The ranking-based choice model is a popular model in revenue management for predicting demand for a firm’s products based on the assortment of products that the firm offers to their customers. Because this model has a huge number of parameters, many different ranking-based choice models can be...

The integration of genetic algorithms to optimize the networks of value chains could enormously improve the performance of supply chains. For this reason, this paper describes in more detail the application of genetic algorithms in the value chains of the automotive industry. For this purpose, a...

The chaos of flow turbulence causes most quantity-of-interests J to become significantly non-convex over time, so that gradients of J soon fail to guide numerical simulations toward a useful local optimum. This challenge is investigated, and a multi-step penalty method is introduced to...

We address two important concerns faced by assortment managers, namely constrained assortment optimization and assortment personalization. We contribute to addressing these concerns by developing bounds and heuristics based on auxiliary multinomial logit (MNL) models. More precisely, we first...

A central problem in planning production capacity is how to effectively manage demand risk. We develop a model that integrates capacity planning and risk hedging decisions under a popular risk measure, conditional value at risk (CVaR). The CVaR objective generalizes the usual risk-neutral...

Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for solving computationally-demanding stochastic optimal stopping problems with known probability distributions. The...

Elective surgery patients have surgery in the operating room (OR), and then recover in one or more downstream recovery units for several consecutive hours or days after surgery. Upstream scheduling that focuses on OR alone or a resource-constrained scheduling approach that fails to account for...

Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MINLP) problem. To solve this constrained...