Search Results (47)

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

Effective management of the hydrogen supply chain (HSC), starting with supplier selection, is crucial for advancing the hydrogen industry and economy. Supplier selection, a complex Multi-Criteria Decision Analysis (MCDA) problem in an inherently uncertain environment, requires careful consideration. This study proposes a novel hybrid MCDA framework that integrates the Bayesian Best–Worst Method (BWM), Fuzzy Analytic Hierarchy Process (AHP), and Entropy Weight Method (EWM) for robust criteria weighting, which is aggregated using a game theory-based model to resolve inconsistencies and capture the complementary strengths of each technique. Next, the globally weighted criteria, emphasizing sustainability performance and techno-risk considerations, are used in the TODIM method grounded in prospect theory to rank hydrogen suppliers. Numerical experiments demonstrate the approach’s ability to enhance decision robustness compared to standalone MCDA methods. The comparative evaluation and sensitivity analysis confirm the stability and reliability of the proposed method, offering valuable insights for strategic supplier selection in the evolving hydrogen landscape in the HSC. Full article

A conflict is a situation where multiple stakeholders have different evaluations over possible scenarios or states. Conflict analysis is an essential tool for understanding and resolving complex conflicts, especially in scenarios involving multiple stakeholders and uncertainties. Confrontation analysis (ConAna) and graph model for conflict resolution (GMCR) have been integrated for dilemma reasoning and conflict resolution in region crisis analysis. This paper discusses the application of decision-theoretic rough sets (DTRS) to three-way decisions (3WD) in dilemma reasoning and conflict resolution. Three-way decisions are a strategy for making decisions under uncertain conditions, which compensates for the shortcomings of traditional two-way decisions (such as accept or reject) by introducing a “delayed decision” option. In terms of dilemma reasoning, we try to address incomplete or conflicting information and provide a more reasonable decision path for decision-makers through comprehensive evaluation of multi-criteria. In terms of conflict resolution, the DTRS model seeks a compromising solution that is acceptable to all parties by analyzing the game relationship between different stakeholders. The DTRS model combines decision-making theory and rough set theory to determine the balanced decision region by constructing a game between multiple criteria. This dynamic integration is of great significance for the study of complex international conflicts, providing a cross-disciplinary perspective for related research. In this paper, we demonstrate the application of DTRS in 3WD and discuss the relationship between DTRS and probabilistic rough sets. The research shows that the DTRS model has significant advantages in dealing with complex decision problems and can effectively deal with the conflicts and uncertainties in multi-criteria decision-making. Full article

Landslides threaten railway safety and operational sustainability. This study developed a game theory-based weighting method that integrates the Entropy Weight Method (EWM) and CRITIC with Analytic Hierarchy Process (AHP) techniques to determine indicator weights, reducing single-method biases. A risk assessment was conducted that coupled hazard likelihood with exposure. These components formed a comprehensive risk index visualized as a landslide risk map. A GIS-integrated assessment of Shandong Province railways incorporated multi-source data to support resilient infrastructure planning. The results show that high-risk zones consistently coincide with mountainous terrain, high-precipitation areas, and concentration of the population/economic activity, identifying critical intervention areas. The integrated weighting method proves effective for multi-criteria risk analysis. Decision-makers can prioritize mitigation measures using these insights, enhancing railway resilience and reducing regional disaster risk. Full article

Risk assessment in tunnel construction using the drilling and blasting method presents a complex multi-criteria decision-making challenge due to numerous interacting factors. This study develops an advanced risk assessment model integrating game theory-based combination weighting with nonlinear fuzzy analytic hierarchy process (FAHP). The methodology establishes a comprehensive risk evaluation system through the systematic coupling of a work breakdown structure (WBS) and a risk breakdown structure (RBS), effectively combining subjective weights from an analytic hierarchy process (AHP) with objective weights derived through principal component analysis (PCA). A specialized nonlinear operator addresses the inherent fuzziness in the risk evaluation processes. The model is applied to the Daliangshan No. 1 Tunnel flat guide entrance drilling and blasting construction section, with the risk level determined to be high. Detailed analysis further revealed that the detonation network reliability and ventilation system performance constituted the most significant secondary risk elements. Comparative validation demonstrates the model’s superior accuracy over conventional methods in both weight determination and risk classification. The results demonstrate the effectiveness of the proposed model in improving risk assessment accuracy and supporting decision-making in complex tunnel construction environments. Full article

This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges. Full article

Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology. Full article

Decision-making involves selecting the best alternative based on evaluation criteria while considering environmental impacts. The translation of environmental factors into quantifiable mathematical expressions is challenging due to the inherent uncertainties. Decision-makers can address the subjective characteristics of alternatives by incorporating fuzzy set theory into decision-making processes where uncertainty and ambiguity exist. Game theory is introduced as another approach to enhance the robustness of decision-making models, leading to more informed and flexible decision outcomes. This approach promotes strategic thinking and aids decision-making by allowing individuals to visualize the potential consequences of different decisions under various conditions. This study proposes a fuzzy multi-criteria decision support system that provides a structured framework to address the complexities of agricultural land selection. The decision support system employs a two-person zero-sum game to identify the optimal land management option, considering the strategic interactions between players. The results from the payoff matrix reveal the equilibrium point, providing an ideal solution for more effective land use planning decisions. Full article

Video games are recognized as significant tools and mediums to be used in civic participation in spatial planning and fostering local communities. As the phenomenon is widely recognized in papers presenting singular case studies and broader analyses in the field, selecting such serious games with certain characteristics remains unclear. The informed process of choosing games with particular properties regarding genesis, graphic style, genre, and complexity as the response for specified needs and process assumptions appears to be supportive in preventing unnecessary costs and data overproduction. Such avoidance is an important part of sustainable digital transformation. Therefore, there is a need for a more conscious process of selecting video games to be used in a participatory process. The following paper aims to propose a numerical base for a decisional instrument that could be useful for specifying the characteristics of games to be utilized in participation. They performed a multicriteria analysis of documented cases of implementing video games in civic engagement, allowing the creation of a set of numeric indicators that help determine the properties of games that will be most appropriate for given process assumptions. Such a tool can prevent overproducing data on the one hand and may cause dissemination of the presented way of handling the participation process on the other. Full article

The choice of management strategy for companies operating in different sectors of the economy is of great importance for their sustainable development. In many cases, companies are in competition within the scope of the same activities, meaning that the profit of one company is at the expense of the other. The choice of strategies for each of the firms in this case can be optimized using game theory for a non-cooperative game case where the two players have antagonistic interests. The aim of this research is to develop a methodology which, in non-cooperative games, accounts for the benefits of different criteria for each of the strategies of the two participants. In this research a new integrated sequential interactive model for urban systems (SIMUS)–game theory technique for decision making in the case of non-cooperative games is proposed. The methodology includes three steps. The first step consists of a determination of the strategies of both players and the selection of criteria for their assessment. In the second step the SIMUS method for multi-criteria analysis is applied to identify the benefits of the strategies for both players according to the criteria. The model formation in game theory is drawn up in the third step. The payoff matrix of the game is formed based on the benefits obtained from the SIMUS method. The strategies of both players are solved by dual linear programming. Finally, to verify the results of the new approach we apply four criteria to make a decision—Laplace’s criterion, the minimax and maximin criteria, Savage’s criterion and Hurwitz’s criterion. The new integrated SIMUS–game theory approach is applied to a real example in the transport sector. The Bulgarian transport network is investigated regarding route and transport type selection for a carriage of containers between a starting point, Sofia, and a destination, Varna, in the case of competition between railway and road operators. Two strategies for a railway operator and three strategies for a road operator are examined. The benefits of the strategies for both operators are determined using the SIMUS method, based on seven criteria representing environmental, technological, infrastructural, economic, security and safety factors. The optimal strategies for both operators are determined using the game model and dual linear programming. It is discovered that the railway operator will apply their first strategy and that the road operator will also apply their first strategy. Both players will obtain a profit if they implement their optimal strategies. The new integrated SIMUS–game theory approach can be used in different areas of research, when the strategies for both players in non-cooperatives games need to be established. Full article

The purpose of this study is to develop a comprehensive algorithm for optimizing the interaction of economic entities with counterparties, taking into account the uncertainty of market conditions and the variety of behavioral strategies of participants. The developed algorithm aims to increase the stability and efficiency of the interactions between the economic entity under study and its counterparties, minimizing risks and optimizing cooperative and competitive strategies within the framework of existing market relations. The methodology uses game theory to devise interaction strategies using mutual influence indices, non-cooperative game principles, and payment matrices. The model analyzes various interaction scenarios with counterparties by using payment matrices and considering both competitive and cooperative conditions. The research methodology is supplemented by the calculation of integral estimates based on a set of financial and economic indicators, enabling the assessment of the impact of various interaction strategies on the overall efficiency of an economic entity. After testing the developed models, a set of data was obtained, which can be used to optimize strategic planning and manage the interaction of economic entities with counterparties. The developed algorithm is an effective tool for improving the operational analysis of enterprises, primarily in industrial sectors. Full article

Cooperative alternatives need complex multi-criteria decision-making (MCDM) consideration, especially in resource allocation, where the alternatives exhibit interdependent relationships. Traditional MCDM methods like the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) often overlook the synergistic potential of cooperative alternatives. This study introduces a novel method integrating AHP/ANP with Shapley values, specifically designed to address this gap by evaluating alternatives on individual merits and their contributions within coalitions. Our methodology begins with defining problem structures and applying AHP/ANP to determine the criteria weights and alternatives’ scores. Subsequently, we compute Shapley values based on coalition values, synthesizing these findings to inform resource allocation decisions more equitably. A numerical example of budget allocation illustrates the method’s efficacy, revealing significant insights into resource distribution when cooperative dynamics are considered. Our results demonstrate the proposed method’s superiority in capturing the nuanced interplay between criteria and alternatives, leading to more informed urban planning decisions. This approach marks a significant advancement in MCDM, offering a comprehensive framework that incorporates both the analytical rigor of AHP/ANP and the equitable considerations of cooperative game theory through Shapley values. Full article

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. Full article

The methodology basic principles of the neuro-evolutionary synthesis of multi-object multi-criteria systems control models under conflict and uncertainty in real time are discussed. The proposed methodology includes the following main stages: a hierarchical optimization game model under conflict and uncertainty development; a library development of hierarchical coevolutionary algorithms for multi-criteria optimization under conflict and uncertainty; software implementation of hierarchical coevolutionary algorithms library based on distributed computing technology; and game algorithms of control under uncertainty synthesis based on the technology of neural networks ensembles. Full article