•Robust models optimizing number, location, and capacity of Distribution Centers developed.•Parameter uncertainty within scenario and relative regret across scenarios modeled.•Facility damage, ...casualty by severity, and travel time uncertainties incorporated.•Concept of social costs probed against robustness of the relief network.•Social costs - setup, supply and deprivation costs due to delayed access modeled.
We develop robust models for earthquake preparedness by optimizing the number, location, and capacity of distribution centers (DCs). The goal is to minimize the total social costs, which include setup and initial supplies, as well as the deprivation costs associated with delayed access to supplies. The models incorporate various earthquake magnitude-specific uncertainties, such as facility damage, casualty by severity, and travel time. Examining the concept of social costs in light of an emerging concern in humanitarian logistics - the robustness of relief networks, we model two types of robustness: parameter uncertainty within a scenario and relative regret across scenarios. This unique approach reveals (1) the magnitude of social costs in the aftermath of an earthquake; (2) the hidden risks associated with inaccurate modeling of deprivation costs; and (3) the impact of budgetary constraints. We demonstrate the applicability of our approach via a case study featuring the Northridge region in California, which experienced two of the strongest earthquakes recorded in North America in 1971 and 1994.
•An MIP model developed for current one-round food aid bidding system used by USDA.•Humanitarian logistics challenges addressed via robust-optimization.•Box and ellipsoid based frameworks employed to ...model uncertainty.•USDA demand, supplier and carrier bid pricing uncertainties addressed.•Case study utilizing historical invoice data demonstrates our model applicability.
The U.S. Department of Agriculture (USDA) currently uses a bidding system to determine carriers and suppliers that would partner in providing food aid annually in response to global emergencies and famine. We mimic the USDA approach via a robust optimization model featuring box and ellipsoid uncertainty frameworks to account for uncertainties in demand, supplier and carrier bid prices. Through a case study utilizing historical invoice data, we demonstrate our model applicability in improving ocean carrier and food supplier bid pricing strategy and similar supply chain network optimization problems. Through a validation algorithm we demonstrate the value of our robust models.
Robust optimization for hurricane preparedness Wang, Xinfang (Jocelyn); Paul, Jomon A.
International journal of production economics,
March 2020, 2020-03-00, Letnik:
221
Journal Article
Recenzirano
This paper applies the concept of social cost (i.e., logistic, deprivation, and fatality costs) to analyze the optimal deployment time, location and capacities of stockpiles for Points of ...Distribution (PODs) in hurricane preparedness. We first propose a single-stage, adaptive robust model to determine the optimal deployment time, given the time-variant characteristics of hurricanes. The model is nested in an optimal stopping-time framework that captures the trade-offs between increasing deployment costs and reduced uncertainty as the hurricane approaches landfall. Once the optimal deployment time has been determined, we then propose a less conservative, two-stage robust optimization model with recourse actions to determine the PODs’ location, stockpile capacities and flow. Tested on a case study, results show that 1) a non-adaptive model leads to poor decisions about the optimal deployment time; 2) improperly modeled deprivation costs pose significant hidden risks to decisionmakers; 3) deprivation costs increase $3–5 for every dollar cut when the available budget is strictly binding; and 4) significant savings in social cost result from the wait-and-see strategy implemented through the two-stage robust model.
This paper uses the concept of social cost, comprised of private and externality costs, to capture the time-elapsed economic value of losses inflicted on users of a service under cyberattack. Our ...approach offers a holistic treatment of cybersecurity that not only employs prevention as a first defense but also uses detection & containment safeguards to mitigate the damage of successful attacks. It examines the optimal balance between these two safeguards under three sources of uncertainty through a robust optimization model with the help of distribution-free ellipsoidal uncertainty sets to ease the challenge of providing exact estimates for uncertain input. Our method is more appropriate than stochastic programming and other competing RO methods in addressing cybersecurity parameter uncertainty. Tested on a case study, results from 25 deterministic scenarios first reveal a strong resource-allocation preference for the prevention safeguard, but when the budget constraint is relaxed, preference shifts toward the containment & detection safeguard. Results from 54 robust test instances indicate that, for the three sources of uncertainty, the adjusted effectiveness of the prevention safeguard has the greatest impact on both social cost and the optimal configuration of safeguards. Our analysis points to some serious flaws in the existing cybersecurity framework's reliance on prevention and provides decisionmakers with urgently needed guidelines.
•Examine the optimal balance between prevention and detection & containment safeguards.•Resource-allocation preference shifts from prevention to detection & containment as budget constraints relax.•Among all uncertainties tested, the effectiveness of the prevention safeguard had the most impact.•Quantify the flaws in the current practice of mainly relying on prevention safeguard.
A critical issue when solving the share-of-choice product design problem is the reliability of the optimal solution in the presence of partworth uncertainty. Existing approaches use point estimates ...of an individual's partworth utilities as input to the product optimization stage, ignoring within-person variability in estimates. Post-optimality sensitivity analysis is occasionally performed to assess the degree to which a solution is negatively impacted by partworth uncertainty. We propose a robust optimization model that explicitly captures variation in partworth estimates during the optimization process. Using a large, commercial dataset, we benchmark our model's performance against its deterministic counterpart. We also present inferential theory to guide the selection of model parameters controlled by the analyst. Results reveal that the new approach produces robust solutions in the face of measurement error. Out-of-sample coverage for individuals drawn from the target population is significantly higher than corresponding solutions from published methods.
► We model the share-of-choice (SOC) product design problem in the presence of partworth uncertainty. ► We provide an exact formulation of the SOC product design problem as a linearized robust optimization problem. ► We conduct an analysis of the sensitivity of solutions to settings of two parameters controlled by an analyst. ► We investigate the benefits of the new approach relative to published deterministic solutions.► Our model results are significantly better than corresponding solutions from published methods.
Abstract To assess the efficacy of stent grafts (SGs) in managing central venous obstruction disease (CVOD) in hemodialysis (HD) patients with arteriovenous (AV) access, and to identify predictive ...factors influencing the SG treatment outcomes. HD subjects with CVOD who underwent SGs placement at our center between August 2018 and June 2022 were enrolled. Survival curve analysis using the Kaplan–Meier method and log-rank test was performed. Cox proportional hazards regression analysis was employed to identify predictive factors associated with outcomes. A total of 59 SG implantation procedures for CVOD were analyzed, comprising 30 cases of stenosis and 29 cases of occlusion. The access circuit primary patency (ACPP) at 6, 12, and 24 months post-SG placement were 80.9%, 53.8%, and 31.4%, respectively, while, the target lesion primary patency (TLPP) were 91.3%, 67.6%, and 44.5%, respectively. Subgroup analysis revealed higher TLPP in the stenosis group compared to the occlusion group, although the difference was not statistically significant ( P = 0.165). The TLPP was significantly improved by SG placement in those who had antecedent balloon dilations ( P < 0.001). Cox proportional hazards regression identified target lesion length ≥ 30 mm and procedure defects as independent predictors of lower TLPP after SG treatment for CVOD in HD patients. SG placement demonstrates safety and efficacy in managing CVOD among HD patients, leading to improved TLPP of endovascular therapy (EVT) for CVOD. Notably, long target lesions (≥ 30 mm) and procedure defects emerged as predictive factors influencing TLPP.
Despite the fact that in the 2012 presidential election, two-thirds of voters waited less than 10min and a mere 3% waited longer than an hour to cast their ballots, media accounts of excruciating ...waits have left a misleading impression on the general public. At the root of the problem is the allocation of voting machines based on efficiency as measured by average waiting time. This method does not account for the damaging consequences of the rare events that cause extremely long waits. We propose an extreme-value robust optimization model that can explicitly consider nominal and worst-case waiting times beyond the single-point estimate commonly seen in the literature. We benchmark the robust model against the published deterministic model using a real case from the 2008 presidential election in Franklin County, Ohio. The results demonstrate that the proposed robust model is superior in accounting for uncertainties in voter turnout and machine availability, reducing the number of voters experiencing waits that exceed two hours by 61%.
•A robust optimization model is proposed to allocate voting machines under uncertainty.•Five metrics are tested, including the number of voters waiting over 2 h.•The robust model outperforms the deterministic solution on four out of five metrics.•The robust model reduces the number of voters waiting over 2 h by 61%.
Abnormal proliferation and migration of human airway smooth muscle cells (HASMCs) play an important role in the development of childhood asthma. Long non-coding RNAs (lncRNAs) have been demonstrated ...to participate in HASMC proliferation and migration. We aimed to explore more effects and molecular mechanism of taurine upregulated gene 1 (TUG1) in childhood asthma.
TUG1 and SMURF2 were overexpressed and miR-216a-3p was downregulated in childhood asthma patients and PDGF-BB-stimulated HASMCs. TUG1 knockdown attenuated PDGF-BB-triggered proliferation and migration of HASMCs. MiR-216a-3p was targeted by TUG1, and miR-216a-3p suppression counteracted the repressive effects of TUG1 interference on proliferation and migration in PDGF-BB-treated HASMCs. SMURF2 was a downstream target of miR-216a-3p, and SMURF2 upregulation abated the inhibiting effects of miR-216a-3p on migration and proliferation in PDGF-BB-exposed HASMCs. TUG1 sponged miR-216a-3p to positively regulate SMURF2 expression.
TUG1 downregulation inhibited PDGF-BB-induced HASMC proliferation and migration by regulating miR-216a-3p/SMURF2 axis, offering novel insight into the potential application of TUG1 for childhood asthma treatment.
MicroRNAs (miRNAs) are a class of small non-coding RNAs that post-transcriptionally regulate the expression of approximately 50 % of all protein-coding genes. They have been demonstrated to act as ...key regulators in various pathophysiological processes and play significant roles in a wide range of human diseases, particularly cancer. Current research highlights the aberrant expression of microRNA-488 (miR-488) in multiple human diseases and its critical involvement in disease initiation and progression. Moreover, the expression level of miR-488 has been linked to clinicopathological features and patient prognosis across different diseases. However, a comprehensive systematic review of miR-488 is lacking. Therefore, our study aims to consolidate the current knowledge surrounding miR-488, with a primary focus on its emerging biological functions, regulatory mechanisms, and potential clinical applications in human diseases. Through this review, we aim to establish a comprehensive understanding of the diverse roles of miR-488 in the development of various diseases.
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•miR-488 expression was downregulated in a diversity of human diseases.•Summarize the latest research on functions, mechanisms, and clinical applications of miR-488.•miR-488 could be a novel biomarker in specific diseases.
Efficiency and equity are the two crucial factors to be considered when allocating public resources such as voting machines. Existing allocation models are all single-objective, focusing on ...maximizing either efficiency or equity despite the fact that the actual decision-making process involves both issues simultaneously. We propose a bi-objective integer program to analyse the tradeoff between the two competing objectives. The new model quantifies the sacrifice in efficiency in order to achieve a certain improvement in equity and vice versa. Using data from the 2008 United States Presidential election in Franklin County, Ohio, we demonstrate that our model is capable of producing significantly more balanced allocation plans, in terms of efficiency and equity, than current practice or other competing methods.
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