scholarly journals Online Risk-Averse Submodular Maximization

2021 ◽  
Author(s):  
Tasuku Soma ◽  
Yuichi Yoshida
Keyword(s):  
Value At Risk ◽  
Online Algorithm ◽  
Submodular Function ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Underlying Distribution ◽  
Online Risk ◽  
On Line ◽  
Real World Datasets ◽  
Submodular Maximization

We present a polynomial-time online algorithm for maximizing the conditional value at risk (CVaR) of a monotone stochastic submodular function. Given T i.i.d. samples from an underlying distribution arriving online, our algorithm produces a sequence of solutions that converges to a (1−1/e)-approximate solution with a convergence rate of O(T −1/4 ) for monotone continuous DR-submodular functions. Compared with previous offline algorithms, which require Ω(T) space, our online algorithm only requires O( √ T) space. We extend our on- line algorithm to portfolio optimization for mono- tone submodular set functions under a matroid constraint. Experiments conducted on real-world datasets demonstrate that our algorithm can rapidly achieve CVaRs that are comparable to those obtained by existing offline algorithms.

scholarly journals Risk-Averse Multi-Stage Stochastic Programming to Optimizing Vaccine Allocation and Treatment Logistics for Effective Epidemic Response

2021 ◽  
Author(s):  
Xuecheng Yin ◽  
Esra Buyuktahtakin
Keyword(s):  
Value At Risk ◽  
Virus Disease ◽  
Risk Measure ◽  
Adverse Outcomes ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Expected Number ◽  
Multi Stage ◽  
Stochastic Mixed Integer Programming

Existing compartmental-logistics models in epidemics control are limited in terms of optimizing the allocation of vaccines and treatment resources under a risk-averse objective. In this paper, we present a data-driven, mean-risk, multi-stage, stochastic epidemics-vaccination-logistics model that evaluates various disease growth scenarios under the Conditional Value-at-Risk (CVaR) risk measure to optimize the distribution of treatment centers, resources, and vaccines, while minimizing the total expected number of infections, deaths, and close contacts of infected people under a limited budget. We integrate a new ring vaccination compartment into a Susceptible-Infected-Treated-Recovered-Funeral-Burial epidemics-logistics model. Our formulation involves uncertainty both in the vaccine supply and the disease transmission rate. Here, we also consider the risk of experiencing scenarios that lead to adverse outcomes in terms of the number of infected and dead people due to the epidemic. Combining the risk-neutral objective with a risk measure allows for a trade-off between the weighted expected impact of the outbreak and the expected risks associated with experiencing extremely disastrous scenarios. We incorporate human mobility into the model and develop a new method to estimate the migration rate between each region when data on migration rates is not available. We apply our multi-stage stochastic mixed-integer programming model to the case of controlling the 2018-2020 Ebola Virus Disease (EVD) in the Democratic Republic of the Congo (DRC) using real data. Our results show that increasing the risk-aversion by emphasizing potentially disastrous outbreak scenarios reduces the expected risk related to adverse scenarios at the price of the increased expected number of infections and deaths over all possible scenarios. We also find that isolating and treating infected individuals are the most efficient ways to slow the transmission of the disease, while vaccination is supplementary to primary interventions on reducing the number of infections. Furthermore, our analysis indicates that vaccine acceptance rates affect the optimal vaccine allocation only at the initial stages of the vaccine rollout under a tight vaccine supply.

Coordinating Supply Chain with Buy-Back Contracts in the Presence of Risk Aversion

2018 ◽  
Vol 35 (02) ◽  
pp. 1840008 ◽  
Author(s):  
Chunlin Luo ◽  
Xin Tian ◽  
Xiaobing Mao ◽  
Qiang Cai
Keyword(s):  
Supply Chain ◽  
Risk Aversion ◽  
Value At Risk ◽  
Performance Measure ◽  
Expected Profit ◽  
Conditional Value At Risk ◽  
Allocation Rule ◽  
Risk Averse ◽  
Buy Back ◽  
The Impact

This paper addresses the operational decisions and coordination of the supply chain in the presence of risk aversion, where the risk averse retailer’s performance is measured by a combination of the expected profit and conditional value-at-risk (CVaR). Such performance measure reflects the desire of the retailer to maximize the expected profit on one hand and to control the downside risk of the profit on the other hand. The impact of risk aversion on the supply chain’s decision and performance is also explored. To overcome the inefficiency due to the double marginalization and the aggravation resulting from risk aversion, we investigate the buy-back contract to coordinate the supply chain. Such contract can largely increase the supply chain’s profit, especially when the retailer is more risk averse. Lastly, we extend such risk measure to the widely-used business model nowadays — platform selling model, and explore the impact of the allocation rule on the manufacturer’s decision.

Optimal Option Purchasing Decisions for the Risk-Averse Retailer with Shortage Cost

2019 ◽  
Vol 36 (02) ◽  
pp. 1940005 ◽  
Author(s):  
Xin-Sheng Xu ◽  
Felix T. S. Chan
Keyword(s):  
Confidence Level ◽  
Value At Risk ◽  
Expected Profit ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Purchasing Decisions ◽  
Shortage Cost ◽  
High Return ◽  
Potential Risks ◽  
Purchase Quantity

To hedge against potential risks, this paper introduces the conditional value-at-risk (CVaR) measure into the option purchasing for the risk-averse retailer with shortage cost. We introduce two models for the risk-averse retailer to select the optimal option purchase quantity. It is found that both two optimal option purchase quantities to two models can be decreasing in the retail price and increasing in the option executing price under certain conditions. This is different from the optimal option purchase quantity for a risk-neutral retailer to maximize the expected profit. It is found that both two optimal option purchase quantities may be increasing or decreasing in the confidence level, which implies a retailer who becomes more risk-averse may purchase more or fewer options to hedge against potential risks. Under both two optimal option purchase quantities, it is proven that the retailer’s expected profit is decreasing in the confidence level. This confirms the fact that high return implies high risk while low risk comes with low return.

SUPPLY CHAIN COORDINATION WITH CVaR CRITERION

2009 ◽  
Vol 26 (01) ◽  
pp. 135-160 ◽  
Author(s):  
LEI YANG ◽  
MINGHUI XU ◽  
GANG YU ◽  
HANQIN ZHANG
Keyword(s):  
Supply Chain ◽  
Value At Risk ◽  
Supply Chain Coordination ◽  
Revenue Sharing ◽  
Retail Price ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Mild Conditions ◽  
Quantity Flexibility ◽  
Buy Back

We study the coordination of supply chains with a risk-neutral supplier and a risk-averse retailer. Different from the downside risk setting, in a conditional value-at-risk (CVaR) framework, we show that the supply chain can be coordinated with the revenue-sharing, buy-back, two-part tariff and quantity flexibility contracts. Furthermore the revenue-sharing contracts are still equivalent to the buy-back contracts when the retail price is fixed. At the same time, it is shown that the risk-averse retailer of the coordinated supply chain can increase its profit by raising its risk-averse degree under mild conditions.

scholarly journals Single-stage gradient-based stellarator coil design: stochastic optimization

2021 ◽  
Author(s):  
Florian Wechsung ◽  
Andrew Giuliani ◽  
M. Landreman ◽  
Antoine J Cerfon ◽  
Georg Stadler
Keyword(s):  
Stochastic Optimization ◽  
Value At Risk ◽  
Optimization Problems ◽  
Numerical Study ◽  
Single Stage ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Coil Design ◽  
Risk Neutral ◽  
Gradient Based

Abstract We extend the single-stage stellarator coil design approach for quasi-symmetry on axis from [Giuliani et al, 2020] to additionally take into account coil manufacturing errors. By modeling coil errors independently from the coil discretization, we have the flexibility to consider realistic forms of coil errors. The corresponding stochastic optimization problems are formulated using a risk-neutral approach and risk-averse approaches. We present an efficient, gradient-based descent algorithm which relies on analytical derivatives to solve these problems. In a comprehensive numerical study, we compare the coil designs resulting from deterministic and risk-neutral stochastic optimization and find that the risk-neutral formulation results in more robust configurations and reduces the number of local minima of the optimization problem. We also compare deterministic and risk-neutral approaches in terms of quasi-symmetry on and away from the magnetic axis, and in terms of the confinement of particles released close to the axis. Finally, we show that for the optimization problems we consider, a risk-averse objective using the Conditional Value-at-Risk leads to results which are similar to the risk-neutral objective.

Role of Risk Aversion in Price Postponement Under Supply Random Yield

2020 ◽  
Author(s):  
Panos Kouvelis ◽  
Guang Xiao ◽  
Nan Yang
Keyword(s):  
Risk Aversion ◽  
Operations Management ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Risk Averse ◽  
Random Yield ◽  
Ex Post ◽  
Responsive Pricing ◽  
Pricing Schemes ◽  
The Impact

Price postponement is an effective mechanism to hedge against the adverse effect of supply random yield. However, its effectiveness and the resulting production decisions have not been studied for risk-averse firms. In this paper, we investigate the impact of price postponement and risk aversion under supply yield risk. Specifically, we study a risk-averse monopoly firm’s production and pricing decisions under supply random yield with two distinct pricing schemes: (1) ex ante pricing in which the firm simultaneously makes the sales price and sourcing decisions before production takes place and (2) responsive pricing in which the pricing decision is postponed until after the production yield realization. We adopt conditional value at risk (CVaR) as the risk-aversion measurement and investigate the impact of the firm’s risk-aversion level on its optimal decisions and the corresponding profit. Among other results, we show that, for each pricing scheme, there exists a unique risk-aversion threshold under which the firm chooses not to produce. Interestingly, price postponement has no impact on the risk-aversion threshold as the cutoff values under both pricing schemes are the same. We further show that the value of CVaR improvement from responsive pricing may not be monotonic in the firm’s risk-aversion level. Consequently, our results indicate that, although price postponement induces operational flexibility by better matching demand with available supply, whether the firm should adopt responsive pricing needs to be carefully evaluated as the benefit may not justify the potential fixed cost associated with price postponement, especially for a highly risk-averse firm. In addition, we show that responsive pricing, even with its ex post revenue-maximization behavior, benefits the end-market consumers in equilibrium. Finally, we conduct extensive numerical studies to check and confirm the robustness of our results. This paper was accepted by Charles Corbett, operations management.

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