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Articles 1 - 3 of 3
Full-Text Articles in Management Sciences and Quantitative Methods
Optimizing Consumer-Centric Assortment Planning Under Cross-Selling Effects, Ameera Ibrahim
Optimizing Consumer-Centric Assortment Planning Under Cross-Selling Effects, Ameera Ibrahim
Doctoral Dissertations
Central to modern-time, consumer-focused retailing is the ability to provide attractive and reasonably-priced product assortments for different customer profiles. To this end, retailers can benefit from the use of data analytics in order to identify distinct customer segments, each characterized by their buying power, shopping behavior, and preferences. Further, retailers can also benefit from a careful examination of alternative procurement options and cost levers associated with products that are considered for inclusion in the assortment. Issues of assortment planning lie at the interface of operations and marketing. Profitable planning trade-offs can be identified using an optimization methodology and are simultaneously …
State Space Modelling Of Dynamic Choice Behavior With Habit Persistence, Kang Bok Lee
State Space Modelling Of Dynamic Choice Behavior With Habit Persistence, Kang Bok Lee
Doctoral Dissertations
In this dissertation, I present a new approach to capturing dependence across time in dynamic choice data. To achieve this, I develop a state space dynamic choice model and a novel algorithm to fit the data. Instead of capturing dependence in outcomes through lagged response variables, referred to as state dependence, I introduce a lagged utility term through the latent state equation. The lagged utility term captures habit persistence, which has not been explored directly in earlier models (Heckman, 1981b). The autoregressive nature of the lagged utility provides a significantly richer summary of prior utility than a lagged outcome variable. …
Indefinite Knapsack Separable Quadratic Programming: Methods And Applications, Jaehwan Jeong
Indefinite Knapsack Separable Quadratic Programming: Methods And Applications, Jaehwan Jeong
Doctoral Dissertations
Quadratic programming (QP) has received significant consideration due to an extensive list of applications. Although polynomial time algorithms for the convex case have been developed, the solution of large scale QPs is challenging due to the computer memory and speed limitations. Moreover, if the QP is nonconvex or includes integer variables, the problem is NP-hard. Therefore, no known algorithm can solve such QPs efficiently. Alternatively, row-aggregation and diagonalization techniques have been developed to solve QP by a sub-problem, knapsack separable QP (KSQP), which has a separable objective function and is constrained by a single knapsack linear constraint and box constraints. …