Management In Retail Sales And Service, Youran Fu

Publicly Accessible Penn Dissertations

This dissertation studies empirical operational problems in retail sales and service systems through three essays. We are in the middle of a remarkable rise in data analytics as available data, as well as the capability of artificial intelligence grows exponentially. This dissertation demonstrates how we can use traditional econometrics and cutting-edge machine learning models to provide data analytics for retail operations management. The first essay studies the value of unstructured social media text data in forecasting future fashion demands several months out, at a granular style-color level. Using recent advancement in natural language processing and machine learning techniques, we show …

Resource-Efficient Scheduling Of Multiprocessor Mixed-Criticality Real-Time Systems, Jaewoo Lee

Publicly Accessible Penn Dissertations

Timing guarantee is critical to ensure the correctness of embedded software systems that

interact with the physical environment. As modern embedded real-time systems evolves,

they face three challenges: resource constraints, mixed-criticality, and multiprocessors. This

dissertation focuses on resource-efficient scheduling techniques for mixed-criticality systems

on multiprocessor platforms.

While Mixed-Criticality (MC) scheduling has been extensively studied on uniprocessor plat-

forms, the problem on multiprocessor platforms has been largely open. Multiprocessor al-

gorithms are broadly classified into two categories: global and partitioned. Global schedul-

ing approaches use a global run-queue and migrate tasks among processors for improved

schedulability. Partitioned scheduling approaches use per …

Unveiling Hidden Values Of Optimization Models With Metaheuristic Approach, Ann Kuo

Publicly Accessible Penn Dissertations

Considering that the decision making process for constrained optimization problem is based on modeling, there is always room for alternative solutions because there is usually a gap between the model and the real problem it depicts. This study looks into the problem of finding such alternative solutions, the non-optimal solutions of interest for constrained optimization models, the SoI problem. SoI problems subsume finding feasible solutions of interest (FoIs) and infeasible solutions of interest (IoIs). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has …