Hierarchical Decision Structure Research Articles

USING THE GEOSPATIAL MULTI-CRITERIA DECISION ANALYSIS MODEL AND METHODS FOR SOIL DEGRADATION RISK MAPPING

Modern methods of spatial analysis and modeling are increasingly being combined with decision-making methods and fuzzy set theory. The latter are actively integrated into the environment of Geographic Information Systems (GIS), such as well-known ones like ArcGIS or QGIS, in the form of separate tools, plugins, or Python scripts. Decision-making methods allow structuring the problem in geographical space, as well as taking into account the knowledge and judgments of experts and the preferences of the decision-maker in determining the priorities of alternative solutions. This paper provides a description of a geospatial multi-criteria decision analysis model, which allows addressing a wide range of ecological and socio-economic issues. An example of applying this model to map soil degradation risk in Ukraine is presented in the paper. According to the object-spatial approach, the properties of a territory are determined as the result of the action (impact) of a set of objects (processes) belonging to this territory. The territory is represented as a two-dimensional discrete grid, each point of which (local area) is an alternative. The set of local areas of the territory constitutes the set of alternatives. The representation of the territory model as a system of objects and relationships between them allows justifying the choice of a set of criteria (factors) for assessing soil degradation risk. Each criterion is a separate raster layer of the map. To build a hierarchical decision-making structure and calculate the importance coefficients of the criteria, the Analytic Hierarchy Process (AHP) method is used. To account for uncertainty in assessments and judgments of experts at the stages of standardization of alternative attributes by different criteria and aggregation of their assessments, expert membership functions for fuzzy sets and fuzzy quantifiers are applied. The particular feature of the proposed multi-criteria decision analysis model is its low computational complexity and ease of integration into the GIS environment.

Иерархическая система ранжирования коммерческих банков

Introduction. The main objective of the study is to develop economic and mathematical tools for constructing a rating barometer of Russian commercial banks according to the level of sustainability in a long term view using a hierarchical decision-making structure. Theoretical analysis. The method of commercial banks’ integral ranking is based on a system of hierarchical analysis. The methodology is based on ranking indicators and dividing them into subgroups. The priority indicator indicates the ranking vector, two levels of data ranking are performed, and subindices are formed. At the last stage, a convolution into an integral index is performed. The mathematical apparatus of a tree-like solution structure is used; the assumption of the method is the choice of priority indicators based on the principle of a commercial bank’s long-term sustainability. Empirical analysis. An algorithm has been compiled for condensing primary financial reporting indicators into an integral indicator. The methodology was tested on data from Russian commercial banks. Results. A mathematical apparatus is presented that makes it possible to assess the long-term financial stability of commercial banks according to their key indicators, an integral rating of banks is constructed for financial stability and investment attractiveness. The hierarchical rating system of commercial banks developed by the authors characterizes the analyzed banks by the size of the bank’s own funds, capital structure and return on equity.

A study on policy decisions to embed flexibility for reactive recovery in the planning and scheduling process in operating rooms

The pursuit for achieving operational excellence in the operating room (OR) department is hampered by the uncertainty characterising the demand for healthcare resources. Incorporating this uncertainty is complicated because planning and scheduling decisions involving both resources and patients are typically organised according to a hierarchical decision structure in different phases. In this study, we link strategic, tactical, and operational decision-making and investigate the impact of policy decisions providing flexibility in the OR planning and scheduling process for reactive recovery to improve the operational outcome, achieving a trade-off between efficiency and consistency. We consider a sequential but interrelated proactive–reactive decision framework that is guided by both generic assumptions stated in the literature and real-life practice, mimicking all decision phases by relying on optimisation and trace-drive simulation. The analysis is performed via computational experimentation on a real-life dataset by varying the parameter settings associated with the availability of open OR capacity, clustering of surgeons, and duration of OR blocks. We compare the performance of well-known scheduling frameworks, namely, open booking and (modified) block booking, and provide insights regarding the necessary conditions to efficiently manage OR resources. The results demonstrate that constructing a master surgery schedule or an advance patient planning improves schedule consistency, whereas the re-scheduling of OR resources is indispensable to attain high OR utilisation. Insights are provided into the impact of different policy decisions that introduce flexibility in the OR planning and scheduling process to increase responsiveness to demand variability. To achieve satisfactory results, a certain degree of flexibility should be embedded via at least one of the policy decisions. However, together with potentially higher efficiency, higher variability in performance is observed such that different policy decisions should be attuned to each other.

A Stackelberg game model for large-scale group decision making based on cooperative incentives

With the rapid development of novel technological paradigms such as e-democracy, e-government, and collective intelligence, large-scale group decision making (LSGDM) has become an emerging topic. In LSGDM, a clustering algorithm is usually adopted to cluster experts into multiple subgroups with the aim of reducing the dimension of the problem. Because of the clustering process, a large group spans three hierarchies of a moderator, multiple subgroup spokesmen, and corresponding experts in each subgroup. The moderator first proposes an incentive to encourage subgroups to modify their opinions to reach an expected degree of consensus. Then, in each subgroup, the spokesman invokes sub-games with involved experts to negotiate the number of changed opinions through allocating incentives. Considering such a hierarchical decision-making structure, this study introduces a hierarchical Stackelberg game model to address the interactions between different players. A critical value is proposed to define the reward or punishment according to the degree of cooperativeness of experts and subgroups. The existence and uniqueness of the Stackelberg equilibrium is verified. We also put forward a consensus model for group decision making based on the Stackelberg equilibrium. A numerical example is provided to demonstrate the applicability of the model, and numerical studies are given to investigate the influence of some parameters.

Lessons For Enhancing Competitive Advantage: Dynamic Capabilities Framework

In turbulent business environments, building and exploiting competitive advantages necessitate manufacturing companies to start new strategic initiatives. For this purpose, new product development (NPD) should be seen as dynamic capabilities (DC) that enable companies to create a better match between the configuration of a firm’s resources and changing conditions. This study aims to make use of the operationalised DC model including sensing, seizing and reconfiguration capacities in a hierarchical decision structure. A multi-case-studies approach is employed for validation of the DC assessment model on automotive companies which are categorized as European, Japanese and American. The qualitative analysis reveals the appropriate management practices underlying the micro-foundation of DC at the operational management level, which can help chief executives to implement the appropriate strategy for enhancing NPD performance. Managers can creatively leverage their companies’ DC components by conceiving of different ways to integrate these DC components into NPD projects.

High throughput screen of small molecules as potential countermeasures to galactic cosmic radiation induced cellular dysfunction

Space travel increases galactic cosmic ray exposure to flight crews and this is significantly elevated once travel moves beyond low Earth orbit. This includes combinations of high energy protons and heavy ions such as 56Fe or 16O. There are distinct differences in the biological response to low-energy transfer (x-rays) or high-energy transfer (High-LET). However, given the relatively low fluence rate of exposure during flight operations, it might be possible to manage these deleterious effects using small molecules currently available. Virtually all reports to date examining small molecule management of radiation exposure are based on low-LET challenges. To that end an FDA approved drug library (725 drugs) was used to perform a high throughput screen of cultured cells following exposure to galactic cosmic radiation. The H9c2 myoblasts, ES-D3 pluripotent cells, and Hy926 endothelial cell lines were exposed to a single exposure (75 cGy) using the 5-ion GCRsim protocol developed at the NASA Space Radiation Laboratory (NSRL). Following GCR exposure cells were maintained for up to two weeks. For each drug (@10µM), a hierarchical cumulative score was developed incorporating measures of mitochondrial and cellular function, oxidant stress and cell senescence. The top 160 scores were retested following a similar protocol using 1µM of each drug. Within the 160 drugs, 33 are considered to have an anti-inflammatory capacity, while others also indirectly suppressed pro-inflammatory pathways or had noted antioxidant capacity. Lead candidates came from different drug classes that included angiotensin converting enzyme inhibitors or AT1 antagonists, COX2 inhibitors, as well as drugs mediated by histamine receptors. Surprisingly, different classes of anti-diabetic medications were observed to be useful including sulfonylureas and metformin. Using a hierarchical decision structure, we have identified several lead candidates. That no one drug or even drug class was completely successful across all parameters tested suggests the complexity of managing the consequences of galactic cosmic radiation exposure.

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure.

Objective: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. Methods and procedures: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. Results: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. Conclusion: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. Clinical impact: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

An optimization decision-making framework for the optimal operation strategy of pumped storage hydropower system under extreme conditions

Frequent condition changes of pumped storage hydropower system make it inclined to stuck in extreme energy conversion process, posing great threats to steady operation. This paper proposes a comprehensive framework to specify optimal operation strategy for admirable transient process under extreme conditions. Three principles consist the framework, the refined model of successive load rejection is primarily established, which accedes time-varying and strong nonlinear characteristics and accurately grasps the trait of successive load rejection. Furthermore, an improved genetic algorithm with excellent capabilities of exploration and exploitation is proposed, multiple control schemes are investigated with variations of control parameters. Whereafter, a hierarchical decision-making structure of operation strategy considering qualitative and quantitative indicators is firstly constructed, the intuitionistic fuzzy analytic hierarchy process is innovatively introduced to specify optimal scheme. The originality of the framework lies in comprehensiveness and completeness. Multiple complexities of refined model, multiple schemes of optimization strategy, multiple determinants of decision-making are fully considered. Testifying to real cases, the optimal strategy can effectively improve water pressure indicators of volute and draft tube by at most 64.4 m and 32.52 m. These results highlight the effectiveness and availability of the proposed framework for sustaining safe operation simultaneously reducing investments of pumped storage hydropower systems.

Early detection of pancreatic cancers by novel nanobiosensor-based protease biomarkers using hierarchical decision structure.

e16273 Background: There is a critical need to develop fast, reliable, and cost-effective methods for the detection of pancreatic cancer (PC) at the earliest stage to maximize the impact of treatment. To-date, early detection of PC is close to impossible due to the location of the pancreas and the absence of characteristic symptoms in early cancer stages. Methods: Our team of clinicians and scientists has established a fast and reliable nanobiosensor technology that comprises iron/iron oxide nanoparticles attached to a protease or arginase activatable FRET pair (tetrakis (4- carboxyphenyl) porphyrin (TCPP) /cyanine 5.5). Arginase and seven proteases (MMP1, 3, and 9, cathepsin B, and E, urokinase plasminogen activator, and neutrophil elastase) were identified using the Gene Expression Omnibus (GEO) web tool based on their different expression pattern in pancreatic cancer patients, pancreatitis and healthy control subjects. Protease/arginase activities were measured in serum after 1h of incubation. Based on this data, a novel engineering approach to improved early stage detection of pancreatic cancer is reported here. This study was funded by American Cancer Society Institutional Research Grant (IRG‐16‐194‐07), awarded to the University of Kansas Medical Center. Results: In our study, 159 patients were enrolled at KU Cancer Center from 2000-2019, 47 with metastatic PC, 36 with localized PC, 26 pancreatitis and 50 healthy controls using KUCC Biospecimen Repository. The problem of early stage detection of pancreatic cancer can be modeled as a multi-class classification problem. Conventional classification approaches provide at most 77% accuracy for the dataset under consideration. A new hierarchical decision structure with specific feature engineering at each step is introduced here to improve the performance of the classifier. The fundamental premise of this information fusion-based framework involves tailoring the statistically most significant features with appropriate weights to execute an efficient binary classification task at each hierarchical step. An overall accuracy of 95% was achieved for the detection of patients with early pancreatic cancer (see table). Conclusions: Because of the dire survival statistics of pancreatic cancer, detection at the earliest possible time by means of a liquid biopsy will offer the greatest benefit. Novel nanobiosensor based protease biomarkers achieved high accuracy in early detection of pancreatic cancers by applying hierarchical decision structure. Our results need validation in a larger cohort. Predicted true class considering the following combination of classification methods: Step1 – kNN*, step2 – kNN*, step3 – RFC* (Accuracy = 94.97%).[Table: see text]

FramePests: A Comprehensive Framework for Crop Pests Modeling and Forecasting

Crop pests are among the greatest threats to food security, generating broad economic, social, and environmental impacts. These pests interact with their hosts and the environment through complex pathways, and it is increasingly common to find professionals from different areas gathering into projects that attempt to deal with this complexity. We propose a framework called FramePests guiding steps and activities for crop pest modeling and forecasting. From theoretical references about carrying out mappings and systematic reviews of the literature, the framework proposes a series of steps leading to a state of science as a knowledge base for modeling tasks. Then, two modeling solutions, based on data and knowledge are used. Finally, the model outputs and performances are compared. The application of the proposed framework was demonstrated for coffee leaf rust modeling, for which we obtained a data-based model built using a gradient boosting algorithm ( XGBoost ) with a mean absolute error of 7.19% and a knowledge-based model represented by a hierarchical multi-criteria decision structure with an accuracy of 56.03%. A complementary study for our case study allowed us to explore how elements of a data-based model can improve a knowledge-based model, improving its accuracy by 7.07%. and showed that knowledge-based modeling can be an alternative to data-based modeling when the available dataset has approximately 60 instances. Data-based models tend to have better performance, but their replicability is conditioned by the diversity in the dataset used. Knowledge-based models may be simpler but allow expert supervision, and these models are not usually tied to specific sites.

Incentive-Based Coded Distributed Computing Management for Latency Reduction in IoT Services—A Game Theoretic Approach

This article studies distributed computing (DC) mechanisms on heterogeneous mobile devices (MDs) for latency reduction (LR) in Internet-of-Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded DC mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs' participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD's computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the DC, and it follows the MDs' decisions to determine the load allocation for each MD. Then, we design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of LR on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs' social welfare.

Market Analysis of Distributed Learning Resource Management for Internet of Things: A Game-Theoretic Approach

In this article, to meet a delay requirement for data analytics from Internet-of-Things (IoT) devices, we design a novel market model of the distributed learning resource management mechanism for multiple mobile-edge computing (MEC) operators. We consider a hybrid architecture (cloud-MEC) for the distributed learning, which is also known as “federated learning” as one of the practical examples, where a coordinator at the cloud coordinates IoT sensors to efficiently distribute their sensing data over multiple MECs. In this sense, multiple MECs receive a shared model from the coordinator and conduct local training of received partial sensing data from IoT sensors. Then, the coordinator at the cloud merges returned local training results from MECs and generates a global model. To model a hierarchical decision-making structure as a market behavior, we formulate and solve a Stackelberg game model. Specifically, in the case of MEC operators as leaders, we design a pricing scheme for MEC operators to obtain its maximum utility by considering a tradeoff between the revenue and energy consumption. Then, as a follower, while the coordinator aims at achieving a balance between the satisfaction attained from the distributed learning and the costs, it follows the MEC operators’ decisions by coordinating IoT sensors to distribute their sensing data over MECs. A unique Stackelberg equilibrium (SE) point is given as a closed form. Finally, we reveal that the SE solution maximizes the utility of all market participants. This game-theoretic study demonstrates that there is an incentive for utilizing multiple MECs to achieve better satisfaction of IoT services in distributed learning.

On the interaction and integration of production planning and (advanced) process control

Process operations typically follow a hierarchical decision-making structure,where the flow of information is predominantly “downwards:” business functions (planning/scheduling) provide targets for the control layers. Promoting “upwards” information flow has recently received significant consideration, in view of integrating decision-making and creating feedback mechanisms that improve process economics. This work considers the interaction of production planning and process control, motivated by the practical observation that control decisions may have a strong “butterfly” effect on the implementation of a production plan, which may induce a significant gap between planned and realized profit. We trace this issue to the practice of sharing process degrees of freedom as decision variables in both the planning and control problems. A new planning approach is proposed, whereby potentially shared degrees of freedom are reserved for process control, and represented as uncertain parameters in the planning problem. The statistical properties of the parameters are learned from historical process operating data. An extensive case study is presented to support the theoretical developments.

Controllability of Nash Equilibrium in Game-Based Control Systems

Controlling complex systems to desired states is of primary importance in science and engineering. In the classical control framework, the plants to be controlled usually do not have their own payoff or objective functions; however, this is not the case in many practical situations in, for examples, social, economic, and “intelligent” engineering systems. This motivates our introduction of the game-based control system (GBCS), which has a hierarchical decision-making structure: one regulator and multiple agents. The regulator is regarded as the global controller that makes decision first, and then, the agents try to optimize their respective objective functions to reach a possible Nash equilibrium as a result of noncooperative dynamic game. A fundamental issue in the GBCS is: Is it possible for the regulator to change the macrostates by regulating the Nash equilibrium formed by the agents at the lower level? This leads to the investigation of controllability of the Nash equilibrium of the GBCS. In this paper, we will first formulate this new problem in a general nonlinear framework and then focus on linear systems. Some explicit necessary and sufficient algebraic conditions on the controllability of the Nash equilibrium are given for a linear GBCS, by solving the controllability problem of the associated forward and backward dynamic equations, which is a key technical issue and has rarely been explored in the literature.

Quality Enhancement Network via Multi-Reconstruction Recursive Residual Learning for Video Coding

Lossy compression algorithms introduce multiple compression artifacts that severely decrease visual quality. These compression artifacts are highly related to texture contents, and the hierarchical coding units decision structure also brings multi-scale similarity to these artifacts. Current loop filters fail to utilize these characteristics to comprehensively remove compression artifacts. To this end, this letter proposes a novel quality enhancement method by adopting a multi-reconstruction recurrent residual network (MRRN). In particular, a modified recursive residual structure is designed to capture the multi-scale similarity of compression artifact. To effectively enhance frames with uneven noise, a multi-reconstruction structure is proposed, which outputs images with different denoise ratios and adaptively fuses them. Experimental results show that the proposed MRRN can improve coding efficiency up to 15.1% compared with the original loop filter in high-efficiency video coding. Averagely, 6.7%, 7.8%, 7.6% BD-rate reduction is achieved for all intra, low-delay P, and low-delay B, respectively. Meanwhile, as a quality enhancement method performed at encoder side, MRRN also achieves a good balance between coding performance and computational complexity compared to the state-of-the-art methods.

Analysis of Activity Location and Trip Mode Choice – A Study on Hierarchical Ordering

This paper focusses on preliminary analyses conducted to investigate the hierarchical decision-making structure of location and mode choice problems for non-work/school activities. The travel behaviour of two streams of individuals are studied separately. Nested logit models are tested using full information maximum likelihood method after estimating independent models for location and mode choice. The results show that individuals are more inclined to change their destination rather than their mode. The architectural framework of TASHA, the operational agent-based activity-based travel demand model of the Greater Toronto and Hamilton Area, will be redesigned in the light of these results and further analyses.

A mixed-integer linear optimization model for a two-echelon agribusiness supply chain

We formulate and solve a mixed-integer linear program to model an agribusiness two-echelon supply chain between an agro-industrial producer and a wholesaler. The presented model is an equivalent reformulation of a bilevel linear program, using the well known KKT conditions and a disjunction approach on the complementary constraints. It includes a hierarchical decision structure that contemplates an agro-industrial producer in the upper level, who maximizes its profit by delineating the field into management zones and assigning to each one a specific crop rotation plan. In turn, in the lower level is the wholesaler who minimizes the penalization cost of lost sales that could experiment in order to satisfy the demand requirement according to the units provided by the producer. We present a case study to show the relevance of the proposed methodology.

Formulating optimal business process change decisions using a computational hierarchical change management structure framework

PurposeThis paper first aims to examine how business process change decisions (BPCDs) were implemented in a government organisation bound by tightly coupled temporal constraints (TTCs). Second, it focuses on how to achieve optimal and efficient BPCDs that require tight compliance with regulators’ temporal constraints. Finally, it formulates a rigorous framework that can facilitate the execution of optimal BPCDs with maximum efficiency and minimal effort, time and cost.Design/methodology/approachDecision-making biases by individuals or groups in organisations can impede optimal BPC implementation; to demonstrate this, a case study is investigated and the formulated framework is applied to tackle these failings.FindingsThe case study analysis shows 76 per cent of the BPCDs implemented were inefficient, mostly because of poor decisions, and these resulted in negative ripple effects. In response, the newly developed hierarchical change management structure (HCMS) framework was used to empower organisations to execute high-velocity BPCDs, enabling them to handle any temporal constraints imposed by regulators or other exogenous factors. The HCMS framework was found to be highly effective, scoring an average improvement of more than 100 per cent when measured using decision quality dimensions. This paper would be of value for business executives and strategic decision makers engaging with BPC.Research limitations/implicationsThe HCMS framework has been applied in a single case study as a proof of concept. Future research could extend its application to broader domains that have multi-attribute structures and environments. The evaluation processes of the proposed framework are based on subjective metrics. Causal links from the framework to business process metrics will provide a more complete performance picture.Practical implicationsThe outcome of this research assists in formulating a systematic BPCD framework that is otherwise unavailable. The practical use of the proposed framework would potentially impact on quality outcomes for organisations. The model is derived from decision trees and analytical hierarchical processes and is tailored to address this problematic area. The proposed HCMS framework would help organisations to execute efficient BPCDs with minimal time, effort and cost. The HCMS framework contributes to the academic literature on BPCD that leverages diverse stakeholders to engage in BPC initiatives.Originality/valueThe research presents a novel framework –HCMS – that provides a platform for organisations to easily determine and solve hierarchical decision structure problems, thereby allowing them to efficiently automate and institutionalise optimal BPCDs.

Optimal storage and transmission investments in a bilevel electricity market model

This paper analyzes the interplay of transmission and storage investments in a multistage game that we translate into a bilevel market model. In particular, on the first level we assume that a transmission system operator chooses optimal line investments and a corresponding optimal network fee. On the second level we model competitive firms that trade energy on a zonal market with limited transmission capacities and decide on their optimal storage facility investments. To the best of our knowledge, we are the first to analyze interdependent transmission and storage facility investments in a zonal market environment that accounts for the described hierarchical decision structure. As a first best benchmark, we also present an integrated, single-level problem that may be interpreted as a long-run nodal pricing model. Our numerical results show that adequate storage facility investments of firms may in general have the potential to reduce the amount of line investments of the transmission system operator. However, our bilevel zonal pricing model may yield inefficient investments in storages, which may be accompanied by suboptimal network facility extensions as compared to the nodal pricing benchmark. In this context, the chosen zonal configuration of the network will highly influence the equilibrium investment outcomes including the size and location of the newly invested facilities. As zonal pricing is used for instance in Australia or Europe, our models may be seen as valuable tools for evaluating different regulatory policy options in the context of long-run investments in storage and network facilities.

Development of a multi­criteria model for making decisions on the location of solid waste landfills

We have developed a structure of the multi-criteria model of decision making related to determine optimal sites for the location of solid waste landfills (using the south of Odessa oblast as an example). A special feature of the model is the integration of GIS and the multi-criteria methods of decision-making. Based on the created raster criteria maps in the GIS geodatabase and expert estimation of the significance of criteria, we ranked alternatives according to the degree of suitability. A multilevel hierarchical decision-making structure includes three groups of criteria: environmental, physical, and socio-economic; it takes into consideration the state building requirements to the construction of SW polygons. Such an approach provides for the acceptability of results of the analysis by most stakeholders.The simulation is performed for the three scenarios that imply the aggregation of layers of criteria into a combined map of suitability using Boolean logic, fuzzy logic, and a combination of methods of weighted overlay and a fuzzy analysis of hierarchies. To account for the uncertainty of original information and subjectivity in expert assessments, we employed an apparatus of fuzzy logic. Piecewise-linear membership functions of the fuzzy set are proposed for the standardization of criteria. We calculated weights of criteria using a modified method of the analysis of hierarchies, in which we used linguistic variables represented by triangular fuzzy numbers to perform the paired comparison of criteria significance.The results of the simulation show that the use of operations of a fuzzy intersection or a fuzzy combination in order to aggregate a combined suitability map can lead to errors related to the underestimation or overestimation of alternatives. The most acceptable method is a weighted linear combination, or the operation of fuzzy geometric averaging, in cases when it is difficult or impossible to determine the weight of criteria.