Compromise Decision Research Articles

Adaptive formulation for probabilistic storm surge predictions through sharing of numerical simulation results across storm advisories

As a tropical storm/cyclone approaches landfall, real-time probabilistic predictions for the anticipated surge provide valuable information for emergency preparedness/response decisions. These probabilistic predictions are made through an uncertainty quantification process that involves: (i) generating a sufficiently large ensemble of storm scenarios based on the nominal storm advisory and the anticipated forecast errors; (ii) performing high-fidelity numerical simulations to obtain surge predictions for each storm scenario; and (iii) estimating surge statistics of interest by assembling the simulation results. This process is repeated whenever the nominal storm advisory is updated. The number of storm scenarios utilized in the analysis directly impacts the statistical accuracy of the probabilistic predictions; a larger ensemble improves accuracy but requires greater computational resources to provide predictions with the desired expediency to guide real-time decisions. This paper revisits two recently proposed Monte-Carlo (MC) frameworks that aim to improve accuracy without increasing the computational burden: adaptive importance sampling (AIS) and adaptive multi-fidelity Monte Carlo (AMFMC). The foundational concept behind them is similar: share numerical simulation results across the probabilistic predictions performed for different storm advisories to accelerate the MC estimation. This is achieved differently for each approach, through adaptive development of an importance sampling proposal density (for AIS) or a surrogate model (for AMFMC). Here, a direct comparison between these frameworks is established, focusing on the mechanisms for the information sharing and the challenges encountered in tuning the algorithm adaptive characteristics to provide probabilistic estimates across a large number of quantities of interest (QoIs), corresponding to the surge predictions for different locations within the coastal region of interest. As this large number results in conflicting choices for the adaptive characteristics, a compromise solution needs to be promoted. The efficacy of the two frameworks is examined in detail in this setting, comparing the accuracy of idealized implementations (adaptive decisions independently made for each QoI) to the accuracy of practical implementations (single, compromise decision within the MC implementation). The study also showcases the importance of information sharing across storm advisories in real-time probabilistic storm surge predictions and provides guidelines for an efficient adaptive MC formulation in such settings.

A Decision Support Framework for Robust Multilevel Co-Design Exploration of Manufacturing Supply Networks

Abstract The design of a manufacturing supply network (MSN) requires the consideration of decisions made by different groups at multiple levels and their interactions that include potential conflicts. Decisions are typically made based on information from computational simulations that are abstractions of reality and, therefore, embody uncertainty. This necessitates focusing on design space exploration to identify robust satisficing solution sets that are relatively insensitive to uncertainty. Current frameworks that support robust satisficing design space exploration are limited by their capability to support the efficient exploration of multilevel design spaces simultaneously. In this paper, we present the Framework for Robust Multilevel Co-Design Exploration (FRoMCoDE), a decision support framework that allows designers to (i) model decision problems across multiple levels and their interactions, (ii) consider uncertainties in the decision problems, and (iii) visualize and systematically carry out simultaneous exploration of multilevel design spaces, termed co-design exploration. In FRoMCoDE, we combine the coupled-compromise Decision Support Problem construct, where a combination of the Preemptive and Archimedean formulations is used, with robust design constructs and interpretable-Self-Organizing Maps (iSOM)-based visualization to facilitate robust co-design. We use a steel MSN problem with decisions made at two levels to test the framework. Using the problem, we demonstrate FRoMCoDE's efficacy in supporting designers in (i) modeling multilevel decision problems and their interactions, considering the uncertainties, and (ii) the efficient co-design exploration of multilevel design spaces. FRoMCoDE is generic and supports designers in the robust co-design exploration of multilevel systems.

A Multi-objective Optimization of Window and Light Shelf Design in Office Buildings to Improve Occupants’ Thermal and Visual Comfort

In office buildings, an efficient design of windows and using light shelves as a passive design strategy significantly influence the thermal and visual comfort of occupants while enhancing the productivity and health of users. This study proposes a multi-objective optimization for the optimal design of windows and light shelves in office buildings to improve occupants' comfort. Initially, a parametric model was developed using Grasshopper parametric software. Afterward, the Honeybee energy and daylight plugin was employed for simulating thermal and visual conditions, and finally, multi-objective optimization was conducted with the Octopus plugin. This plugin can determine the best solution as a compromise decision for maximizing occupants' comfort. In this paper, an office building in Tehran has been chosen as a case study. The decision variables are window-to-wall ratio (WWR), shading control strategy, viewpoint, the transmission of glass, light shelf length, and light shelf height. The objective functions of the study are the annual average Predicted Percentage of Dissatisfied (PPD) and the annual average Discomfort Glare Probability (DGP). According to the results, the proposed optimization model leads to an 18.5–70.1% and 9.3–57.1% reduction in DGP and PPD indexes, respectively. The study findings provide practical and useful instruction for architects to select optimal specifications of windows and light shelves to develop occupants' thermal and visual comfort in office buildings.

Design of Self-Organizing Systems Using Multi-Agent Reinforcement Learning and the Compromise Decision Support Problem Construct

Abstract In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behavior and are able to perform tasks specified by the designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper, we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. The proposed framework consists of two stages, namely, preliminary design followed by design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in the preliminary design. These surrogate models represent the goals of the cDSP. Our focus in this paper is to describe the framework. A multi-robot box-pushing problem is used as an example to test the framework’s efficacy. This framework is general and can be extended to design other multi-robot self-organizing systems.

Supply chain resilience: four perspectives on the concept

Presence of risk or perception of uncertainty in supply chains tends to induce unstable conditions and compromise decisions in organizations. This situation leads to the need to assess the resilience of these arrangements, understood as the capacity to coexist competitively with situations of tension in these chains. In this sense, the present study aimed to identify the understanding of this construct and the convergence between different approaches to this concept, present in recent literature, particularly accelerated by the advent of the global pandemic associated with COVID-19. Thus, a bibliometric study was developed on the SCOPUS base, concentrated in the period from 2015 to 2022, with the main documents containing the words resilience in supply chains and analyzed using the VOSViwer tool. The construction of a theoretical inventory on the resilience construct in supply chains indicated the presence of different perspectives on resilience. The critical comparison between these perspectives offered an understanding of these in terms of approach and research context. As main results, the presence of four perspectives of the resilience approach in supply chains was identified which, although relatable, present different patterns. This result provides a basis for comparisons and compositions of these perspectives, leading to the advancement of knowledge about this phenomenon.

Optimize or satisfice in engineering design?

In this paper, we address the issue of whether to optimize or satisfice in model-based engineering design. When dealing with operations research problems in the context of engineering design, one may encounter (i) nonlinear, nonconvex objectives and constraints, (ii) objectives with different units, and (iii) computational models that are abstractions of reality and fidelity, Seeking a single-point optimal solution that meets the necessary and sufficient Karush–Kuhn–Tucker (KKT) conditions makes it impossible to obtain a solution that satisfies all the targeted goals. Instead, a method to identify satisficing solutions that satisfies necessary KKT condition but not the sufficient condition is proposed. These solutions are relatively robust, easy to acquire, and often good enough. In this paper, we demonstrate the combined use of the compromise Decision Support Problems and the adaptive linear programming algorithm, as proposed by Mistree and co-authors. This method is appropriate in formulating design problems and obtaining solutions that satisfy only the necessary KKT condition. Further, the use of the proposed method circumvents complications associated with the use of gradient-based optimization algorithms typically used to solve optimization problems. We discuss the efficacy of our proposed method using four test problems to illustrate how the satisficing strategy outperforms the optimizing strategy in model-based engineering design.

An Adaptive Linear Programming Algorithm with Parameter Learning

When dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems. To realize the design with limited computational resources, problems with the features above need to be linearized and then solved using solution algorithms for linear programming. The adaptive linear programming (ALP) algorithm is an extension of the Sequential Linear Programming algorithm where a nonlinear compromise decision support problem (cDSP) is iteratively linearized, and the resulting linear programming problem is solved with satisficing solutions returned. The reduced move coefficient (RMC) is used to define how far away from the boundary the next linearization is to be performed, and currently, it is determined based on a heuristic. The choice of RMC significantly affects the efficacy of the linearization process and, hence, the rapidity of finding the solution. In this paper, we propose a rule-based parameter-learning procedure to vary the RMC at each iteration, thereby significantly increasing the speed of determining the ultimate solution. To demonstrate the efficacy of the ALP algorithm with parameter learning (ALPPL), we use an industry-inspired problem, namely, the integrated design of a hot-rolling process chain for the production of a steel rod. Using the proposed ALPPL, we can incorporate domain expertise to identify the most relevant criteria to evaluate the performance of the linearization algorithm, quantify the criteria as evaluation indices, and tune the RMC to return the solutions that fall into the most desired range of each evaluation index. Compared with the old ALP algorithm using the golden section search to update the RMC, the ALPPL improves the algorithm by identifying the RMC values with better linearization performance without adding computational complexity. The insensitive region of the RMC is better explored using the ALPPL—the ALP only explores the insensitive region twice, whereas the ALPPL explores four times throughout the iterations. With ALPPL, we have a more comprehensive definition of linearization performance—given multiple design scenarios, using evaluation indices (EIs) including the statistics of deviations, the numbers of binding (active) constraints and bounds, the numbers of accumulated linear constraints, and the number of iterations. The desired range of evaluation indices (DEI) is also learned during the iterations. The RMC value that brings the most EIs into the DEI is returned as the best RMC, which ensures a balance between the accuracy of the linearization and the robustness of the solutions. For our test problem, the hot-rolling process chain, the ALP returns the best RMC in twelve iterations considering only the deviation as the linearization performance index, whereas the ALPPL returns the best RMC in fourteen iterations considering multiple EIs. The complexity of both the ALP and the ALPPL is O(n2). The parameter-learning steps can be customized to improve the parameter determination of other algorithms.

Development of optimization mathematical models for making compromise decisions on the efficiency of an agricultural moving power units in fleet

In order to build a calculation routine for making compromise decisions on the efficiency of a fleet of agricultural moving power units, this work analyzed mathematical models characterizing the efficiency of the agricultural moving power units fleet in the context of digitalization of agriculture. In order to select initial data for solving a multicriteria optimization problem for making compromise decisions, the main quality criteria for the efficiency of an agricultural moving power units fleet and their functional limitations were selected. A formulation of the problem of multi-criteria optimization of the efficiency of the agricultural moving power units in fleet and a list of variable parameters, in the amount of 14, as well as the range of their variation have been generated for further adjustment of the problem statement for making compromise decisions when assessing the efficiency of the agricultural moving power units in fleet. In the future, when forming a mathematical and software complex, according to multi-criteria assessment, they will be significant for the decision maker (DM)

Optimal power flow research of AC–DC hybrid grid with multiple energy routers

AC/DC hybrid grids are the future development trends of grids, and energy routers (ERs) can play key roles in controlling power flow, realizing the regulation of electric energy and improving power quality in the grids. At present, there have been many studies on the structure and control strategy of ERs, but only a few studies are on the power flow distribution and optimization of AC/DC hybrid grids with ERs. To enhance the efficacy of ERs in system-wide regulation and operational optimization, a multi-objective optimal power flow model is formulated for AC–DC hybrid systems with multiple ERs. In order to make ERs play a better role in overall regulation and operation optimization, the multi-objective optimal power flow model of AC–DC hybrid system with multi-ERS is established based on Newton–Raphson method (NR) and alternating iteration method with power consumption and voltage deviation as optimization objectives. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the model, and a smoother Pareto solution set is obtained compared with multi-objective particle swarm optimization (MOPSO). Then, fuzzy membership method is used to make compromise decision on the Pareto solution set to obtain the optimal compromise solution. Simulation and analysis on an enhanced IEEE 69 bus system verifies the validity of the model. In addition, the results obtained from the compromise solution are compared with the optimization results of MOPF obtained by MOPSO and that of single-objective power flow (SOPF) obtained by Particle Swarm Algorithm (PSO) in the same system. It is proved that the optimization model established can achieve the optimization objectives of reducing voltage deviation and power loss simultaneously to improve the overall performance of the system and optimize the operation state of the system.

Designing self-organizing systems using surrogate models and the compromise decision support problem construct

In this paper, we address the following question:How can self-organizing system designers steer the system towards expected global behaviors that satisfy multiple conflicting performance indicators?Self-organizing systems (SOS) have advantages in performing tasks in exploratory and hazardous domains that are not suitable for humans. The design of the SOS is however difficult because negative emergence with unwanted behaviors is likely to happen and multiple conflicting performance indicators need to be considered. To address this challenge, in this paper, we propose an SOS design method using surrogate models and the compromise Decision Support Problem (cDSP) construct. Surrogate models are used to capture the relationship between low-level rules or parameters and high-level emerging system performance. And the cDSP construct is used to explore “good enough” solutions (characterized by rule adoption rates) while managing the trade-offs among conflicting performance indicators. The efficacy of the proposed method is illustrated using a multi-agent box-pushing problem in the Webots simulation environment. It is shown in the results that our method leads to a 6.9% improvement in time efficiency, an 8.4% improvement in energy efficiency, and 26.2% in system reliability.

Ensemble Variance Reduction Methods for Stochastic Mixed-Integer Programming and their Application to the Stochastic Facility Location Problem

Sample average approximation (SAA), the standard approach to stochastic mixed-integer programming, does not provide guidance for cases with limited computational budgets. In such settings, variance reduction is critical in identifying good decisions. This paper explores two closely related ensemble methods to determine effective decisions with a probabilistic guarantee. (a) The first approach recommends a decision by coordinating aggregation in the space of decisions as well as aggregation of objective values. This combination of aggregation methods generalizes the bagging method and the “compromise decision” of stochastic linear programming. Combining these concepts, we propose a stopping rule that provides an upper bound on the probability of early termination. (b) The second approach applies efficient computational budget allocation for objective function evaluation and contributes to identifying the best solution with a predicted lower bound on the probability of correct selection. It also reduces the variance of the upper bound estimate at optimality. Furthermore, it adaptively selects the evaluation sample size. Both approaches provide approximately optimal solutions even in cases with a huge number of scenarios, especially when scenarios are generated by using oracles/simulators. Finally, we demonstrate the effectiveness of these methods via extensive computational results for “megascale” (extremely large scale) stochastic facility location problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by The Office of Naval Research [Grant N00014-20-1-2077] and the Air Force Office of Scientific Research [Grant FA9550-20-1-0006]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0324 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0324 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Approval of gender equality in parliaments of the world and Ukraine

The article is devoted to the study of the issue of gender equality in the parliament of Ukraine and other countries of the world. Attention to such a problem is conditioned by the fact that Ukraine, as a legal state, has undertaken to develop state policy taking into account gender aspects. This is reflected in the implementation of the standards of international legislation, implementing them in national legislation, state programs for the implementation of gender equality, etc. The author draws attention to the fact that it is especially important to take into account gender aspects in parliamentary activity. Parliament as a legislative body has a significant influence on policy formation, strategic decision-making, and the level of efficiency in solving key issues. A gender-balanced composition of the parliament is a guarantee that decisions will reflect the different views and needs of women and men, resulting in more objective, rational and compromise decisions that take into account the political, economic, social and cultural composition of society. It was analyzed that the equal representation of women and men in the country’s parliament is an important indicator of the degree of solving gender problems in society. It is emphasized that the initiative to introduce gender equality in the parliament acts not only as a tool of political development, but also as a key element in the formation of new thinking and gender consciousness.
 By popularizing the concept of gender equality, the parliament acts as a catalyst for changes in the perception of the roles of women and men in society. By actively supporting and increasing the participation of women in politics and management, a new view and perception of women is being built in society and the expansion of her opportunities in all spheres of life, including science, economy, culture and primarily the social sphere.
 The article notes that the increase in women’s participation in the country’s political life is influenced by gender quotas provided for in the electoral law, the main purpose of which is to promote changes in the gender balance in society, and not just a temporary increase in the number of women in parliament.

Realisation of responsive and sustainable reconfigurable manufacturing systems

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing.

Towards pre-emptive resilience in military supply chains: A compromise decision support model-based approach

ABSTRACT The complex and dynamic nature of military supply chains (MSC) requires constant vigilance to sense potential vulnerabilities. Several studies have employed decision support models for the optimization of their operations. These models are often limited to a best single-point solution unsuitable for complex MSC constellations. In this article, the authors present a novel approach based on decision support models to explore a range of satisficing solutions against disruptions in MSCs using a compromise Decision Support Problem (cDSP) construct and Decision Support in the Design of Engineered Systems (DSIDES). Two cases were evaluated: (1) a baseline scenario with no disruption and (2) with disruption to achieve target values of three goals: (1) minimizing lead time, (2) maximizing demand fulfilment and (3) maximizing vehicle utilization. The results obtained in Case 1 identified a more stable solution space with minimal deviations from the target value, while in Case 2 the solution space was unstable with deviations from the target values

A decision analysis model for material selection using simple ranking process

A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods’ outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

A novel collaborative decision-making method based on generalized abductive learning for resolving design conflicts

In complex product design, lots of time and resources are consumed to choose a preference-based compromise decision from non-inferior preliminary design models with multi-objective conflicts. However, since complex products involve intensive multi-domain knowledge, preference is not only a comprehensive representation of objective data and subjective knowledge but also characterized by fuzzy and uncertain. In recent years, enormous challenges are involved in the design process, within the increasing complexity of preference. This article mainly proposes a novel decision-making method based on generalized abductive learning (G-ABL) to achieve autonomous and efficient decision-making driven by data and knowledge collaboratively. The proposed G-ABL framework, containing three cores: classifier, abductive kernel, and abductive machine, supports preference integration from data and fuzzy knowledge. In particular, a subtle improvement is presented for WK-means based on the entropy weight method (EWM) to address the local static weight problem caused by the fixed data preferences as the decision set is locally invariant. Furthermore, fuzzy comprehensive evaluation (FCE) and Pearson correlation are adopted to quantify domain knowledge and obtain abducted labels. Multi-objective weighted calculations are utilized only to label and compare solutions in the final decision set. Finally, an engineering application is provided to verify the effectiveness of the proposed method, and the superiority of which is illustrated by comparative analysis.

Corporate Integrated Management of Groups and Associations of Enterprises

The article is devoted to the theoretical justification and development of methodical support for the formation of the system of integrated management of groups of enterprises. The essence, content and principles of building an integrated management system are revealed. The methodical approach to the implementation of group development tasks based on the coordination of group members’ interests has been improved. The integration trends of economic development are considered. The definition of integrated management as a complex functional management system, which ensures the development of interdependent management solutions, each of which makes its contribution to the overall performance of enterprises and the group as a whole, has been improved. It was determined that the formation of any group of enterprises and the integration of the management systems of its individual members should be carried out on a systemic basis, for which the use of holaric methods of analysis of sub-holistic objects is proposed. It is proposed to consider each enterprise as a separate hierarchical level in the group structure, and the subject of integrated management includes all actions and relations between group members and within each of them. It is proposed to build a system of integrated management based on certain principles, which are proposed to be divided into: general principles of integrated management of a group of enterprises; principles of organizing the integrated management process; principles of organization of integrated management structures; cybernetic principles of managing complex systems. Therefore, the system of integrated management should be implemented on a comprehensive basis, ensuring the interdependent solution of the tasks and obtaining high final results of the activities of all members of the group. Accordingly, it is proposed to consider the group as a set of chains of added value creation and distribution of logistics interaction, in which the members of the group are considered as separate links of the logistics system. Thus, an integrated logistics network is created, the main goal of which is to create such organizational and economic conditions that will allow maximum satisfaction of the requirements of all members of the group by making compromise decisions and overcoming inconsistencies between various elements of the system. To facilitate the construction of such a network, the concept of an integrated supply chain was introduced and the stages of its construction were highlighted, a typology of logistic relationships of group members was developed, and a scheme of interaction between value chains was presented, oriented to the emergence of a synergistic effect for the group as a whole. Based on the analysis of the integration trends of the economy, it was established that in any case, the main reasons for the ineffectiveness of the group association may be either the choice of the wrong partner or the poor management of the new entity. This led to the development in the work of the technology for evaluating the organization and the effectiveness of the group’s activity, based on the calculation of certain parameters and the development of ways to bring the indicators of the group’s activity to the optimal value. At the same time, it is proposed to select several groups of indicators and their corresponding assessment methods. The first group includes indicators of the efficiency of the integrated management system, which reflect the final results of the group’s functioning. The second group of indicators, which characterizes the content of the integrated management process, takes into account the current costs of maintaining the management apparatus and its provision. Another developed group of indicators will characterize the configuration of a group of enterprises and assess the compliance of the SIUGP with the object of management. These are such indicators as: the density of the group, the degree of its centralization, homogeneity, closedness, prevalence, structural autonomy and similarity, the closeness and strength of business ties, the stability of the network. Their calculation will allow a more thorough approach to the solution of the main tasks that arise before the integrated management system.

Experience in the fight against separatism in Belgium

Many countries in the modern world have faced with the problem of separatism, which can threaten not only the well-being of one individual state, but also the world order as a whole. Thus, it is necessary to study and use the experience of countering this phenomenon on the example of other countries, in particular, Belgium. In this country, there were social contradictions, and eventually the separatist sentiments throughout the entire period of independence. The purpose of this research is to study the methods of combating separatism in the history of Belgium and to analyze the possible use of these methods to counter separatist sentiments at the present stage of development of this country. In the course of this study, a number of methods of theoretical knowledge were used: analysis, synthesis, abstraction, concretization and generalization. As a result of the study, it was determined that different ethnic groups were formed on this territory due to the events unfolded on the territory of modern Belgium, back in the days of ancient history. In the course of the study, it was stated that after gaining independence, two mature national communities were combined in one state. The domination of Wallonia over Flanders was the cause of social contradictions. It has been determined that reaching a compromise is the main method of counteracting social protests and separatism, which manifested in many historical events. It is substantiated that the use of forceful methods of struggle was ineffective. Simultaneously with the use of method of compromise decisions, the principle of balance in the federal authorities is important for Belgium. The results made it possible to note that many decisions (although they were able to preserve the unity of Belgium) increased social contradictions between ethnic groups. This study puts into science a new perspective on the issues of combating separatism in Belgium, and also provides an analysis of how these methods affect the modernity and what variants are possible for the solution of modern problems

A process simulation-based framework for resource, food, and ecology trade-off by optimizing irrigation and N management

Improving food production and resource utilization efficiency and mitigating the functional degradation of agroecosystems are major challenges worldwide. However, few studies have reconciled the conflicts among resources, food, and ecology (RFE) in agricultural production. This study develops a water–carbon–nitrogen process simulation-based framework for RFE trade-off by coupling the agro-hydrological model, i.e., AHC (Agro-Hydrological & chemical and Crop systems simulator) with the ecosystem service value (ESV) assessment method and decision-making method, i.e., VIKOR (VlseKriterijuska Optimizacija I Komoromisno Resenje). The framework is capable of (1) quantifying farmland ESVs; (2) reconciling conflicts among resource utilization, food production and ecosystem development by optimizing farmland irrigation and nitrogen (N) application strategies (FINS). The framework was used to analyze the FINS, and then to obtain the compromise decision focusing on a lower negative ESV and the maximizing group utility decision focusing on a higher yield and positive ESV for spring wheat farmland in Northwest China. Results indicated that under the local FINS, i.e., 450 mm irrigation and 340 kg ha−1 N application, the total ESV generated by spring wheat farmland per season was [4.76, 4.87] × 104 CNY ha−1. Among these ESVs, every 1 CNY ha−1 positive ESV generation was accompanied by approximately 0.11 CNY ha−1 negative ESV. Compared with the local FINS, the compromise decision, i.e., the FINS with 210 mm irrigation and 200 kg ha−1N application, significantly reduced negative ESV but caused slight losses in yield and positive ESV, while the maximizing group utility decision, i.e., the FINS with 210 mm irrigation and 260 kg ha−1N application, increased yield and positive ESV but resulted in a small negative ESV mitigation. Moreover, both FINSs could save irrigation water and N fertilizer resources, and improve water and N productivity to various degrees. This framework has excellent performance in reconciling conflicts among RFE systems and generating coordinated solutions and has potential to achieve sustainable development goals in agricultural RFE systems.

Между „заплаха“ и „герой“: княз Александър І Български и Германия

In 1879, when Alexander von Battenberg became ruler of Bulgaria as a compromise decision of the European powers in their search for balance, he faced a difficult situation. It took him a lot of strength to handle the different crises. When the long-term break with Russia occurred, he tried to achieve the cooperation of Germany. He had friends in Berlin, but he also had even more powerful enemies. The analysis of sources that have not yet been evaluated on the topic shows that Alexander I waited with hope for the change of the throne in Berlin. This is because the political plans of the Crown Prince’s family were also connected with Bulgaria and his career. A high-level political game began, which put Alexander at the centre of the politics of the German chancellor Otto von Bismarck for years, as well as of European relations. This power struggle, marked by intrigues and threats, was to end in the defeat of Alexander and his allies. The Bulgarian ruler was forced to abdicate, not so much because of his own mistakes, but as a result of the conflicts among the Great Powers and their efforts to maintain balance. The whole situation had negative consequences for Bismarck’s political course as well.