scholarly journals Viable Medical Waste Chain Network Design by Considering Risk and Robustness

2021 ◽  
Author(s):  
Reza Lotfi ◽  
Bahareh Kargar ◽  
Alireza Gharehbaghi ◽  
Gerhard-Wilhelm Weber
Keyword(s):  
Network Design ◽  
Cost Function ◽  
Value At Risk ◽  
Medical Waste ◽  
Conditional Value At Risk ◽  
Production Cycle ◽  
Recovery Coefficient ◽  
Energy And Environment ◽  
Increasing Demand ◽  
The Cost

Abstract Medical Waste Management (MWM) is an important and necessary problem in the COVID-19 situation for treatment staff. When the number of infectious patients grows up and amount of MWMs increases day by day. We present Medical Waste Chain Network Design (MWMCND) that contains Health Center (HC), Waste Segregation (WS), Waste Purchase Contractor (WPC) and landfill. We propose to locate WS to decrease waste and recover them and send them to the WPC. Recovering medical waste like metal and plastic can help the environment and return to the production cycle. Therefore, we proposed a novel Viable MWCND by a novel two-stage robust stochastic programming that considers resiliency (flexibility and network complexity) and sustainable (energy and environment) requirements. Therefore, we try to consider risks by Conditional Value at Risk (CVaR) and improve robustness and agility to demand fluctuation and network. We utilize and solve it by GAMS CPLEX solver. The results show that by increasing the conservative coefficient, the confidence level of CVaR and waste recovery coefficient increases cost function and population risk. Moreover, increasing demand and scale of the problem make to increase the cost function.

Supply Chain Network Design Optimization with Risk-Averse Retailer

2017 ◽  
Vol 10 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Hêriş Golpîra
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Value At Risk ◽  
Realistic Model ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Integer Linear Programming Problem ◽  
Data Driven Approach

This paper proposes a model to formulate a supply chain network design (SCND) problem against uncertainty. The objective of the model is to minimize total cost of the network. The model employs risk averseness of retailers to obtain more realistic model regarding uncertain demand. Using Conditional Value at Risk (CVaR) to deal with this uncertainty makes the model to be robust. In this way, data-driven approach is used to avoid any distributional assumptions because realizations of uncertain parameters are the only information obtainable. This approach reformulates the initial uncertain model as a mixed integer linear programming problem. Numerical results show that the proposed model is efficient for robust SCND with respect to retailers risk averseness.

Application of Portfolio Theory Based on CVaR in Determining Optimal Spinning Reserve with Consideration of Load and Wind Power Uncertainties

2013 ◽  
Vol 724-725 ◽  
pp. 649-654
Author(s):  
Jun Li Wu ◽  
Bu Han Zhang ◽  
Zhen Yin Xiao ◽  
Kui Wang
Keyword(s):  
Power System ◽  
Wind Power ◽  
Value At Risk ◽  
Efficient Frontier ◽  
Portfolio Theory ◽  
Conditional Value At Risk ◽  
Power Prediction ◽  
Spinning Reserve ◽  
The Cost ◽  
Installed Capacity

With the increased installed capacity of wind power in power system, determining optimal spinning reserve capacity is one of the most important problems in operation of electricity power system. CVaR (conditional value at risk) is introduced to calculate the risk of the cost associated with load shed and abandoning wind power with the consideration of load and wind power prediction uncertainties. Portfolio theory based on CVaR is used to build the Cost-CVaR model. Efficient frontier, which can support the system operators (SO) with the decision of optimal spinning reserve, can be obtained by solving the Cost-CVaR model. The analysis of RTS example can demonstrate the usefulness and efficiency of the model.

Robust bi-level risk-based optimal scheduling of microgrid operation against uncertainty

2020 ◽  
Vol 54 (4) ◽  
pp. 993-1012 ◽  
Author(s):  
Hêriș Golpîra ◽  
Salah Bahramara ◽  
Syed Abdul Rehman Khan ◽  
Yu Zhang
Keyword(s):  
Risk Aversion ◽  
Value At Risk ◽  
Goodness Of Fit ◽  
Distribution Functions ◽  
Optimal Scheduling ◽  
Conditional Value At Risk ◽  
Optimization Approach ◽  
Goodness Of Fit Tests ◽  
The Cost ◽  
Initial Objective

The model introduced in this paper is the first to propose a decentralized robust optimal scheduling of MG operation under uncertainty and risk. The power trading of the MG with the main grid is the first stage variable and power generation of DGs and power charging/discharging of the battery are the second stage variables. The uncertain term of the initial objective function is transformed into a constraint using robust optimization approach. Addressing the Decision Maker’s (DMs) risk aversion level through Conditional Value at Risk (CVaR) leads to a bi-level programming problem using a data-driven approach. The model is then transformed into a robust single-level using Karush–Kahn–Tucker (KKT) conditions. To investigate the effectiveness of the model and its solution methodology, it is applied on a MG. The results clearly demonstrate the robustness of the model and indicate a strong almost linear relationship between cost and the DMs various levels of risk aversion. The analysis also outlines original characterization of the cost and the MGs behavior using three well-known goodness-of-fit tests on various Probability Distribution Functions (PDFs), Beta, Gumbel Max, Normal, Weibull, and Cauchy. The Gumbel Max and Normal PDFs, respectively, exhibit the most promising goodness-of-fit for the cost, while the power purchased from the grid are well fitted by Weibull, Beta, and Normal PDFs, respectively. At the same time, the power sold to the grid is well fitted by the Cauchy PDF.

OPTIMAL REINSURANCE WITH LIMITED CEDED RISK: A STOCHASTIC DOMINANCE APPROACH

2013 ◽  
Vol 44 (1) ◽  
pp. 103-126 ◽  
Author(s):  
Yichun Chi ◽  
X. Sheldon Lin
Keyword(s):  
Stochastic Dominance ◽  
Value At Risk ◽  
Piecewise Linear ◽  
Optimization Criterion ◽  
Conditional Value At Risk ◽  
Convex Order ◽  
Optimal Reinsurance ◽  
Reinsurance Treaty ◽  
Stop Loss ◽  
The Cost

AbstractAn optimal reinsurance problem from the perspective of an insurer is studied in this paper, where an upper limit is imposed on a reinsurer's expected loss over a prescribed level. In order to reduce the moral hazard, we assume that both the insurer and the reinsurer are obligated to pay more as the amount of loss increases in a typical reinsurance treaty. We further assume that the optimization criterion preserves the convex order. Such a criterion is very general as most of the criteria for optimal reinsurance problems in the literature preserve the convex order. When the reinsurance premium is calculated as a function of the actuarial value of coverage, we show via a stochastic dominance approach that any admissible reinsurance policy is dominated by a stop-loss reinsurance or a two-layer reinsurance, depending upon the amount of the reinsurance premium. Moreover, we obtain a similar result to Mossin's Theorem and find that it is optimal for the insurer to cede a loss as much as possible under the net premium principle. To further examine the reinsurance premium for the optimal piecewise linear reinsurance policy, we assume the expected value premium principle and derive the optimal reinsurance explicitly under (1) the criterion of minimizing the variance of the insurer's risk exposure, and (2) the criterion of minimizing the risk-adjusted value of the insurer's liability where the liability valuation is carried out using the cost-of-capital approach based on the conditional value at risk.

scholarly journals A New Portfolio Rebalancing Model with Transaction Costs

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Meihua Wang ◽  
Cheng Li ◽  
Honggang Xue ◽  
Fengmin Xu
Keyword(s):  
At Risk ◽  
Transaction Costs ◽  
Cost Function ◽  
Transaction Cost ◽  
Value At Risk ◽  
Financial Data ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Rebalancing ◽  
Proposed Model

A portfolio rebalancing model with self-finance strategy and consideration of V-shaped transaction cost is presented in this paper. Our main contribution is that a new constraint is introduced to confirm that the rebalance necessity of the existing portfolio needs to be adjusted. The constraint is constructed by considering both the transaction amount and transaction cost without any additional supply to the investment amount. The V-shaped transaction cost function is used to calculate the transaction cost of the portfolio, and conditional value at risk (CVaR) is used to measure the risk of the portfolios. Computational tests on practical financial data show that the proposed model is effective and the rebalanced portfolio increases the expected return of the portfolio and reduces the CVaR risk of the portfolio.

THE DESIGN OF AN OPTIMAL RETROSPECTIVE RATING PLAN

2015 ◽  
Vol 46 (1) ◽  
pp. 141-163
Author(s):  
Xinxiang Chen ◽  
Yichun Chi ◽  
Ken Seng Tan
Keyword(s):  
Value At Risk ◽  
Conversion Factor ◽  
Insurance Premium ◽  
Conditional Value At Risk ◽  
Insurance Policy ◽  
Stop Loss ◽  
The Cost ◽  
The Relationship ◽  
Insurance Agreement ◽  
Actual Loss

AbstractA retrospective rating plan, whose insurance premium depends upon an insured's actual loss during the policy period, is a special insurance agreement widely used in liability insurance. In this paper, the design of an optimal retrospective rating plan is analyzed from the perspective of the insured who seeks to minimize its risk exposure in the sense of convex order. In order to reduce the moral hazard, we assume that both the insured and the insurer are obligated to pay more for a larger realization of the loss. Under the further assumptions that the minimum premium is zero, the maximum premium is proportional to the expected indemnity, and the basic premium is the only free parameter in the formula for retrospective premium given by Meyers (2004) and that the basic premium is determined in such a way that the expected retrospective premium equates to the expected indemnity with a positive safety loading, we formally establish the relationship that the insured will suffer more risk for a larger loss conversion factor or a higher maximum premium. These findings suggest that the insured prefers an insurance policy with the expected value premium principle, which is a special retrospective premium principle with zero loss conversion factor. In addition, we show that any admissible retrospective rating plan is dominated by a stop-loss insurance policy. Finally, the optimal retention of a stop-loss insurance is derived numerically under the criterion of minimizing the risk-adjusted value of the insured's liability where the liability valuation is carried out using the cost-of-capital approach based on the conditional value at risk.

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