ترغب بنشر مسار تعليمي؟ اضغط هنا

A decision support framework for optimal vaccine distribution across a multi-tier cold chain network

67   0   0.0 ( 0 )
 نشر من قبل Varun Ramamohan
 تاريخ النشر 2021
والبحث باللغة English




اسأل ChatGPT حول البحث

The importance of vaccination and the logistics involved in the procurement, storage and distribution of vaccines across their cold chain has come to the forefront during the COVID-19 pandemic. In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine; the second being an extension of the first for the case when multiple vaccines with differing efficacies and costs are available for the same disease. Vaccine transportation and administration lead times are also incorporated within the models. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments to demonstrate: (a) the organization of the model outputs; (b) how the models can be used to assess the impact of storage capacities (at the cold chain points, transportation vehicle capacities) and manufacturer capacities on the optimal vaccine distribution pattern; and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce their computational burden. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.



قيم البحث

اقرأ أيضاً

With the approval of vaccines for the coronavirus disease by many countries worldwide, most developed nations have begun, and developing nations are gearing up for the vaccination process. This has created an urgent need to provide a solution to opti mally distribute the available vaccines once they are received by the authorities. In this paper, we propose a clustering-based solution to select optimal distribution centers and a Constraint Satisfaction Problem framework to optimally distribute the vaccines taking into consideration two factors namely priority and distance. We demonstrate the efficiency of the proposed models using real-world data obtained from the district of Chennai, India. The model provides the decision making authorities with optimal distribution centers across the district and the optimal allocation of individuals across these distribution centers with the flexibility to accommodate a wide range of demographics.
178 - Yutao Tang , Ding Wang 2020
In this paper, we investigate a constrained optimal coordination problem for a class of heterogeneous nonlinear multi-agent systems described by high-order dynamics subject to both unknown nonlinearities and external disturbances. Each agent has a pr ivate objective function and a constraint about its output. A neural network-based distributed controller is developed for each agent such that all agent outputs can reach the constrained minimal point of the aggregate objective function with bounded residual errors. Two examples are finally given to demonstrate the effectiveness of the algorithm.
A hybrid simulation-based framework involving system dynamics and agent-based simulation is proposed to address duopoly game considering multiple strategic decision variables and rich payoff, which cannot be addressed by traditional approaches involv ing closed-form equations. While system dynamics models are used to represent integrated production, logistics, and pricing determination activities of duopoly companies, agent-based simulation is used to mimic enhanced consumer purchasing behavior considering advertisement, promotion effect, and acquaintance recommendation in the consumer social network. The payoff function of the duopoly companies is assumed to be the net profit based on the total revenue and various cost items such as raw material, production, transportation, inventory and backorder. A unique procedure is proposed to solve and analyze the proposed simulation-based game, where the procedural components include strategy refinement, data sampling, gaming solving, and performance evaluation. First, design of experiment and estimated conformational value of information techniques are employed for strategy refinement and data sampling, respectively. Game solving then focuses on pure strategy equilibriums, and performance evaluation addresses game stability, equilibrium strictness, and robustness. A hypothetical case scenario involving soft-drink duopoly on Coke and Pepsi is considered to illustrate and demonstrate the proposed approach. Final results include P-values of statistical tests, confidence intervals, and simulation steady state analysis for different pure equilibriums.
In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, w e opt for a survivability-focused approach. Towards this end, the task prioritization and execution -- through which the allocation of tasks to robots is effectively realized -- are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots, and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.
Electricity distribution networks that contain large photovoltaic solar systems can experience power flows between customers. These may create both technical and socio-economic challenges. This paper establishes how these challenges can be addressed through the combined deployment of Community-scale Energy Storage (CES) and local network tariffs. Our study simulates the operation of a CES under a range of local network tariff models, using current Australian electricity prices and current network prices as a reference. We assess the financial outcomes for solar and non-solar owning customers and the distribution network operator. We find that tariff settings exist that create mutual benefits for all stakeholders. Such tariffs all apply a discount of greater than 50% to energy flows within the local network, relative to regular distribution network tariffs. The policy implication of these findings is that the, historically contentious, issue of network tariff reform in the presence of local solar power generation can be resolved with a mutually beneficial arrangement of local network tariffs and CES. Furthermore, the challenge of setting appropriate tariffs is eased through clear and intuitive conditions on local network tariff pricing.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا