Invited Session Wed.2.H 0106

Wednesday, 13:15 - 14:45 h, Room: H 0106

Cluster 13: Logistics, traffic, and transportation [...]

Math programming in supply chain applications

 

Chair: Pavithra Harsha

 

 

Wednesday, 13:15 - 13:40 h, Room: H 0106, Talk 1

Paat Rusmevichientong
Robust assortment optimization

Coauthor: Huseyin Topaloglu

 

Abstract:
We study robust formulations of assortment optimization problems under the multinomial logit choice model. The true parameters of the logit model are assumed to be unknown, and we represent the set of likely parameter values by a compact uncertainty set. The objective is to find an assortment that maximizes the worst case expected revenue over all parameter values in the uncertainty set. We give a complete characterization of the optimal policy in both settings, show that it can be computed efficiently, and derive operational insights. We also propose a family of uncertainty sets that enables the decision maker to control the tradeoff between increasing the average revenue and protecting against the worst case scenario. Numerical experiments show that our robust approach, combined with our proposed family of uncertainty sets, is especially beneficial when there is significant uncertainty in the parameter values.

 

 

Wednesday, 13:45 - 14:10 h, Room: H 0106, Talk 2

Maxime Cohen
Designing consumer subsidies with industry response for green technology adoption

Coauthors: Ruben Lobel, Georgia Perakis

 

Abstract:
The recent developments in green technologies would not have been possible without the subsidies offered by the government to consumers. While the government designs subsidies to stimulate adoption of new technologies, the manufacturing industry responds to these policies with the goal to maximize profits. In this talk, we study how government should set subsidies when considering the industry's response. More specifically, the supplier adjusts its production quantities and price depending on the level of subsidies offered by the government. In this setting, we expand the understanding of the price-setting newsvendor model, incorporating the external influence from the government who is now an additional player. We consider a model with a general demand function and quantify how uncertainty impacts the system relative to ignoring stochasticity and considering an average case analysis. By assuming that the deterministic part of the demand is a non-increasing and convex function of the effective price, we show that when demand uncertainty increases, quantities produced are higher whereas prices and supplier's profits are lower. Finally, we study the efficiency of this supply chain.

 

 

Wednesday, 14:15 - 14:40 h, Room: H 0106, Talk 3

Pavithra Harsha
Demand-response in the electricity smart grid: A data-driven pricing and inventory optimization approach

Coauthors: Ramesh Natarajan, Dharmashankar Subramanian

 

Abstract:
Demand response schemes based on dynamic pricing are of considerable interest in the emerging smart grid. For instance, an electric utility can optimize its operational objectives by providing certain “price incentive signals” to consumers, so as to minimize generation, spinning reserve and salvage costs, and revenue shortfalls, while simultaneously satisfying the resulting stochastic responsive demand. Although perhaps not well known and widely used, this demand-management problem can be formulated as a classical price-sensitive, newsvendor model, but with several enhancements to make it applicable to the smart grid context. A major concern is the interaction of multiple drivers of demand, including weather, time-of-day, and seasonality, in addition to the type and form of the incentive signals. We consider a novel approach that is based on the use of quantile and mixed quantile regression to jointly estimate the optimal stocking level and pricing signals. This approach is data-driven, distribution-free, and makes best use of the sparse, high-dimensional demand data. We illustrate its efficacy, robustness and accuracy over possible alternatives with computational examples.

 

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