Different Levels Of Uncertainty Research Articles

Computationally semi-numerical technique for solving system of intuitionistic fuzzy differential equations with engineering applications

Some complex problems in science and engineering are modeled using fuzzy differential equations. Many fuzzy differential equations cannot be solved by using exact techniques because of the complexity of the problems mentioned. We utilize analytical techniques to solve a system of fuzzy differential equations because they are simple to use and frequently result in closed-form solutions. The Generalized Modified Adomian Decomposition Method is developed in this article to compute the analytical solution to the linear system of intuitionistic triangular fuzzy initial value problems. The starting values in this case are thought of as intuitionistic triangular fuzzy numbers. Engineering examples, such as the Brine Tanks Problem, are used to demonstrate the proposed approach and show how the series solution converges to the exact solution in closed form or in series. The corresponding graphs at different levels of uncertainty show the example’s numerical outcomes. The graphical representations further demonstrate the effectiveness and accuracy of the proposed method in comparison to Taylor’s approaches and the classical Decomposition method.

The interplay between uncertainty, managerial decision making, and firm value: Evidence from Bangladesh

This paper investigates how uncertainty influences investment and cash-holding decisions and how these nexuses influence the performance of Bangladeshi firms. The results indicate threshold levels of investment and cash holdings up to which investment increases and cash holding decreases with uncertainty, and both reverse afterward. In other words, the effect of uncertainty on investment and cash holding are inverted U and U-shaped, respectively. The results also imply that the shareholders perceive the impact of these managerial decisions on firms' market value at different levels of uncertainty and depict a nonmonotonous relationship between market value and uncertainty.

Development of LSN-based pipe repair rate models utilising data from the 2011 Christchurch earthquakes

The Canterbury Earthquake Sequence (CES) adversely impacted built, economic and social environments. This included widespread physical damage to the water supply pipe network in Christchurch, resulting in long service disruptions. The transient and permanent ground deformations generated by the earthquakes in the CES caused a range of pipe damage, particularly in the MW 6.2 22 February 2011 and the relatively less damaging MW 6.0 13 June 2011 event. Damage to the pipes in both events was largely attributed to liquefaction and lateral spreading effects. Pipes made of ductile material (e.g. PVC, HDPE) sustained lesser damage (and therefore lower repair rates) compared to the pipes made of non-ductile material (e.g. AC, CI). In all cases, the repair rates (number of repairs per kilometre) typically increased with increasing liquefaction severity. Utilising the pipe repair dataset and Liquefaction Severity Number (LSN) maps generated from extensive geotechnical investigation following the CES events, new repair rate prediction models for water pipes subjected to liquefaction effects have been derived and are presented in this paper. Repair data from both earthquakes has been analysed independently and in combination, providing two sets of repair rate functions and different levels of uncertainty. Repair rate functions were first derived from pipes grouped by combination of diameter (i.e. ϕ < 75 mm or ϕ ≥ 75 mm) and material type (i.e. ductile or non-ductile). The models were then refined by adding correction factors for those material types and diameters with sufficient sample length. Correction factors were derived for AC, CI, PVC pipes of diameter ≥75 mm and for MDPE and HDPE80 pipes of diameter <75 mm. Galvanised Iron (GI) pipes performed poorly during the earthquakes, resulting in very high repair rates compared to the other non-ductile pipes of diameter <75 mm damaged in the network; this warranted a separate repair rate model to be developed for this pipe type. The proposed models can be used in risk assessment of water pipe networks; i.e. to estimate the number of pipe repairs from potential liquefaction damage from future earthquakes.

Administrative Decision-Making Under Conditions of Uncertainty

Abstract. Introduction. Enterprises of Ukraine today are at a new stage of development, which is determined both by pandemic events and changes of a political, financial and global nature, which significantly complicates the management decisionmaking. It is clear that making a management decision is an important stage in the company's activity. Usually, a decision is made as one of many alternatives that are mutually exclusive. It is believed that the decision that allows to obtain the maximum profit is rational. However, today, this is not quite the case. The effective functioning of the economic system as a whole and its individual subjects is determined by a certain level of uncertainty. It is impossible to thoroughly study its behavior without considering this level. Purpose. The purpose of the article is to study the concepts of uncertainty, clarify the levels of uncertainty and determine the features of management decision-making at certain stages of uncertainty in modern dynamic conditions. Results. Management decision-making in conditions of uncertainty is a process that results in the selection of an alternative based on the complex interaction of analysis, forecasting, optimization, economic justification, etc., which occurs in conditions of complete or partial lack of information. The article updates issues related to the functioning of enterprises in conditions of risk and uncertainty. A brief analysis of the crisis situation in which our country and the world found itself was conducted, it was compared with other similar situations, and the main consequences of this situation were outlined. Questions are raised regarding the problems of identification and timely assessment of the level of uncertainty at which the company found itself, since its correct definition will allow effective decisions to be made for further development. The concept of uncertainty is studied, the reasons for its occurrence are analyzed. The main levels of uncertainty and their signs, which determine the peculiarities of decision-making at its individual stages in modern dynamic conditions, are outlined and clarified. An analysis of the features of management decision-making in crisis conditions was carried out. Directions of company behavior and decision-making at different levels of uncertainty are proposed. Conclusions. Underestimating the uncertainty factor leads to choosing a strategy that will not allow the company to protect itself from challenges and threats. After conducting the analysis, we note the dependence of the risk degree on the level of uncertainty. To help in choosing an alternative, it is advisable to use a modern mathematical apparatus. The effectiveness of management decision-making depends on the correct definition of the level and type of uncertainty.

A Fuzzy Simultaneous Evaluation of Criteria and Alternatives (F-SECA) for Sustainable E-Waste Scenario Management

The process of production, consumption, and final disposal of electrical and electronic equipment usually leads to harmful waste to the environment called e-waste. Eliminating and decreasing this type of waste could be considered as an essential goal for many enterprises working toward sustainable management systems. In this paper, we aim at introducing a new methodology for evaluation of sustainable e-waste management scenarios. The evaluation is defined as an MCDM (Multi-Criteria Decision-Making) problem, and the scenarios are the alternatives of the problem that need to be evaluated with respect to several sustainability criteria. An extended fuzzy SECA (Simultaneous Evaluation of Criteria and Alternatives) integrated with SMART (Simple Multi-Attribute Rating Technique), named F-SECA, is proposed to deal with the evaluation process. The α-cut approach is used to consider different levels of uncertainty and obtain interval values for assessment of criteria and alternatives. The proposed methodology helps us to make the evaluation with incorporation of subjective and objective data, opinions of multiple experts and uncertainty of information. We applied the methodology to evaluate sustainable e-waste management scenarios in a case. Through comparative and sensitivity analyses, the paper shows that the proposed methodology is efficient and gives reliable results.

Numerical investigation on natural vibration characteristics of ship propulsion shafting under uncertainty based on nonparametric approach

In order to investigate the natural vibration characteristics of ship propulsion shafting under uncertainties, the random matrixes of mass, damping, and stiffness are constructed based on a nonparametric approach. The effect of uncertainties on the natural vibration characteristics of ship propulsion shafting is discussed by the distribution characteristics of natural frequency. Meanwhile, the natural frequency identification experiment of ship propulsion shafting was used to illustrate the effectiveness of the nonparametric modelling method. The results show that different levels of uncertainties in stiffness, mass and damping matrixes could be obtained by controlling the divergence parameter. The natural frequency could fluctuate significantly under uncertainties. Furthermore, the uncertainties of stiffness and mass have significant impact on the natural frequency. However, the uncertainty in damping has little influence on it. Therefore, it is essential to consider the uncertainties in the analysis of natural vibration characteristics of the ship propulsion shafting.

Formal and Clan Control as Antecedents to Coopetition Under Different Levels of Uncertainty

Coopetition is a paradoxical phenomenon that requires conscious managerial intervention to control its inherent tensions. However, if done right it can yield considerable benefits to the firm as well as the involved teams. Building on agency theory, this study investigates to what extent behavior and outcome control as well as clan control are antecedents to coopetition between teams within a firm. Further, the boundary condition of task uncertainty as a moderator in this relationship is examined. Our findings from a cross-sectional sample of 97 work teams in a large industrial company support the notion that the right control strategy is an important antecedent at specific uncertainty levels. Specifically, we show that outcome and clan control have positive relationships with coopetition. We identify a curvilinear effect of task uncertainty on the outcome control – coopetition link indicating that this control strategy functions best under moderate levels of uncertainty. Further, we find a weakening effect on the clan control – coopetition link. With these findings, the study contributes theoretically to agency theory by examining coopetition and task uncertainty, two instances of complexity for the efficacy of control strategies. Additionally, we contribute to both the control as well as the coopetition literature by offering an explicit theoretical link and empirically supporting this relationship.

Operation twins: production-intralogistics synchronisation in Industry 4.0

ABSTRACT The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties.

Mapping habitat loss in the deep-sea using current and past presences of Isidella elongata (Cnidaria: Alcyonacea)

Abstract The bamboo coral Isidella elongata is an engineering species that forms a characteristic biogenic habitat in the bathyal mud of the Mediterranean Sea. This habitat has been severely reduced in recent decades due to trawling impacts, and there is a growing concern about its conservation status. In this work, the habitat loss of I. elongata was computed using a novel approach that combines the realized niche of the species with the estimation of its past distribution (before trawling) to delineate potential areas of habitat loss with different levels of uncertainty. The realized niche of the species was modelled using only live colonies and including trawling effort as explanatory variable whereas the past distribution was estimated also using the leftovers of dead colonies as presences. Trawling effort had a statistically significant negative effect on the extent of the realized niche of I. elongata, confirming previous results on the impact of this pressure on its distribution. The novel approach used in this work has allowed us to map for the first time several areas of potential habitat loss for I. elongata in the studied area, opening new opportunities to provide this essential information for future management and restoration actions of vulnerable marine ecosystems worldwide.

Animal wastewater treatment with an improved combined Anaerobic-Aerobic System: Towards energy Self-Sufficiency

A combined anaerobic–aerobic (CAO) system is a feasible animal wastewater treatment approach, and one of the prerequisites for its sustainable application is to realise energy self-sufficiency. Here, we built an energy balance model to calculate the probability of achieving energy self-sufficiency in three CAO systems with multiple uncertainties through Monte Carlo simulation. Upon comparison with the conventional combined anaerobic–aerobic (C-CAO) system and partial raw wastewater bypassed combined anaerobic–aerobic (PRB-CAO) system, the matters pre-captured combined anaerobic–aerobic (MPC-CAO) system displayed the highest probability (45.5%) and stability (with 10.9% coefficient of variation) in achieving energy self-sufficiency with the uniformly distributed input parameters. The uncontrollable factor of ambient temperature mainly contributed to the CAO system’s net energy production among all input parameters. However, operating with the optimal control parameters of anaerobic digestion temperature (approximately 20 °C for C-CAO and PRB-CAO systems, 25–35 °C for MPC-CAO system) and organic loading rate (OLR) (50% of simulated maximum OLR) reduced the ambient temperature effect, thereby increasing the probability of energy self-sufficiency. And increasing the organic capture efficiency further reduced the effect of ambient temperature on net energy production of the MPC-CAO system. In addition, the probability of achieving energy self-sufficiency in the MPC-CAO system was more than 80%, with 30–50% of nitrogen captured simultaneously with organic matter and the direct use of anaerobic digestate (e.g., as fertilizer). These findings indicate that capturing more matter and increasing the capture efficiency is highly conducive for achieving energy self-sufficiency in CAO systems. Meanwhile, Monte Carlo simulation efficiently locates the key factors determining the CAO system’s energy production performance from the different levels of uncertainty of multi-parameters, which is demonstrated as a time saving method for further system evaluation, design and upgrade. However, additional factors such as capital investment, social and environmental impacts should also be further considered for increasing the robustness of energy balance simulation model and providing a comprehensive assessment of CAO system for animal wastewater treatment.

Regulating emerging technology in times of crisis: Digital contact tracing in Norway during the Covid-19 pandemic.

In times of crisis, emerging technology can pose major challenges for regulators. They must deal with great uncertainty and urgency related to both the crisis and the technology. To understand such situations, this article studies the revelatory case of privacy regulation of a contact‐tracing application called Smittestopp, created in Norway during the COVID‐19 crisis. Based on public and organizational documents and 48 interviews, the analysis shows that the Norwegian Data Protection Authority faced several options for regulatory intervention throughout the crisis, and adapted its approach based on intra‐crisis experience, regulatees’ responses, and different levels of uncertainty and urgency. Building on these findings, the study formulates propositions regarding the regulation of emerging technology during a crisis and regulatory agencies’ use of rule‐based, idea‐based, and norm‐based interventions. This study provides insight into how these three types of intervention relate to different aspects of a crisis situation. Furthermore, it stresses the importance of idea‐based intervention as a key site of analysis in studying technology that emerges during a crisis.

Minimod: An Open Source Python Package to Evaluate the Cost Effectiveness of Micronutrient Intervention Programs

ObjectivesMicronutrient (MN) deficiencies are a public health problem in many low- and middle-income countries. Given limited funds to invest in MN intervention programs, it is important to identify sets of interventions that are cost-effective. We describe a Python package, minimod, that leverages data on the benefits and costs of alternative intervention programs to find sets of economically optimal interventions over space and time. This software can be used to carry out Monte Carlo simulations and assess the stability of the results to different levels of uncertainty. We illustrate the functionality of this software using examples from the Micronutrient Intervention Modeling Project.Methodsminimod is an open-source python package that uses mixed-integer programming to find sets of intervention programs that maximize nutritional benefits subject to cost constraints or that minimize costs subject to a pre-specified benefit constraint. The software is designed for use with estimates of effective coverage (% of a population that achieves adequate MN intake), lives saved, or any other nutritional benefit data coupled with its costs. The package can then run Monte Carlo simulations under reasonable distribution assumptions, and it can be easily programmed to allow other distributions based on users’ needs.ResultsThis optimization procedure can yield the set of MN intervention programs that are cost-effective over time (e.g., over a 10-year time horizon) and space (e.g., across administrative regions). Results are summarized by several visualizations and a report of the frequency with which each intervention appears to be economically optimal across a given set of simulations. When applied to vitamin A programs in Cameroon, the model predicts that investment reallocation can both increase the number of children covered and reduce the cost per child.Conclusionsminimod is a tool that can be used by stakeholders and policymakers to identify cost-effective MN intervention programs over space and time. By comparing optimal solutions against current intervention programs, this tool highlights the potential cost savings associated with adopting more cost-effective policies.Funding SourcesThis research was supported by a grant to UC Davis from Helen Keller International through their support from the Bill & Melinda Gates Foundation.

Uncertainty of Preferences in the Assessment of Supply Chain Management Systems Using the PROMETHEE Method

The use of Supply Chain Management (SCM) systems allows for the improvement of an organization’s operations. Companies use many Enterprise Resource Planning (ERP) systems that also include SCM functionalities. As a result, the selection of the right system to be used in the enterprise is a complex problem. The use of multi-criteria decision aid (MCDA) methods provides the possibility of system ordering in a ranking, based on an asymmetric preference relation, symmetric indifference and incomparability relations. The aim of the article is to evaluate ERP systems in terms of their support for SCM. The scientific contribution of the article is the study of the impact of various degrees of uncertainty of the decision-maker’s preferences on the evaluation results and the analysis of the impact of various approaches to the preferences of alternatives on the final ranking. An approach based on MCDA Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used for the study. The decision model uses 12 criteria, three different preference functions and two variants of the PROMETHEE method (I and II). In this way, a total of six rankings were built, and each of them includes seven systems, supporting the management of the supply chain. As a result of the study, it was found that the highest functionality in logistics is characterized by the Oracle E-Business Suite system, which is more functional than SAP ERP and JD Edwards EnterpriseOne. The remaining analysed systems offer much less functionality. The applied approach, which was possible with the use of various preference functions, allowed three different levels of uncertainty in the preferences of decision-makers to be taken into account in the study. Moreover, the application of two different variants of the PROMETHEE method made it possible for the obtained solution to take into account the uncertainty of positions taken by individual ERP systems in the final rankings.

Learning quantile QoT models to address uncertainty over unseen lightpaths

Uncertainty in quality-of-transmission (QoT) estimation is traditionally addressed through empirical, myopic margins, ignoring the fact that each unseen lightpath is subject to different levels of uncertainty. To address this limitation, in this work, deep quantile regression is leveraged to finer capture QoT estimation uncertainty through the inference of margins that act discriminative over the unseen lightpaths. Specifically, deep-quantile regression is applied to approximate QoT models capable of inferring the QoT of unseen lightpaths, according to a predefined level of certainty. Quantile models automatically account for the uncertainty during inference, without the need to consider additional empirical margins for decision-making (i.e., the margins are learned and considered upon inference). It is shown that quantile QoT models lead to significant margin reduction when compared to baseline myopic margin schemes, resulting in more confident and network efficient allocation decisions.

Improved particle filter-based estimation of a quadrotor subjected to uncertainties

PurposeThe purpose of this paper is to present an improved particle filter-based attitude estimator for a quadrotor unmanned aerial vehicle (UAV) that addresses the degeneracy issues.Design/methodology/approachControl of a quadrotor is not sufficient enough without an estimator to eliminate the noise from low-cost sensors. In this work, particle filter-based attitude estimator is proposed and used for nonlinear quadrotor dynamics. But, since recursive Bayesian estimation steps may rise degeneracy issues, the proposed scheme is improved with four different and widely used resampling algorithms.FindingsRobustness of the proposed schemes is tested under various scenarios that include different levels of uncertainty and different particle sizes. Statistical analyses are conducted to assess the error performance of the schemes. According to the statistical analysis, the proposed estimators are capable of reducing sensor noise up to 5x, increasing signal to noise ratio up to 2.5x and reducing the uncertainty bounds up to 36x with root mean square value of as low as 0.0024, mean absolute error value of 0.036, respectively.Originality/valueTo the best of the authors’ knowledge, the originality of this paper is to propose a robust particle filter-based attitude estimator to eliminate the low-cost sensor errors of quadrotor UAVs.

The VEGAHUB Platform: The Philosophy and the Tools.

VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

Robust tracking control design for fractional-order interval type-2 fuzzy systems

This paper is concerned with an uncertainty and disturbance estimator-based tracking control problem for a class of interval type-2 fractional-order Takagi-Sugeno fuzzy systems subject to time-varying delays. The footprints of the uncertainty of the underlying fuzzy systems are taken into account to capture and model different levels of uncertainties. The uncertainty and disturbance estimator is used to promote the tracking behavior of rejecting disturbance in the control system. First, by applying the Lyapunov approach, we focus on the examination of stability and performance of the fractional-order tracking error system. Next, unknown system uncertainties, external disturbances and nonlinearities are accurately estimated via an appropriate filter design. Particularly, the proposed control technique does not require any prior knowledge about above said unknown factors and it only requires the bandwidth information about the low-pass filter. Then, four numerical examples with simulation results are presented in the end, to show the potential of the theoretical results of the proposed control method.

When Unequals Compete: Where Do They Stand After the Competition?

When two unequals compete, the stronger, more able, richer competitor commonly stands a better chance of winning. If the stronger competitor does win, this worsens the relative status of the weaker competitor even further. Does this result depend on the type of competition? Does it depend on the size of the reward to be won? In the present paper, we report an experimental study of how a very simple competitive mechanism can affect the relative standing of the weaker, poorer of two competitors. In a lab experiment with 208 participants, we employed competitions with different levels of uncertainty in how a winner was determined and with different sizes of rewards to be won, to explore effects on the relative standing of the weaker and stronger competitors. We used an investment game in which participants differing in their endowed budgets competed against one another, forfeiting their investment whether they won or lost. Two versions of the game were used: a simple all-pay-auction contest and a non-constant-sum Colonel Blotto contest (Roberson & Kvasov, 2012), both with players that were unequal in their budgets. Results revealed that, in line with published game-theoretic solutions, the relative standing of the weaker agents worsened following competition, and increasingly so the higher the rewards. At the same time, the effect was mitigated in the variant of the Colonel Blotto game, which involved more uncertainty in how the winner was determined.

A cognitive and participative decision-making model for transportation planning under different uncertainty levels

Decision-making processes related to transportation systems are often very complex, belonging to the class of “wicked problems” in social studies. The literature and the media abound in examples of transportation planning “failures” including decisions made, delayed or withdrawn without any traceable motivations, public opposition, public opposition, extra costs/implementation times and outright cancellation, wrong traffic/revenue forecasts. Planning failures stem from several causes, including technical errors in forecasting predictable effects, lack of consensus, new governance cycles and inability to recognize the intrinsic uncertainty affecting some key variables. The underlying assumption of this paper is that the quality of the decision-making process critically depends on how the process is structured and managed. The paper proposes a conceptual model to represent and organize transport planning as a cognitive decision-making process with strong involvement of different stakeholders in various stages, accounting for different levels of uncertainty and including short- and long-term decisions options. The result is a planning process that allows for changes in objectives, strategies and decisions during different stages of the process itself, as is usually the case when different governance and socio-economic cycles succeed over time. However, the process maintains a form of procedural consistency and is intended to counteract the “Penelope syndrome”, whereby decisions made under previous governance cycles are undone regardless of their merits and costs. The real-life implementation of the proposed model requires strong “political commitment” to an open and accountable decision-making process, a technical structure able to act as “process owner” coordinating technical and engagement activities, and a general societal attitude towards organized participation to public decision making. The proposed model was applied to the Regional Transportation Plan of Veneto in Italy where the above conditions applied. A number of lessons were learnt from that exercise: i) stakeholders’ engagement revealed an important potential to manage uncertainty and increase consensus; ii) flexibility and robustness with respect to medium–to–high uncertainty levels already recognized at the time of the plan (immediately before the COVID-19 crisis) were an appreciated feature of the plan; iii) separation between mature decisions and those needing further analysis was a success strategy, reducing political “decision costs” (from naysaying) by delaying decisions still recognizing underlying needs; iv) reduced decision time (a largely bi-partisan vote in less than one year).

Bi-objective facility location under uncertainty with an application in last-mile disaster relief

Multiple and usually conflicting objectives subject to data uncertainty are main features in many real-world problems. Consequently, in practice, decision-makers need to understand the trade-off between the objectives, considering different levels of uncertainty in order to choose a suitable solution. In this paper, we consider a two-stage bi-objective single source capacitated model as a base formulation for designing a last-mile network in disaster relief where one of the objectives is subject to demand uncertainty. We analyze scenario-based two-stage risk-neutral stochastic programming, adaptive (two-stage) robust optimization, and a two-stage risk-averse stochastic approach using conditional value-at-risk (CVaR). To cope with the bi-objective nature of the problem, we embed these concepts into two criterion space search frameworks, the epsilon -constraint method and the balanced box method, to determine the Pareto frontier. Additionally, a matheuristic technique is developed to obtain high-quality approximations of the Pareto frontier for large-size instances. In an extensive computational experiment, we evaluate and compare the performance of the applied approaches based on real-world data from a Thies drought case, Senegal.