Scenario Tree Research Articles

Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

Abstract A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling

This paper examines the importance of including operational scenarios representing short-term stochasticity in the long-term capacity expansion models with high shares of variable renewables. As scenario generation routines often are probabilistic, for example based on sampling, it is crucial that they ensure stable results in the capacity expansion model, so that it is the underlying uncertainty that decides the optimal solution, and not the approximation of that uncertainty in the model. However, it is unclear which operational scenario properties that are important to ensure good results and stability in stochastic models. This paper evaluates three sampling-based scenario generation routines in a multi-horizon stochastic capacity expansion problem representing the European electricity system. We compare the use of stochastic versus deterministic modelling with high shares of variable renewables. Further, we perform in-sample and out-of-sample stability tests on 90 scenario trees for each routine, and we compare the routines’ ability to produce stable system costs and capacity investments when approximating the optimal value from the real distribution. Results show that stochastic modelling with more than 80% share of variable renewables leads to more investments in both dispatchable and variable renewable capacity compared to deterministic modelling, which means that stochastic modelling should be used with very high shares of variable renewables. The scenario generation routine based on stratified sampling increases stability with the same number of operational scenarios compared to its alternatives, and scenario generation routines using stratified sampling should be further explored.

Development and evaluation of a new method to combine clinical impression survey data with existing laboratory data for veterinary syndromic surveillance with the Canada West Swine Health Intelligence Network (CWSHIN)

The Canada West Swine Health Intelligence Network (CWSHIN) is a surveillance system imbedded in an intelligence network. It has been conducting syndromic surveillance in the four western provinces of Canada since 2012. The quarterly activities include repeated clinical impression surveys, compilation of laboratory data, discussion of trends with an expert group (practitioners, laboratory diagnosticians) and swine health reports for producers and swine practitioners. However, due to the lack of standardized population identifiers across data sources usual methods of combining data could not be applied and the collated data were not being fully utilized and analysed. Therefore in 2019, CWSHIN underwent a substantial review resulting in the “Next Generation CWSHIN”.The objectives of this study were to develop and evaluate a new data merging method to combine CWSHIN’s clinical impression survey and laboratory data; and to provide examples of analyses and modeling based on these data.The data for analysis were restricted to repeated clinical impression surveys (2019–2020) from veterinary practitioners and laboratory diagnostic data (2016–2020). Merging surveillance data from existing sources can be challenging. Therefore, as an alternative to merge data using a hierarchy of population identifiers, we developed a Disease Map to link surveillance data from all our data-sources. The resulting Data Repository allowed monitoring of temporal trends of syndromes, clinical diseases, and laboratory identified organisms, but it cannot provide estimates of disease occurrence. Two main reasons were the lack of denominators and using existing data on routine diagnostic tests. Therefore, discussion in the expert group (veterinary practitioners, laboratory diagnosticians, swine health experts) was critical to the system’s success. Based on repeated clinical impression surveys a stochastic scenario tree model for freedom from Foot and Mouth Disease (CWSHIN Blister model) was also developed.In conclusion, the method to link existing data systems from multiple divergent sources by means of a Disease Map improved CWSHIN’s veterinary syndromic surveillance. Together the Data Repository and Disease map provided flexibility to monitor temporal trends, define populations and diseases, and allow analysis. However, it is critical that the surveillance is coupled with a good intelligence network that can help interpret the results and disseminate knowledge to veterinarians and producers.

Varroa destructor detection in non-endemic areas

Australia is one of the few countries where Apis mellifera are not infested by the parasitic mite Varroa destructor. In Australia a program called the ‘Sugar Shake Team’ has been implemented to detect an incursion of V. destructor, should it occur. The aim of this paper was to provide an estimate of the probability that V. destructor would be detected by the Sugar Shake Team program if an incursion had actually occurred (surveillance system sensitivity) using a scenario tree modelling approach. A secondary aim was to provide commentary on how surveillance for V. destructor incursions into Australia might be made more effective. Our analyses show that if one apiary in the study area was infested with V. destructor, there was only a 0.47% probability that infestation would be detected by the current program. We conclude that in its current form the Sugar Shake Team program does not provide protection to multi-billion-dollar beekeeping, pollination and agricultural industries in Australia. Surveillance for V. destructor can be made more effective by increasing the coverage and frequency of testing, deployment of additional sentinel hives and the use of sticky boards.

Integrating stochastic discrete constraints in MPC. Application to Home Energy Management System

In this paper, in a Model Predictive Control problem, we tackle the integration of uncertain constraints that belong to a discrete set. We propose a controller that offers a solution to reformulate the discrete stochastic constraints to avoid the exponential growth of scenario tree experienced by existing controllers. The proposed controller’s efficiency is shown by benchmarking and comparing it with the Multistage Stochastic Programming algorithm in a context of Home Energy Management System. The comparison is made based on simulations using real-life data to manage the energy flow within a building connected to the electricity feeder, which random character involves its availability and nonavailability.

Designing a testing kit supply network for suspected COVID-19 cases under mixed uncertainty approach

Motivated by the COVID-19 (C-19) pandemic and the challenges it poses to global health and the medical communities, this research aims to investigate the factors affecting of reduction health inequalities related to the C-19 to tackle the increasing number of outbreaks and their social consequences in such a pandemic. Hence, we design a COVID-19 testing kit supply network (C-19TKSN) to allocate various C-19 test kits to the suspected C-19 cases depending on the time between the emergence of their first symptoms and the time they are tested. In particular, this model aims to minimize the total network cost and decrease false results C-19 test by considering the fundamental characteristics of a diagnostic C-19 test (i.e., specificity and sensitivity). In the sensitivity characteristic, a gamma formula is presented to estimate the error rate of false-negative results. The nature of the C-19TKSN problem is dynamic over time due to difficult predictions and changes in the number of C-19 patients. For this reason, we consider the potential demands relating to different regions of the suspected C-19 cases for various C-19 test kits and the rate of prevalence of C-19 as stochastic parameters. Accordingly, a multi-stage stochastic programming (MSSP) method with a combined scenario tree is proposed to deal with the stochastic data in a dynamic environment. Then, a fuzzy approach is employed based on Me measure to cope with the epistemic uncertainty of input data. Eventually, the practicality and capability of the proposed model are shown in a real-life case in Iran. The results demonstrate that the performance of the MSSP model is significantly better in comparison with the two-stage stochastic programming (TSSP) model regarding the false results and the total cost of the network.

Potential economic value of integrating concentrating solar power into power grids

Increasingly, climate neutrality is becoming a consensus among many countries. While this can motivate toward higher proportions of renewable energy, it can also result in significant increases in the volatility of power systems. Concentrating solar power (CSP) combines the features of zero-emissions and dispatching capability, rendering it an ideal technology for sustainable power systems. In comparison with other clean energy generation technologies, CSP remains expensive with a levelized cost of energy (LCOE) of 0.184 $/kWh in 2018. However, in addition to LCOE, the benefits of integrating CSP for the entire power system are worth considering for a liberalized electricity market. This paper presents the development of a multistage stochastic programming model, in which the uncertainty of variable renewable energy is indicated by a modified scenario tree approach. A case study is demonstrated based on three provinces in China with different generation structures. The results demonstrate the economic value of CSP integration, which increases its investment potential—especially when combined with more variable renewables.

The effectiveness of bovine tuberculosis surveillance in Dutch badgers.

Countries survey wildlife for bovine tuberculosis (bTB) to ensure case detection or to ascertain a high probability of freedom from bTB in wildlife. The Eurasian badger (Meles meles) is a potential bTB reservoir host. Between 2008 and 2019, 282 badgers were examined post‐mortem in the context of general wildlife disease and targeted bTB surveillance programmes in the Netherlands, and no bTB cases were detected. However, it was unclear how effective this surveillance effort was to demonstrate freedom from Mycobacterium bovis infection in the badger population of ±6000 or to detect cases if present.Therefore, surveillance effectiveness was assessed using scenario tree modelling. For lack of standards for wildlife, the models were run against three assumed levels of disease in the population called design prevalence P*: 0.1%, 0.5%, and 3%. A small risk of introduction (0.015/year) was applied, because the Netherlands are officially free from bTB in cattle, with rare import of bTB‐infected cattle and no bTB‐infected wildlife reported along the Belgian and German borders with the Netherlands. Surveillance more readily picks up bTB presence in badgers when case detection sensitivity tends towards 100% and demonstrates freedom best when the probability of freedom tends towards 100%.For P* 0.1%, 0.5% and 3%, respectively, maximum case detection sensitivity during 2008–2019 was 8%, 35% and 94% and the probability of freedom in 2019 was 46%, 67%, and 95%. At P* = 3%, performing targeted surveillance on 300 badgers in a year would make it extremely unlikely to miss a case (case detection sensitivity > 99.9%); and if no cases are detected, the adjusted probability of freedom would then reach nearly 98.5%. Stakeholders should be made aware that at P* = 3%, one case detected implies around 3% infected badgers. Additional surveillance system components to assess bTB in wildlife and its economics are to be explored further.

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

In order to reduce the energy consumption of furnaces and save costs in the product delivery time, the focus of this paper is to discuss the uncertainty of demand in the rolling horizon and to globally optimize the sustainability of the production in the aluminum furnace hot rolling section in environmental and economic dimensions. First, the triples α/β/γ are used to describe the production scheduling in the aluminum furnace hot rolling section as the scheduling of flexible flow shop, satisfied to constraints of demand uncertainty, operation logic, operation time, capacity and demand, objectives of minimizing the residence time of the ingot in the furnace and minimizing the makespan. Second, on the basis of describing the uncertainty of demand in rolling horizon with the scenario tree, a multi-objective mixed integer linear programming (MILP) optimization model for sustainable production in the aluminum furnace hot rolling section is formulated. Finally, an aluminum alloy manufacturer is taken as an example to illustrate the proposed model. The computational results show that when the objective weight combination takes the value of α=0.7, β=0.3, the sustainability indicators of the environmental and economic dimensions can be optimized to the maximum extent possible at the same time. Increasingly, managerial suggestions associated with the trade-off between environmental and economic dimensions are presented. Scheduling in the rolling horizon can optimize the production process of the aluminum furnace hot rolling section globally, indicating that it is more conducive to the sustainable development of the environment and economic dimensions than scheduling in a single decision time period.

Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties

Planning a regional energy system based on the advantages of different types of energies and building an economic and efficient regional integrated energy system (RIES) are hot research subjects in the energy planning field. The call for using clean energy and attaining carbon neutrality worldwide has also attracted research attention in the field of low-carbon and clean planning. Because of the growth of the energy system construction cycle together with the utilization of various types of energies, multi-stage planning considering the uncertainty and construction time sequence in a long time scale has gradually become the primary research direction for planning RIES. The uncertainty of multi-energy load growth and the energy price fluctuation in a long time scale considerably affects the construction and economy of the final planning scheme. Aiming to solve the problems of incomplete load data, low accuracy of single load forecasting, and weak regularity of the energy price fluctuation, this study proposes a multi-stage scenario tree generation method based on the conditional generative adversarial network–random forest–Markov chain. And a method of energy price upper and lower boundary determination is based on the multi-index artificial neural network method for analyzing and solving energy price fluctuation. A multi-stage stochastic planning model of RIES is built herein to minimize the comprehensive cost, including the construction investment and regional operation costs, energy hub income, and carbon emission reduction. Finally, the Beichen district of Tianjin, China is considered a case for verifying the effectiveness and applicability of the proposed model and method. Results show that the construction time sequence of the energy hub substantially affects the planning scheme. Energy hub construction in advance benefits the energy hub income and carbon emission reduction; however, the initial stage requires more investment, which affects the planning and construction of renewable energy. Compared with a single-stage deterministic planning scheme, the optimized multi-stage stochastic planning scheme reduces the number of idle facilities and benefits cost recovery, energy hub income, coverage scenarios, and carbon emission reduction.

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What-If Analyses of Localized and Dynamic Effects.

In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming.

In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sample variance, associating the probability distributions to normal distributions with defined mean and standard deviation; and a second proposed model with a discrete distribution with 3 values and their respective probabilities of occurrence. In both cases, a scenario tree is generated. The models developed are applied to an aggregate production plan (APP) for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important clients throughout the country. Production capacity and demand are defined as random variables of the model. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The developed models were compared and analyzed. Moreover, this work was complemented with a sensitivity analysis; varying the percentage of service level, also, varying the stochastic parameters (mean and standard deviation) to test how these variations impact in the solution and decision variables.

A quantitative risk assessment to evaluate the efficacy of mitigation strategies to reduce highly pathogenic avian influenza virus, subtype H5N1 (HPAI H5N1) in the Menoufia governorate, Egypt

BackgroundThe poultry industry in Egypt has been suffering from endemic highly pathogenic avian influenza (HPAI) virus, subtype H5N1 since 2006. However, the emergence of H9N2, H5N8, and H5N2 in 2011, 2016, and 2019 respectively, has aggravated the situation. Our objective was to evaluate how effective are the mitigation strategies by a Quantitative Risk Assessment (QRA) model which used daily outbreak data of HPAI-H5N1 subtype in Egypt, stratified by different successive epidemic waves from 2006 to 2016.ResultsBy applying the epidemiologic problem-oriented approach methodology, a conceptual scenario tree was drawn based on the knowledgebase. Monte Carlo simulations of QRA parameters based on outbreak data were performed using @Risk software based on a scenario-driven decision tree. In poultry farms, the expected probability of HPAI H5N1 prevalence is 48% due to failure of mitigation strategies in 90% of the time during Monte Carlo simulations. Failure of efficacy of these mitigations will raise prevalence to 70% with missed vaccination, while failure in detection by surveillance activities will raise it to 99%. In backyard poultry farms, the likelihood of still having a high HPAI-H5N1 prevalence in different poultry types due to failure of passive and active surveillance varies between domestic, mixed and reservoir. In mixed poultry, the probability of HPAI-H5N1 not detected by surveillance was the highest with a mean and a SD of 16.8 × 10–3 and 3.26 × 10–01 respectively. The sensitivity analysis ranking for the likelihood of HPAI-H5N1 in poultry farms due to missed vaccination, failure to be detected by passive and active surveillance was examined. Among poultry farms, increasing vaccination by 1 SD will decrease the prevalence by 14%, while active and passive surveillance decreases prevalence by 12, and 6%, respectively. In backyard, the active surveillance had high impact in decreasing the prevalence by 16% in domestic chicken. Whereas the passive surveillance had less impact in decreasing prevalence by 14% in mixed poultry and 3% in domestic chicken.ConclusionIt could be concluded that the applied strategies were not effective in controlling the spread of the HPAI-H5N1 virus. Public health officials should take into consideration the evaluation of their control strategies in their response.

Welfare-maximizing transmission capacity expansion under uncertainty.

We apply the JuDGE optimization package to a multistage stochastic leader-follower model that determines a transmission capacity expansion plan to maximize expected social welfare of consumers and producers who act as Cournot oligopolists in each time period. The problem is formulated as a large-scale mixed integer programme and applied to a 5-bus instance over scenario trees of varying size. The computational effort required by JuDGE is compared with solving the deterministic equivalent mixed integer programme using a state-of-the-art integer programming package. This article is part of the theme issue 'The mathematics of energy systems'.

Implementation of Network Traffic Monitoring using Software Defined Networking Ryu Controller

Network traffic monitoring is vital for enhancing the overall network performance and for optimizing the traffic flows. However, an emerging growth of use in cloud services, internet-of-things, block-chain and data analytics, demand the hardware-based-network-controller to provide more features for expanding network architecture. Therefore, Software Defined Networking (SDN) offers a new solution in terms of scalability, usability and programmable software-based-network-controller for the legacy network infrastructure. In fact, SDN provides a dynamic platform for the network traffic monitoring using international standard. In this study, SDN setup and installation method uses a Mininet emulator containing a controller Ryu with switching hub component, OpenFlow switches, and nodes. The number of nodes is adding until reaches to 16 nodes and evaluated through different network scenarios (single, linear and tree topology). Findings show that the single topology gives a winning criterion compared to other topologies. SDN implementation is measured with performance parameters such as Throughput, Jitter, Bandwidth and Round-Trip Time between scenarios using the Ryu controller. Future research explores on the performance of SDN in larger network and investigates the efficiency and effectiveness of SDN implementation in mesh topology.

A Stochastic Dual Dynamic Integer Programming for the Uncapacitated Lot-Sizing Problem with Uncertain Demand and Costs

We study the uncapacitated lot-sizing problem with uncertain demand and costs. We consider a multi-stage decision process and rely on a scenario tree to represent the uncertainty. We propose to solve this stochastic combinatorial optimization problem thanks to a new extension of the stochastic dual dynamic integer programming algorithm. Our results show that this approach can provide good quality solutions in a reasonable time for large-size instances.

МЕТОДЫ АНАЛИЗА ВОЗМОЖНЫХ СОСТОЯНИЙ ОБЪЕКТА ДИАГНОСТИРОВАНИЯ.

The development of new methods of technical diagnostics is an important task aimed at improving the efficiency and safety of the operation of industrial equipment. Such methods are complexes of jointly used methods for obtaining diagnostic information, methods for extracting from it the actual data on the technical state of the diagnosed objects, and methods for organizing diagnostic processes. Complex technical systems are characterized by complex nonlinear interactions between their constituent elements, complex scenarios of causal relationships between hazardous, probabilistic events and processes occurring during their operation. These scenarios can be implemented in complex scenario trees. Features of technical diagnostics of complex technical systems are determined by the distinctive properties of failures in such systems and scenarios. The technical diagnostics system is an information-measuring system and contains sensors of controlled parameters, communication lines with an information collection unit, executive devices, devices for interfacing with other information-measuring and control systems. Methods of technical diagnostics serve as the basis for constructing such a system. The current level and prospects for the development of diagnostic tools, flaw detection and automated control open real possibilities of using methods of maintenance and repair of equipment by a technical condition. The greatest effect from the use of such a system is achieved when operating complex equipment, preventive repair of which is associated with high costs, and emergency repair.

Sensitivity of bovine tuberculosis surveillance through intradermal tests in cattle in France: An evaluation of different scenarios

The current situation regarding bovine tuberculosis (bTB) in Europe is spatially heterogeneous, with stagnating or increasing trends in bTB prevalence in many European regions, underlying the challenge in controlling this disease. In France, in spite of the implementation of two control programs in 2010–2012 to eradicate the disease and maintain the bTB-free status, bTB prevalence has continued to increase, underlying the need to reinforce and adapt surveillance measures. The goal of this study was to evaluate the effectiveness of bTB surveillance in high-risk areas in metropolitan France, with an emphasis on the criteria to select herds and animals within herds in the context of programmed surveillance and movement testing.The fraction of bTB-infected herds detected by the surveillance was quantified using a stochastic scenario tree modelling approach, with input parameter values based on surveillance and cattle traceability data and literature. The detection fraction was assessed for the current surveillance system and for alternative scenarios.The model predicted that the median detection fraction of infected herds by the current programmed surveillance in high-risk areas, which consists in annual testing of herds with a minimum age of testing of 24 months, was 71.5 % (interquartile interval: 47.4−89.4). The results showed a significant gain of the detection fraction with a decrease from 24 to 12 months old (83.5 % [60.6−95.9]) or to six weeks old (91.3 % [71.6−99.0]). Regarding pre-movement surveillance, tests are currently mandatory for bovines that originate from a previously infected herd or from a herd epidemiologically linked to a bTB-infected herd. The median detection fraction predicted by the model for this surveillance scenario was 1.2 % [0.7−1.8]. For the alternative scenario, where surveillance would be extended to all herds in high-risk areas, the model predicted a significant increase of the detection fraction to 26.5 % [18.1−37.9]. The results were sensitive to the following input values: the number of infected bovines within herds and, to a lower extent, the comparative intradermal tuberculin test sensitivity for both models, and surveillance coverage for the model on pre-movement surveillance.Our study underlines several complementary ways to improve the detection of infected herds, which is critical for implementing control measures and epidemiological investigations as early as possible. These necessary changes in surveillance must be accompanied by a global reflexion on surveillance financing.

Evaluation of the sensitivity of the classical swine fever surveillance system in two free zones in Colombia.

Infection with the classical swine fever virus (CSFV) causes a disease in pigs that ranges from a hyperacute form in which animals die in a few hours to subclinical disease. Due to this wide range of virulence, several complementary surveillance strategies should be implemented for the early detection of the disease. The objective of the present study was to determine the sensitivity of the surveillance system to detect CSFV outbreaks in a free zone (Zone 1) and in a zone undergoing an eradication process (Zone 2) in Colombia. Stochastic scenario tree models were used to describe the population and surveillance structures and to determine the probability of CSFV detection. The total sensitivity of the surveillance system in the case of a single infected farm in Zone 1 was 31.4% (CI 95%: 7.2-54.1) and in the case of 5 infected farms was 85.2% (CI 95%: 67.3-93.7), while in Zone 2 the sensitivities were 27.8% (CI 95%: 6.4-55.1) and 82.5% (CI 95%: 65-92.9), respectively. The on-farm passive surveillance shows the highest sensitivity for detection of a single CSFV infected farm in both zones (22.8% in Zone 1 and 22.5% in Zone 2). The probability of detection was higher in a family / backyard premise than on a commercial farm in both zones. The passive surveillance at slaughterhouse had a sensitivity of 5.3% and 4.5% for the detection of a single infected farm in Zone 1 and 2, respectively. Active surveillance presented a range of sensitivity between 2.2% and 4.5%. In conclusion, the sensitivity of the surveillance in the two studied zones was quite high, one of reasons for this good sensitivity being the sentinel network based on the voluntary participation of 5,500 collaborators that were trained for the identification and notification of diseases of national interest.

A framework for multicriteria decision-aid analyses in animal health surveillance applied to periodic screening for French bovine tuberculosis.

In animal health surveillance, decision-makers must allocate limited financial and human resources, choosing appropriate protocols that consider technical, economic and human aspects (i.e. test sensitivity, cost benefits and policy acceptability respectively). Choosing in an objective manner and considering all these criteria can be challenging, especially where criteria have a tendency to be at odds with one another. In France, there are several mandatory protocols implemented to screen for bovine tuberculosis (TB), each with advantages and drawbacks concerning effectiveness, cost and acceptability. Previous studies have developed scenario tree models in order to evaluate mandatory periodic screening protocols and alternative protocols. Using these previously developed models, we estimated protocol sensitivity, costs at the level of State and farmer, probability of false suspects, and probability of culling an uninfected animal, which influence stakeholders' acceptability. We then assessed the level of difficulty for protocol implementation for veterinarians, farmers and State through the use of surveys. Using these criteria, we rank the protocols with the PROMETHEE method, a multicriteria decision-aid method, by considering the relative importance of each criterion from the decision-maker point of view in four administrative areas with contrasting epidemiological context. This method can be considered a tool to aid decision-makers in choosing the appropriate protocol to apply to a heard while considering the technical and socio-economic facets of the problem. Additionally, by adapting the criteria to specific issues with regards to decision-making, there is potential for applying the PROMETHEE method to other animal health surveillance problems.