Decision Model For Management Research Articles

Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Groundwater surface (GWS), which denotes the vertical extent of the water table or the volume of subterranean water within geologic formations, is pivotal for effective groundwater resource management. Accurately predicting GWS requires comprehensive and precise data to fully understand the influencing factors. The inherent temporal complexity and often incomplete datasets of GWS pose significant challenges to accurate assessments. This research aims to devise a comprehensive method that merges interpolation and prediction techniques to develop a functional model and dynamic system for GWS prediction. The study was conducted on the Azarshahr Plain aquifer in Iran, involving 34 observation wells with partially or entirely missing data. Initial analysis utilized three interpolation methods—Kriging, Support Vector Machine (SVM), and M5P—with the M5P method emerging as the most accurate, evidenced by the lowest Root Mean Square Error (RMSE) of 1.83. Two subsequent scenarios were examined: (1) using the M5P method to interpolate missing data for all 34 wells, and (2) using only data from 15 wells with complete records. GWS levels were predicted using Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models. Comparative analysis highlighted the superior performance of the CNN model in both scenarios, particularly noting its effectiveness in GWS prediction. The improvement of data quality through interpolation significantly enhanced predictive accuracy by approximately 90 percent, thereby increasing the reliability of the predictive models for future groundwater management decisions.

Design and optimization of dynamic reliability-driven order allocation and inventory management decision model.

Efficient order allocation and inventory management are essential for the success of supply chain operations in today's dynamic and competitive business environment. This research introduces an innovative decision-making model incorporating dependability factors into redesigning and optimizing order allocation and inventory management systems. The proposed model aims to enhance the overall reliability of supply chain operations by integrating stochastic factors such as demand fluctuations, lead time uncertainty, and variable supplier performance. The system, named Dynamic Reliability-Driven Order Allocation and Inventory Management (DROAIM), combines stochastic models, reliability-based supplier evaluation, dynamic algorithms, and real-time analytics to create a robust and flexible framework for supply chain operations. It evaluates the dependability of suppliers, transportation networks, and internal procedures, offering a comprehensive approach to managing supply chain operations. A case study and simulations were conducted to assess the efficacy of the proposed approach. The findings demonstrate significant improvements in the overall reliability of supply chain operations, reduced stockout occurrences, and optimized inventory levels. Additionally, the model shows adaptability to various industry-specific challenges, making it a versatile tool for practitioners aiming to enhance their supply chain resilience. Ultimately, this research contributes to existing knowledge by providing a thorough decision-making framework incorporating dependability factors into order allocation and inventory management processes. Practitioners and experts can implement this framework to address uncertainties in their operations.

Fuzzy Logic Decision Model for Robust Risk Management in ubiquitous environment- A Review

Soft computing models are invaluable resources for solving addressing real-world issues. Soft computing is the process of solving difficult computer problems with imprecise yet practical results through the application of approximate computations. The method enables the resolution of issues that might be too difficult or time-consuming to handle with the technology available today. The term ‘computational intelligence’ is often used interchangeably with soft computing. This article will discuss various soft computing strategies, with a particular focus on the fuzzy logic approach. The study delves into risk management in the real world using fuzzy logic techniques across multiple fields, providing a comprehensive review and comparing performance analyses.

Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty

Given the steep rises in renewable energy's proportion in the global energy mix expected over several decades, a systematic way to plan the long-term deployment is needed. The main challenges are how to handle the significant uncertainties in technologies and market dynamics over a large time horizon. The problem is further complicated by the fast-timescale volatility of renewable energy sources, potentially causing grid instability and unfulfilled demands. As a remedy, energy storage and power-to-hydrogen systems are considered in conjunction with energy management system but doing so raises the complexity of the planning problem further. In this work, the long-term capacity planning for a hybrid microgrid (HM) system is formulated as a multi-period stochastic decision problem that considers uncertainties occurring at multiple timescales. Long-term capacity decisions are inherently linked with energy dispatch and storage decisions occurring at fast-timescale and it is best to solve for them simultaneously. However, the computational demand for solving it becomes quickly intractable with problem size. To this end, we propose to develop a Markov decision process (MDP) formulation of the problem and use simulation-based reinforcement learning for multi-period capacity investments of the planning horizon. The MDP includes the policies used for dispatch and storage operation, which are represented by linear programming as a part of the simulation model. The effectiveness of our proposed method is demonstrated with a case study, where decisions over multiple decades are considered along with various uncertainties of multi-timescales. Economic and environmental assessments are performed, providing valuable guidelines for government's energy policy.

A study of forest management plans based on single-objective optimization

The global decline in forest area and massive greenhouse gas emissions have increased concern about the scale of forest carbon sinks, but the social value of forests as a source of timber and of forests themselves for tourism is also a focus of attention. The balance between forest carbon sinks and other values becomes a research question in this paper. We propose a forest management plan for forest managers by developing a carbon sink model and a forest management decision model. First, we developed a carbon sequestration model to determine how much carbon dioxide can be sequestered by the forest and its products over time. Then, we developed a single-objective optimization model with the goal of maximizing the amount of carbon sequestered. To develop a forest management plan, we selected six influencing factors: carbon storage, forest trees and their products, biodiversity, soil water storage, climate regulation, and landscape value, and then used hierarchical analysis to determine the influence weights of each factor, and then built a forest management decision model based on the single-objective optimization model. For specific forests, we initialize the model with specific parameters of the influencing factors and determine the transition points so that the model can be suitable for diverse forests and make a targeted management plan.

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections.

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC<0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β=0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.

Study on the Shape Characteristics and the Allometry of Phalaenopsis Leaves for Greenhouse Management.

Phalaenopsis orchids are highly economical ornamental potted plants. Controlling their production schedule requires information on the leaf development characteristics of the orchids. Phalaenopsis leaves affect the plant's photosynthesis, respiration, and transpiration. The leaf growth conditions can serve as a development index for greenhouse management. The use of the growth characteristics of Phalaenopsis leaves as the basis for greenhouse cultivation and management needs to be studied. The allometry of Phalaenopsis leaves is worth studying. The goal of this research was to investigate the allometry of Phalaenopsis leaves and develop prediction models of the total leaf area. Then, these total leaf area models were developed and validated. In this study, five Phalaenopsis varieties (amabilis, Sin-Yuan beauty, Ruey Lish beauty, Ishin KHM1095, and Sogo F1091) were selected. Each sample had five mature leaves. The lengths, widths, and areas of the sequential leaves were measured, and then the length ratios, width ratios, and area ratios were calculated. The top and bottom models were used to calculate the total leaf areas. The results indicate that no significant differences could be found in the length ratios, width ratios, and area ratios of the sequential leaves from the same variety. However, significant differences were found in these leaf characteristics between different varieties. The observation of leaf growth characteristics can be used to provide useful information for Phalaenopsis management. Comparing the predictive criteria of the two models, the top model had a better predictive ability than the bottom model. From a practical viewpoint, measuring the top leaf area is easier than measuring the bottom leaf area in a greenhouse operation. Comparing the effects of the sample numbers on the predictive ability of the model, the sample number of 30 was sufficient to ensure the accuracy of the total leaf area measurements. We provide an easy and accurate method to measure the total leaf area of Phalaenopsis. The calculated values of total leaf areas can be incorporated into decision models for smart management.

A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks

The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

Нормативное значение индивидуального пожарного риска как основа модели и алгоритма поддержки управленческих решений по обеспечению пожарной безопасности Вьетнама

PURPOSE. Determining standard value of individual fire risk (IFR) is necessary to improve management decisions in the field of fire safety in Vietnam. As evidenced in practice, in countries with the established IFR standard the process of supporting management decisions in fire safety has been improved. Therefore, reasonable IFR standard establishment and its application in models and algorithms for making management decisions to reduce fire risks is a crucial task that is solved in this article. METHODS. Mathematical statistics and system analysis methods are used in the work. FINDINGS. Based on the results of the study, algorithm for determining and standard individual fire risk specific value for Vietnam have been obtained as well as model and algorithm for management decisions support to ensure fire safety in Vietnam. RESEARCH APPLICATION FIELD. The results of the study can be used as rationale for management decisions to ensure fire safety in Vietnam. CONCLUSIONS. Based on the developed algorithm, it has been established that individual fire risk standard value for Vietnam should be 1.34⋅10–6 victims/inh. year. This means that death toll from fires in Vietnam should not exceed 1.34 victims per 1 million people a year. Standard individual fire risk value for Vietnam should range within 3.7⋅10–8 &lt; 1.34⋅10–6 &lt; 3.7⋅10–6, which has been established by the authors in previous studies. Applying the model and algorithm for supporting management decisions to ensure fire safety in Vietnam will increase the country’s fire protection effectiveness.

Human Resource Management Decision System University Based on Decision Model

The purpose of university personnel management is to improve the efficiency of running a school by optimizing the allocation of university human resources. Personnel management is one of the most important aspects of university administration, as it plays a key role in the formation of discipline teams and management teams. The use of advanced information technology is to improve the efficiency of personnel management, the establishment of university human resources analysis system, and to promote the school management work. Combining with the achievements of office automation and decision support development, this study analyzes the realization methods and key technologies of human resource management in university decision system, discusses the significance of human resource management in university decision system analysis based on decision model, and constructs human resource management in university decision system. This study first introduces the basic content of human resource management decision model and makes a comprehensive analysis of human resource demand prediction and then constructs a human resource management in university decision model according to the analysis structure. After defining the construction path of the decision‐making model of human resource management in colleges and universities, the human resource grading and promotion analysis is carried out to additionally define the prediction of human resource demand in universities. Finally, the decision system is realized and tested by means of mathematical model.

Public economic management decision model based on improved ant colony algorithm

Public economic management decision model based on improved ant colony algorithm

Editorial to the Inaugural Issue

Dear editors, reviewers, authors, readers, and other members of the whole scientific and professional community, Welcome to this inaugural issue of Information Dynamics and Applications (IDA)! On behalf of the editorial office, I am writing with great pleasure and sincere privilege to present the first issue of the journal IDA. In the last few years, there is a growing necessity for information dynamics, especially with the extensive application of artificial intelligence in virtually all fields of science, economy, and life. It can be observed that many conflicting indicators are involved in the complex nature and society systems. The conflicting aims need to be balanced by developing and applying intelligent management decision models, which represents a significant challenge in uncertain environments, given the limited resources, time, and funds available. In addition, an important task in modern society is the possibility to make adequate decisions in real time through quantitative and qualitative analysis. Such analysis enables decision-makers to make real-time decisions by looking at the real needs of the companies and the market at any moment. This first issue aims to give answers to many important questions related to the scope of the journal. This issue consists of five very interesting high-quality papers, which apply decision models in various fields: in the Integration of Ontology Transformation into Hidden Markov Model, in the Geographic Routing Protocols in Wireless Sensor Network, in the Heterogeneous Sources at Different Levels of Granularity. In addition, digital marketing, quality models, MCDM model, and intelligent models related to information dynamics are discussed in this issue, shedding new lights on various management problems. Information Dynamics and Applications (IDA) (ISSN 2958-1486) is a fully refereed international open access journal, which publishes original research results in all subjects related to the dynamics and applications of information technology. The mission of the journal is to promote interdisciplinary research centered around the key issues of information processing, storage, and transmission. We welcome original submissions in various forms, including reviews, regular research papers, and short communications as well as Special Issues on particular topics. Our special interest lies in new analytical and application techniques of information technology in different fields of science and engineering. We follow principle of international diversity regarding the editorial board, reviewers and authors. Therefore, we invite and welcome submissions from around the world. All information related to IDA journal are available in our website: https://www.acadlore.com/journals/ida.

Research on Equipment Management Decision Model based on Semantic Analysis

In recent years, the industrial automation of cigarette enterprises has made great progress, and the underlying data acquisition, centralized control and condition monitoring systems have been established. The management has also established a large number of application systems. There are a large number of equipment data resources in these industrial automation systems and application systems, but incremental data value mining is difficult to really solve the problem. The exploration of big data and machine learning has gradually encountered bottlenecks and ceilings, Lack of integration of domain knowledge, it is difficult to mine high-value application scenarios. From the point of view of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application, which is the basis of effective data collection and management. From the perspective of "semantic analysis" and "equipment management", it is necessary to establish a unified method of data collection and application.

Berth planning and real-time disruption recovery: a simulation study for a tidal port

With the increasing volume of container freight transport, future port planning is crucial. Simulation models provide a means to gain insight in the effects of terminal expansions. Detailed simulations incorporate berth allocation: assigning vessels a time and location at the quay wall, where the vessel is loaded and unloaded. This article develops decision models for both offline preliminary berth planning and for online recovery of this plan during simulation. First, we develop an optimisation-based approach that incorporates realistic aspects—cyclic vessel arrivals, tidal windows, and minimisation of vessel draught during low water periods—in order to develop a cyclic baseline berth allocation plan. The approach can proactively incorporate slack for increased robustness. Exploiting a constraint-based solver, we can obtain optimal or satisficing solutions for a year’s operation of a large port. The resulting preliminary berth plan is used as a basis for the arrival times. However, disruptions can occur, such as vessel arrival and loading times varying from the planned. Hence, second, we develop a real-time disruption management decision model. This multi-level heuristic approach reacts to disruptions while minimising perturbation of the original berth plan. Computational experiments with a high-resolution simulator show our recovery approach finds good solutions until a tipping point of disturbance. Results also show that when the expected occupation of a terminal is higher, strengthening robustness of the preliminary plan has increased importance. The approach described in the article is implemented for a major European inland tidal port, forming the basis of a simulation-based decision support tool for operational planning and exploring port expansion options.

Urban drainage decision model for storm emergency management based on multi-objective optimization

This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 km2 watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging.

A general optimal adaptive framework for managing a threatened species

AbstractManagers must determine which interventions best protect threatened species when the outcomes of interventions are uncertain. Adaptive management is a dynamic optimization approach that generates optimal management actions based on current knowledge while learning to improve future management outcomes. Although adaptive management theory is well‐developed, uptake has been impeded by its complexity and a tendency to develop bespoke solutions with high implementation costs for problem‐specific returns.To increase uptake of adaptive management and improve threat management for species recovery, we developed a general adaptive management decision model, framed as a Mixed Observability Markov Decision process. We embraced principles of generality, simplicity and interpretability to overcome previous implementation challenges. We created a general model structure that is applicable to any species–threat combination, thus avoiding the need to develop customized models for every species. Simplicity was achieved by minimizing states to reduce the information requirements for parameterization. To improve interpretability, we implemented our method as a Shiny application and employed a recent artificial intelligence approach to simplify the optimal strategy. We applied our approach to a case study of fox impacts on a threatened marsupial.Our case study shows that when one management action is robust to uncertainty, the value of information of optimal adaptive management may be low. Cases like these highlight species–threat combinations where investment in adaptive management is not required.Our tool provides a rapid prototype adaptive management approach with minimal cost to management agencies. Our simple yet general model structure improves efficiency for implementing adaptive management for large numbers of threatened species, improving the effectiveness of conservation investments.

Research on Forest Management Decisions Based on Optimal Carbon Sequestration Model

According to the carbon cycle flow chart (Figure 1), this paper divides the total carbon sequestration into forest carbon sequestration and forest product carbon sequestration, and uses this to build a carbon sink model. Since the model does not consider environmental factors, this paper selects 11 environmental factors and calculates their weights by entropy weight method and TOPSIS analysis (Table 1), and thus derives linear equations of environmental factors, which are combined with the carbon sink model to predict the amount of forest carbon sequestration. Through a survey of forest management plans across the country, this paper designs a forest management plan that combines various factors such as environmental topography (Table 6), and sets green indicator parameters to classify cities by combining actual data from 31 cities across the country. Finally, Beijing, China was selected to test the predictive carbon sequestration model, and two cities, Taiyuan and Xiamen, were selected for serious scenario 2 and 3 decision results and prediction results to derive the best forest management decision model.

Optimization-based integrated decision model for smart resource management in the petrochemical industry

In this study, a new decision-supporting platform for the overall supply chain management of the petrochemical industries was proposed to maximize business profits. The proposed system integrates various decision-supporting models that address the critical challenges, both vertically and horizontally. Specifically, horizontal integration includes various decision-based problems along with the productive flow on the value chain from raw material purchasing and manufacturing to the final product sales, while vertical integration involves critical decisions at different levels, including the enterprise supply chain, the plant scheduling/planning, and the process operation. The optimization-based decision platform effectively supports the business's supply chain management, plant planning, and process operation strategy. The platform integrates different decision and featuring models, including a price prediction and a paper trading model for reducing the financial risks, a mathematical model of naphtha thermal cracker for identifying the optimal operating, and an optimization model for maximizing business profits. As a result, business profit was improved by 5.30% with only paper trade optimization, 6.67% with optimal operating conditions combined with price forecasting, and 11.98% with overall supply chain optimization. Thereby, the proposed platform assists decision-makers in determining the timing and quantity of raw material purchases and final product sales, as well as the operation strategies for process facilities, utilities, and inventory management. This study could be used to aid in the establishment of annual planning and scheduling as an auxiliary indicator for business operations.

Outsource or not? An AHP Based Decision Model for Information Security Management

Abstract Purpose: Outsourcing information security has proven to be an efficient solution for information security management; however, it may not be the most suitable approach for every organization. This research aimed to develop a multi-criteria decision-making model that would enable organizations to determine which approach to information security management (outsourcing or internal management) is more suitable for their needs and capabilities. Methods: Our study utilized several different research methods. First, the decision criteria were identified by reviewing related work and then selected by information security experts in a focus group. Second, a survey was conducted among information security practitioners to assign the criteria weights. Third, four use cases were conducted with four real-world organizations to assess the usability, ease of use, and usefulness of the developed model. Results: We developed a ten-criteria model based on the analytic hierarchy process. The survey results promote performance-related criteria as more important than efficiency-focused criteria. Evidence from use cases proves that the decision model is useful and appropriate for various organizations. Conclusion: To make informed decisions on approaching information security management, organizations must first conduct a thorough analysis of their capabilities and needs and investigate potential external contractors. In such a case, the proposed model can serve as a useful support tool in the decision-making process to obtain clear recommendations tailored to factual circumstances.

МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ОРГАНИЗАЦИОННЫХ СИСТЕМ

Introduction. Modern rates of development of information systems and web-technologies are increasingly being introduced into the life of all mankind, including organizational systems, which requires the improvement of management and decision-making processes. In modern sources of literature, the consideration of management processes is given on the basis of analysis, but from the side of the synthesis of the mathematical model of management decisions, they are not considered, which allows us to consider the ongoing research as an urgent task. For the head of the organizational system, the solution of the problem of management is the most important task based on the complex interaction of all departments of the managed system. Practical relevance: When a destructive impact occurs in a managed system, a decision-maker should have a management decision model that allows timely management decisions to counter emerging threats. Counteraction is carried out at the expense of the resources available to the decision-maker. The mathematical model of a management decision makes it possible to respond in a timely manner to emerging threats in the system and make appropriate management decisions with a given level of the efficiency indicator of the decision being made. The application of the Kolmogorov differential equations makes it possible to link the finding of a mathematical modeling system with various states of the controlled system. Discussion: The resulting indicator of the effectiveness of the decision makes it possible to link three states of the system when considering the target process, namely, the process of threat formation, the process of threat identification and the process of threat neutralization. By setting the required indicator of the effectiveness of a management decision, it allows the manager to respond in a timely manner to the destructive impact, to use the available resources to ensure the achievement of the management goal.