Mixed Decision Research Articles

Evaluation of silk industrial heritage value based on IAHP, D-S theory and mixed decision model: the case of Hangzhou-Jiaxing-Huzhou in China

ABSTRACT A comprehensive understanding of the diverse value of industrial heritage is a prerequisite for preservation and reuse. After studying the composition and evaluation methods of the value of world cultural heritage, this paper puts forward a mixed decision model based on the combination of interval analytic hierarchy process, expert clustering ranking weighting method, fuzzy set and D-S theory. Firstly, an evaluation system that can describe the value of modern silk industry heritage in Hangzhou-Jiaxing-Huzhou area is established, which reflects comprehensiveness and particularity. Then, the interval analytic hierarchy process and expert clustering ranking method are proposed to calculate the weight of various indicators in the evaluation system, which can effectively reduce the influence of subjective experience and extreme experts on group decision-making. In addition, the fuzzy set theory and D-S theory are used to calculate the protection grades of several modern silk industrial heritages, and compared with the actual protection grades recognized by the state, which verifies the scientificity and effectiveness of the mixed decision-making model. Finally, the results show that the mixed decision model can accurately calculate the protection level consistent with the reality, demonstrating high feasibility and providing new ideas for the future evaluation of industrial heritage values.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world.

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic Control

Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic Control

Risk-averse energy management of hydro/thermal/pumped storage complementarily operating with wind/solar: Balancing risk, cost and carbon emission

Large-scale grid integration of variable renewable energy is crucial for achieving decarbonized development. However, this integration requires frequent regulation of flexible power sources for complementary operation, which can lead to wear-and-tear and fatigue damage to key components. This poses potential risks to flexible power sources. Existing studies have primarily focused on limiting unit startups, while have neglected the risk of frequent power regulation. Thus, this work proposes a risk-averse short-term scheduling method for a Wind-Solar-Cascade hydro-Thermal-Pumped storage hybrid energy system to balance frequent regulation risk, cost, and carbon emission: (1) a risk-averse short-term scheduling model is proposed, considering multilayer constraints; (2) a multi-objective hybrid African vulture optimization algorithm is proposed to effectively solve the scheduling problem including continuous and discrete variables. A case study in the Songhua River basin, China shows that: (1) compared with traditional models, the proposed model reduces the risk by 31.4% and enhances the comprehensive performance in balancing the three objectives by 22.4%; (2) the proposed algorithm performs robustness and search capability advantages, with improvements of 33.01% and 21.44% respectively, in solving the problem of challenging constraints and mixed decision variables. Overall, this work contributes to enhancing the management of large hybrid energy systems.

A Hybrid Communication Scheme for Throughput Maximization in Backscatter-Aided Energy Harvesting Cognitive Radio Networks

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided energy harvesting cognitive radio networks (EH-CRNs) in a multi-channel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient backscatter communication (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Inclusion Bayes factors for mixed hierarchical diffusion decision models.

Cognitive models provide a substantively meaningful quantitative description of latent cognitive processes. The quantitative formulation of these models supports cumulative theory building and enables strong empirical tests. However, the nonlinearity of these models and pervasive correlations among model parameters pose special challenges when applying cognitive models to data. Firstly, estimating cognitive models typically requires large hierarchical data sets that need to be accommodated by an appropriate statistical structure within the model. Secondly, statistical inference needs to appropriately account for model uncertainty to avoid overconfidence and biased parameter estimates. In the present work, we show how these challenges can be addressed through a combination of Bayesian hierarchical modeling and Bayesian model averaging. To illustrate these techniques, we apply the popular diffusion decision model to data from a collaborative selective influence study. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on mixed decision implications based on formal concept analysis

Decision implication is an important form of knowledge representation and acquisition in Formal Concept Analysis. Decision implication reduces the redundancy of knowledge extracted from data. However, decision implication cannot extract negative information from data, so there is information loss in decision implication. This paper introduces negative attributes to decision implication and proposes mixed decision implication, enabling decision implication to extract negative knowledge from data and to represent richer decision knowledge. This paper studies the logical systems of mixed decision implications. The semantical system of mixed decision implications is constructed to represent and deduce sound mixed decision implications and avoid contradictory mixed decision implications. In the syntactical system, Mixed Augmentation and Mixed Combination are introduced and the soundness, completeness and non-redundancy of these two inference rules are proved.

Multi-AGV Dynamic Scheduling in an Automated Container Terminal: A Deep Reinforcement Learning Approach

With the rapid development of global trade, ports and terminals are playing an increasingly important role, and automatic guided vehicles (AGVs) have been used as the main carriers performing the loading/unloading operations in automated container terminals. In this paper, we investigate a multi-AGV dynamic scheduling problem to improve the terminal operational efficiency, considering the sophisticated complexity and uncertainty involved in the port terminal operation. We propose to model the dynamic scheduling of AGVs as a Markov decision process (MDP) with mixed decision rules. Then, we develop a novel adaptive learning algorithm based on a deep Q-network (DQN) to generate the optimal policy. The proposed algorithm is trained based on data obtained from interactions with a simulation environment that reflects the real-world operation of an automated in Shanghai, China. The simulation studies show that, compared with conventional scheduling methods using a heuristic algorithm, i.e., genetic algorithm (GA) and rule-based scheduling, terminal the proposed approach performs better in terms of effectiveness and efficiency.

Unified Multirate Control: From Low-Level Actuation to High-Level Planning

In this article, we present a hierarchical multirate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expressed using syntactically co-safe linear temporal logic (LTL) specifications and the nonlinear system is subject to state and input constraints. At the highest level of abstraction, we model the system–environment interaction using a discrete mixed observable Markov decision process, where the environment states are partially observed. The high-level control policy is used to update the constraint sets and cost function of a model predictive controller (MPC) which plans a reference trajectory. Afterward, the MPC planned trajectory is fed to a low-level high-frequency tracking controller, which leverages control barrier functions to guarantee bounded tracking errors. Our strategy is based on model abstractions of increasing complexity and layers running at different frequencies. We show that the proposed hierarchical multirate control architecture maximizes the probability of satisfying the high-level specifications, while guaranteeing state and input constraint satisfaction. Finally, we tested the proposed strategy in simulations and experiments on examples inspired by the Mars exploration mission, where only partial environment observations are available.

Rule reductions of decision formal context based on mixed information

Formal concept analysis is an effective tool in knowledge discovery. Nonetheless, it ignores the negative information, especially in rule extraction. And some applications require to mix positive and negative information for management and representation explicitly. This paper discusses an emerging method for rule reduction in formal contexts with mixed information by presenting a mixed decision rule based on a mixed concept lattice. Based on the mixed concept model, we discuss the relationships between mixed concept, formal concept and three-way concept, respectively. In this paper, to fully consider the mixed information, we construct a mixed decision rule. Moreover, to reduce the redundance of rule, a novel approach for weak-basis from the viewpoint of granular computing is designed to selection the core mixed decision rules and necessary mixed decision rules. Finally, the comparison of mixed decision rules and three-way decision rules is perfectly discussed, and case study is presented for the difference.

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 C2C e-commerce taxation based on mixed decision game

The rapid rise of C2C e-commerce in China has brought serious difficulties to China's tax collection and management. Based on the principle of progressive taxation, the necessity of Taxation on C2C e-commerce is analyzed and discussed. It is shown that the absence of Taxation in C2C e-commerce is contrary to the principle of tax equity and tax neutrality, and the tax revenue is lost.It is demonstrated that it is imperative that China to implement C2C e-commerce taxation in a comprehensive and standardized way. By constructing a hybrid decision game model between C2C business operators and tax authorities, the static game analysis of incomplete information is conducted for the balance of interests between tax authorities and e-commerce operators.

Selective maintenance and inspection optimization for partially observable systems: An interactively sequential decision framework

Selective maintenance is an important condition-based maintenance strategy for multi-component systems, where optimal maintenance actions are identified to maximize the success likelihood of subsequent missions. Most of the existing works on selective maintenance assumed that after each mission, the components’ states can be precisely known without additional efforts. In engineering scenarios, the states of the components in a system need to be revealed by inspections that are usually inaccurate. Inspection activities also consume the limited resources shared with maintenance activities. We, thus, put forth a novel decision framework for selective maintenance of partially observable systems with which maintenance and inspection activities will be scheduled in a holistic and interactively sequential manner. As the components’ states are partially observable and the remaining resources are fully observable, we formulate a finite-horizon Mixed Observability Markov Decision Process (MOMDP) model to support the optimization. In the MOMDP model, both maintenance and inspection actions can be interactively and sequentially planned based on the distributions of components’ states and the remaining resources. To improve the solution efficiency of the MOMDP model, we customize a Deep Value Network (DVN) algorithm in which the maximum mission success probability is approximated. A five-component system and a real-world multi-state coal transportation system are used to demonstrate the effectiveness of the proposed method. It is shown that the probability of the system successfully completing the next mission can be significantly increased by taking inspections into account. The results also demonstrate the computational efficiency of the customized DVN algorithm.

Optimal sensing energy and transmission interval in hybrid overlay–underlay cognitive radio networks with energy harvesting

In this work, we present a new concept called “transmission interval” in a hybrid overlay/underlay cognitive radio network. A transmission interval consists of a sequence of time slots during which the secondary user (SU) transmits its data using the optimal mode based on its current state. After the transmission interval ends, the SU has to choose between staying idle for a single time slot to save energy for future possible transmission, transmitting using the underlay mode without sensing to optimize the usage of the limited amount of available energy, or sensing the channel and transmitting using either overlay or underlay mode depending on the primary user (PU) state. The energy harvesting technology is also considered in the presence of multiple PUs and multiple SUs. For the SU network, a sequential decision problem is formulated using the mixed observable Markov decision process to determine the optimal sensing energy and the optimal transmission interval length that maximize the SU network throughput and minimize both the consumed energy and the interference to the PUs. Numerical results show that applying the transmission interval concept increases the SU network throughput and decreases the interference to the PUs compared to conventional models. Moreover, adding the action of underlay transmission without sensing increases the SU network throughput.

Designing an incentive scheme for producer responsibility organization of waste tires: A MCGP cooperative game approach

Extended producer responsibility (EPR) of waste tires is usually implemented by a producer responsibility organizations (PRO) composed of a group of tire producers. As a collective EPR system, the main goals of PRO are to achieve recycling cost efficiency as well as offer incentives for all member producers to design their products for recyclability (or called eco-design). Nevertheless, current PROs for tire recycling are far from their goals and do not provide sufficient incentives because they ignore the differentiated recyclability of products from different producers and charge producers a uniform unit fee. To resolve these problems, we first create a mixed recovery decision model for tire recycling of PROs based on multi-choice goal programming (MCGP) approach towards system optimality and obtain the optimal total recovery cost. In particular, the proposed MCGP model allows for integrating a variety of different regulations under a carbon-policy regime into a single programming model through the formulated utility functions and penalty functions. This makes it easy to adjust the regulatory intensity to compromise the conflicting objectives regarding cost efficiency and carbon emission reduction. Further, recognizing the optimal total recovery cost as a baseline, a cost-sharing scheme is proposed to maintain the voluntary participation of producers for PRO as well as offer eco-design incentives for producers. This study proves that the solutions of the proposed hybrid method is more suitable for the research problem than the eminent solutions such as the Shapley value. The feasibility of the proposed hybrid model is examined via a numerical study with calibrated real-world datasets.

Covering rough set-based incremental feature selection for mixed decision system

Covering rough sets conceptualize different types of features with their respective generated coverings. By integrating these coverings into a single covering, covering rough set-based feature selection finds valuable features from a mixed decision system with symbolic, real-valued, missing-valued and set-valued features. Existing approaches to covering rough set-based feature selection, however, are intractable to handle large mixed data. Therefore, an efficient strategy of incremental feature selection is proposed by presenting a mixed data set in sample subsets one after another. Once a new sample subset comes in, the relative discernible relation of each feature is updated to disclose incremental feature selection scheme that decides the strategies of increasing informative features and removing redundant features. The incremental scheme is applied to establish two incremental feature selection algorithms from large or dynamic mixed data sets. The first algorithm updates the feature subset upon the sequent arrival of sample subsets and returns the reduct when no further sample subsets are obtained. The second one merely updates the relative discernible relations and finds the reduct when no subsets are obtained. Extensive experiments demonstrate that the two proposed incremental algorithms, especially the second one speeds up covering rough set-based feature selection without sacrificing too much classification performance.

Comparative Research on the Game Behavior of the Participants in the Traditional Supply Chain Finance and the Supply Chain Finance on the Blockchain

The authors took the financing warehouse in supply chain finance as an example, used the game between capital providers (banks and their entrusted logistics supervision enterprises) and capital demanders (core enterprises of supply chain, upstream suppliers, and downstream dealers) as the research object, and constructed the income matrices, respectively, and the Nash equilibrium of pure decision and mixed decision was calculated. The authors compared the game behavior of the participants in the traditional supply chain finance and the supply chain finance on the blockchain and the difference of the mixed decision Nash equilibrium whether blockchain rewards and punishment were added. When the rewards and punishment were added to encourage the transaction information placing in the blockchain, the Nash equilibrium point would be further away from the origin point, and the capital provider and the capital demander would choose to cooperate with greater probability. When the cost of the blockchain is gradually reduced, the two sides of the game choose to place the transaction information in the chain, which can improve the cooperation of the participants of the supply chain finance, and they can get more profit.

A hybrid monotone decision tree model for interval-valued attributes

The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas others, called non-monotonic attributes. In practice, monotonic and non-monotonic attributes coexist in most classification tasks, and some attribute values are even evaluated as interval numbers. In this paper, we proposed a fuzzy rank-inconsistent rate based on probability degree to judge the monotonicity of interval numbers. Furthermore, we devised a hybrid model composed of monotonic and non-monotonic attributes to construct a mixed monotone decision tree for interval-valued data. Experiments on artificial and real-world data sets show that the proposed hybrid model is effective.

Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making -text mining-based investigation

Since its inception, the third party logistics (3PL) industry has remained an area of interest for academicians and practitioners. The existing literature mostly focuses on single multi criteria decision making (MCDM) method-based holistic performance evaluations of 3PL service providers, whereas distinct operational and financial performance measurements have not received enough attention. Several real-life examples of organizations, such as Hub Group and DSV, indicate that the reliance on financial performance improvement solely does not ensure better operational performance and integrated performance, and vice versa. Additionally, there is an absence of works that focus on designing an integrated MCDM methodology that applies multiple MCDM methods to increase the robustness of the methodology and consider distinct operational, financial, and integrated performance measurements of the 3PL service providers. Additionally, the application of emerging ratio analysis-based MCDM methods such as multi objective optimization based on ratio analysis (MOORA) and complex proportional assessment (COPRA) for performance evaluation has been ignored. Furthermore, the assessment of the service quality of 3PL service providers through their customers’ feedback and the association of this service quality with the abovementioned performance measures have not received enough attention. This motivates us to design a criteria importance through intercriteria correlation (CRITIC) weighting-based integrated MOORA-COPRA MCDM methodology for the performance evaluation of 3PL service providers. We apply our proposed methodology to evaluate the performance of 21 leading 3PL service providers in North America. Additionally, we incorporate text mining methods such as sentiment analysis and topic modeling to analyze the effect of these service providers’ service quality captured through their customers’ reviews on distinct operational, financial, and integrated performance. The insights obtained from the study indicate that service quality (as captured from the consumer reviews) has a positive association with the operational and financial performance of 3PL service providers.