Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19.

As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.

Safe distancing in the time of COVID-19.

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area-a policy that significantly reduces the overall risk of contagion.

On constrained smoothing and out-of-range prediction using [formula omitted]-splines: A conic optimization approach

• We study the problem of estimating smooth curves which verify structural properties. • We propose a mathematical optimization formulation to build constrained P-splines. • An open-source Python library is developed: cpsplines. • We estimate constrained curves in simulated, COVID-19 and demographic data. Decision-making is often based on the analysis of complex and evolving data. Thus, having systems which allow to incorporate human knowledge and provide valuable support to the decider becomes crucial. In this work, statistical modelling and mathematical optimization paradigms merge to address the problem of estimating smooth curves which verify structural properties, both in the observed domain in which data have been gathered and outwards. We assume that the curve to be estimated is defined through a reduced-rank basis (B -splines) and fitted via a penalized splines approach (P -splines). To incorporate requirements about the sign, monotonicity and curvature in the fitting procedure, a conic programming approach is developed which, for the first time, successfully conveys out-of-range constrained prediction. In summary, the contributions of this paper are fourfold: first, a mathematical optimization formulation for the estimation of non-negative P-splines is proposed;second, previous results are generalized to the out-of-range prediction framework;third, these approaches are extended to other shape constraints and to multiple curves fitting;and fourth, an open source Python library is developed: cpsplines. We use simulated instances, data of the evolution of the COVID-19 pandemic and of mortality rates for different age groups to test our approaches. [ FROM AUTHOR]

Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China

• The accuracy of the temperature, radiation and hybrid models improved by 12.05 %, 11.06% and 10.46% after being optimized by WOA. • The estimation accuracy of the temperature, radiation and hybrid models optimized by the whale algorithm were higher than the prediction result of the ELM model. • The empirical model with more input parameters has higher estimation accuracy than the empirical model with fewer parameters. The accurate estimation of reference crop evapotranspiration (ET 0) is of great significance to improve agricultural water use efficiency and optimize regional water resources management. At present, the applicability evaluation system of ET 0 models is still lacking in several climate regions in China, leading to the confusion in application of the ET 0 model in some specific regions. In this study, the daily meteorological data of 84 representative stations in four climate regions of China during the past 30 years (1991–2019) were selected to evaluate the ET 0 simulation results of twelve empirical models (four temperature models, five radiation models, and three hybrid models) on the daily scale, and the optimal models suitable for each climate region were screened. Whale optimization algorithm (WOA) was used to optimize the optimal model to improve the simulation accuracy, and the ET 0 results were compared with those predicted by extreme learning machine (ELM). The results showed that the estimation accuracy of the hybrid model was the best throughout China, followed by the radiation model, and the temperature model was relatively poor, with R2 ranges of 0.77–0.88, 0.60–0.86, and 0.58–0.82, respectively. Among the temperature-based models, Hargreaves-Samani and Improve Baier-Robertson model had the highest accuracy, with R2 of 0.80 and 0.79. Among the radiation-based models, Priestley-Taylor and Jensen-Haise models had the best accuracy, with R2 of 0.82 and 0.79. Among the hybrid models, Penman model had the highest accuracy, with R2 of 0.84. The accuracy of Hargreaves-Samani and Improve Baier-Robertson model in SMZ climate region was higher than TCZ, TMZ, and MPZ, and the accuracy of Jensen-Haise model in TCZ was the highest. The estimation accuracy of Priestley-Taylor and Penman models was similar in SMZ, TCZ, TMZ and MPZ. Using WOA to optimize the optimal temperature, radiation, and hybrid models, the prediction accuracy was improved by 12.05 %, 11.06 %, and 10.46 %, which were higher than the result of ELM model, with R2 of 0.90, 0.91, 0.95 and 0.90, respectively. Therefore, it is recommended to adopt WOA to optimize the empirical model to estimate the ET 0 all over China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Bi-objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID-19 pandemic.

This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.

COVID-19 Propagation Prediction Model Using Improved Grey Wolf Optimization Algorithms in Combination with XGBoost and Bagging-Integrated Learning

Toward solving the slow convergence and low prediction accuracy problems associated with XGBoost in COVID-19-based transmission prediction, a novel algorithm based on guided aggregation is presented to optimize the XGBoost prediction model. In this study, we collect the early COVID-19 propagation data using web crawling techniques and use the Lasso algorithm to select the important attributes to simplify the attribute set. Moreover, to improve the global exploration and local mining capability of the grey wolf optimization (GWO) algorithm, a backward learning strategy has been introduced, and a chaotic search operator has been designed to improve GWO. In the end, the hyperparameters of XGBoost are continuously optimized using COLGWO in an iterative process, and Bagging is employed as a method of integrating the prediction effect of the COLGWO-XGBoost model optimization. The experiments, firstly, compared the search means and standard deviations of four search algorithms for eight standard test functions, and then, they compared and analyzed the prediction effects of fourteen models based on the COVID-19 web search data collected in China. Results show that the improved grey wolf algorithm has excellent performance benefits and that the combined model with integrated learning has good prediction ability. It demonstrates that the use of network search data in the early spread of COVID-19 can complement the historical information, and the combined model can be further extended to be applied to other prevention and control early warning tasks of public emergencies. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Decision-support systems for ambulatory care, including pandemic requirements: using mathematically optimized solutions.

BACKGROUND: The healthcare sector poses many strategic, tactic and operational planning questions. Due to the historically grown structures, planning is often locally confined and much optimization potential is foregone. METHODS: We implemented optimized decision-support systems for ambulatory care for four different real-world case studies that cover a variety of aspects in terms of planning scope and decision support tools. All are based on interactive cartographic representations and are being developed in cooperation with domain experts. The planning problems that we present are the problem of positioning centers for vaccination against Covid-19 (strategical) and emergency doctors (strategical/tactical), the out-of-hours pharmacy planning problem (tactical), and the route planning of patient transport services (operational). For each problem, we describe the planning question, give an overview of the mathematical model and present the implemented decision support application. RESULTS: Mathematical optimization can be used to model and solve these planning problems. However, in order to convince decision-makers of an alternative solution structure, mathematical solutions must be comprehensible and tangible. Appealing and interactive decision-support tools can be used in practice to convince public health experts of the benefits of an alternative solution. The more strategic the problem and the less sensitive the data, the easier it is to put a tool into practice. CONCLUSIONS: Exploring solutions interactively is rarely supported in existing planning tools. However, in order to bring new innovative tools into productive use, many hurdles must be overcome.

Scheduling optimization of emergency supplies with multi-supply points based on variable length genotype genetic algorithm.

How to dispatch emergency supplies timely and efficiently and reduce the damage caused by emergencies has become the focus of social attention. On the premise of considering the characteristics of special emergencies such as the epidemic situation of COVID-19, this paper constructed a kind of emergency supplies scheduling network of multi-supply points and multimodal transportation. Taking the lowest transportation cost, the least time penalty and the minimum risk of infection of dispatchers as the optimization objectives, it established a kind of multi-objective optimal scheduling model. In view of the limitation of the optimization algorithm based on clustering in solving multi-supply points, especially multi-objective scheduling optimization problems, the paper proposed a kind of hybrid niche genetic algorithm for variable length genotypes considering the idea of full feasible regions, which could avoid the problem above by restoring the search range of the solution to the fully feasible regions. The experiment results of 23 benchmark instances show that the optimization performance of the algorithm is stronger and it can search better solutions than best-known solutions of some examples. On this basis, the simulation results of four kinds of genetic algorithms in emergency supplies scheduling examples of multi-supply points and multimodal transportation come to a conclusion that the improved strategies such as hybrid niche are superior. (English) [ FROM AUTHOR] 如何及时、高效地调度应急物资以减小突发事件带来的伤害成为社会关注的焦点问题。在综合考虑新 冠肺炎疫情这类特殊突发事件特点的前提下, 构建了一类多供应点多式联运应急物资调度网络, 并以运输成本 最低、时间惩罚最少、配送员被感染风险最小为优化目标建立了一类多目标调度优化模型。考虑到基于聚类思 想的优化算法在解决多供应点, 尤其是多目标调度优化问题中缩减可行域方法科学性存疑的局限性, 提出了一 类考虑完全可行域思想的变长基因型混合小生境遗传算法, 并借助 ２３个基准测试实例验证了这一算法的有效 性, 更新了部分实例的现有最优解。在此基础上, 通过比较多供应点应急物资多式联运算例中四类遗传算法的 仿真结果进一步验证了混合小生境等改进策略的优越性。 (Chinese) [ FROM AUTHOR] Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

On the optimal layout of a dining room in the era of COVID-19 using mathematical optimization.

We consider the problem of maximizing the number of people that a dining room can accommodate provided that the chairs belonging to different tables are socially distant. We introduce an optimization model that incorporates several characteristics of the problem, namely: the type and size of surface of the dining room, the shapes and sizes of the tables, the positions of the chairs, the sitting sense of the customers, and the possibility of adding space separators to increase the capacity. We propose a simple, yet general, set-packing formulation for the problem. We investigate the efficiency of space separators and the impact of considering the sitting sense of customers in the room capacity. We also perform an algorithmic analysis of the model, and assess its scalability to the problem size, the presence of (or lack thereof) room separators, and the consideration of the sitting sense of customers. We also propose two constructive heuristics capable of coping with large problem instances otherwise intractable for the optimization model.

Optimization models to find maximum appointment capacity in a pediatric orthopedic clinic - A COVID experience

During the first few months of the COVID crisis in 2020, a large number of appointments were canceled at many outpatient clinics across the US as a precautionary measure. This cancelation of appointments resulted in a long backlog. As clinics started to slowly open with safety precautions, they were faced with the daunting task of rescheduling these canceled appointments in addition to regular appointments. Administrators were faced with the question - how many appointments can be scheduled in a day without increasing physician and exam-room capacity? In this paper, a combination of mathematical optimization models and best-practice guidelines gleaned from literature were used to arrive at a set of recommendations for a pediatric orthopedic clinic. Recommendations include number of appointments per provider per day, the sequence of appointments by visit type and start-time (appointment time) for each appointment. Implicit in these recommendations is the minimization of length of visit (LOV) by minimizing waiting-time for patients. Exam-room utilization was also simulated to ensure existing exam-rooms will be sufficient for handling the number of appointments recommended by mathematical models. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19.

Since the seminal paper by Bates and Granger in 1969, a vast number of ensemble methods that combine different base regressors to generate a unique one have been proposed in the literature. The so-obtained regressor method may have better accuracy than its components, but at the same time it may overfit, it may be distorted by base regressors with low accuracy, and it may be too complex to understand and explain. This paper proposes and studies a novel Mathematical Optimization model to build a sparse ensemble, which trades off the accuracy of the ensemble and the number of base regressors used. The latter is controlled by means of a regularization term that penalizes regressors with a poor individual performance. Our approach is flexible to incorporate desirable properties one may have on the ensemble, such as controlling the performance of the ensemble in critical groups of records, or the costs associated with the base regressors involved in the ensemble. We illustrate our approach with real data sets arising in the COVID-19 context.