Tiffany Bayley is an Assistant Professor in Management Science at the Ivey Business School.
Recent Refereed Articles
Bayley, T.; Hurst, A.,
2018, "Teaching Line Balancing through Active and Blended Learning*", Decision Sciences Journal of Innovative Education, April 16(2): 82 - 103.
Abstract: The design of balanced assembly lines, especially when considering workforce, material, and cycle time factors, is an important managerial decision-making activity in manufacturing firms. Numerous active learning activities are available to assist instructors in teaching assembly line balancing to students. While effective in improving student engagement, they require considerable planning and expense on the part of instructors, and they may be difficult to implement in inflexible teaching spaces and lecture-oriented curricula. We present a new approach to teaching line balancing using online videos depicting an assembly process. Students design an assembly line by determining themselves how to separate and time tasks, rather than by modifying an existing configuration. To save valuable classroom time, students complete a portion of the activity outside of class. This blended learning approach allows for all students to be engaged in the activity, both in and out of class. Furthermore, a controlled study showed that compared to the traditional lecture format, it better equips students to address less tangible aspects of line balancing, such as ergonomic and workforce factors, material handling considerations, and changing cycle time. With the online content for this activity completely developed and available, other instructors can easily implement this approach within their courses.
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Bayley, T.; Süral, H.; Bookbinder, J. H.,
2018, "A hybrid Benders approach for coordinated capacitated lot-sizing of multiple product families with set-up times", International Journal of Production Research, February 56(3): 1326 - 1344.
Abstract: We examine a coordinated capacitated lot-sizing problem for multiple product families, where demand is deterministic and time-varying. The problem considers set-up and holding costs, where capacity constraints limit the number of individual item and family set-up times and the amount of production in each period. Using a strong reformulation and relaxing the demand constraints, we improve both the upper and lower bounds using a combination of Benders decomposition and an evolutionary algorithm, followed by subgradient optimisation. Through computational experiments, we show that our method consistently achieves better bounds, reducing the duality gap compared to other single-family methods studied in the literature.
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