On the basis of previous studies about lactic acid (LA) production from food waste (‘cooked’ waste from canteens and restaurants), this study explored LA production from kitchen garbage (KG, ‘raw’ waste from household kitchens) for the first time. Considerable pretreatment method for...

In transportation service procurement, total quantity discounts offered by a strategic carrier are incentives to a shipper. While the shipper reduces total cost by activating discounts, the carrier can win a valuable contract as considerable freight volume is assigned. Moreover, a strategic...

We consider the problem of demand forecasting for long-lived products when a new product is introduced. This problem is challenging in part because sales data is limited or non-existant for the new product. In addition, a new product can change the demand for the existing products through...

While solutions of Distributionally Robust Optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the Sample Average Approximation (SAA), there is no guarantee. In this paper, we introduce the class of Distributionally Optimistic Optimization (DOO) models, and...

Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose...

We formulate a distributionally robust optimization problem where the empirical distribution plays the role of the nominal model, the decision maker optimizes against a worst-case alternative, and the deviation of the alternative distribution from the nominal is controlled by a $\phi$-divergence...

We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a φ-divergence penalty in the objective, and show that a large class of these problems are essentially equivalent to a mean-variance problem. We also show that while...

We study the out-of-sample properties of robust empirical optimization problems with smooth φ-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the non-negative “robustness parameter” δ that controls the size of the deviations...

This paper concerns dynamic pricing of multiple perishable products when there is model uncertainty, which we formulate as a worst-case stochastic intensity control problem where ambiguity is modeled using the notion of relative entropy. One feature of our formulation is that the demand models...