Optimal Decision Research Articles

Optimal Maintenance Decision Method for a Sensor Network Based on Belief Rule Base considering Attribute Correlation

Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.

Repositioning to sink: The pricing and quality decisions for product line considering the sinking market

Opening up new markets (e.g., the sinking market) is one way to boost growth momentum, especially with the decrease in the rate of technological development and the impact of the black swan event (e.g., the COVID-19 pandemic). Considering the difficulty and cost of developing new products, repositioning existing products by a price markdown is an easier way to expand the market. Companies can implement different pricing strategies for different products to target different markets and make extra profits, but products entering the sinking market may deeply erode the original market. Therefore, balancing the advantages and disadvantages of entering the sinking market is a significant challenge for firms selling products as a product line. By building a theoretical model, we investigate the optimal product line repositioning strategy when companies decide to expand the market. First, we examine the optimal conditions for companies to enter a sinking market. We then analyze the optimal product line strategy in the sinking market. Furthermore, we investigate the interaction between repositioning strategy and quality decisions. The main results indicate that with the increase of the consumers’ willingness to pay at the sinking market and the decrease of the production cost, the seller's motivation to enter the sinking market would be greater. More importantly, entering the sinking market with higher quality can also be the optimal decision. What's more, we also find that the seller implementing low-quality repositioning strategy may prefer fiercer product erosion. Interestingly, the consideration of the quality decision would motivate the seller to enter the sinking market more aggressively even when the production cost is relatively high. Yet, this motivation would be weakened when the production cost is relatively low.

Dynamic asset allocation and consumption ratcheting with costs

This paper delves into studying the optimal decisions regarding consumption and investment for an agent operating within a finite time frame, where any reduction in consumption is intolerable. In contrast to Jeon et al. (2018), we assume that the agent incurs a proportional utility cost with each increase in consumption. To tackle this problem, we employ the duality method to define the dual problem and investigate the resulting upper obstacle problem using PDE techniques. Our study reveals the existence of a critical time attributed to the utility cost, beyond which not only does the agent cease to further increase their consumption, but they also abstain from investing in the risky asset. Furthermore, the existence of the critical time makes free boundary analysis even more challenging, as this is because the presence of the critical time can also lead to the disruption of the monotonicity of the free boundary.

The Economic Value of Biodiversity Preservation

We study the decision to preserve diverse species when the value of biodiversity is uncertain, or even affected by ambiguity. Optimal decisions are derived both from the perspective of the producer/investor and the policy regulator (ecosystem planner). We find that while calculated risk creates a scope for biodiversity preservation, the presence of ambiguity aversion reduces it, thus accelerating the extinction of species with lower value. Our results suggest that effective conservation strategies would involve a reduction of ambiguity aversion by creating a stable and transparent policy environment. Furthermore, they may involve a two tier strategy, with one tier addressing output targets and the other conservation targets.

Diversified Filtering Mechanism for Evaluation Indicators of Urban Road Renewal Schemes

As global urban infrastructure renewal progresses, the urgent need for sustainability-driven urban renewal intensifies amidst resource scarcity and environmental concerns. Effective evaluation and decision making regarding urban road renewal schemes are prerequisites for their successful implementation. However, variation in the prioritization of indicators in project evaluations and the poor adaptability of existing frameworks hinder the quick assessment of diverse projects. To address this issue, this paper proposes a scheme evaluation framework with embedded renewal project features comprising four modules. Following the initial construction of a sustainability-driven evaluation system, an indicator-filtering mechanism combining the Latent Dirichlet Allocation (LDA) model with a text similarity algorithm is developed. The Entropy Weight-–TOPSIS method is then employed to derive the final optimal decision based on selected indicators. Applying the decision framework to the G15 Jialiu Widening and Reconstruction Project in Shanghai, China, indicators are reduced by 48.3%, with the optimal scheme decision consistent with the traditional Entropy Weight–TOPSIS method. The framework is robust and enhances decision efficiency, filling theoretical gaps in existing indicator-filtering mechanisms.

Orthopedic surgeons' transition into full-time practice over the last 20 years: an analysis using Ministry of Health billing data.

Underemployment is a reality for many new graduates, who accept locum or part-time work as an alternative to unemployment because of lack of opportunities. We sought to analyze orthopedic surgeons' Ontario Health Insurance Program (OHIP) billing data over a 20-year period as a proxy of practice patterns and hypothesized that billing in the first 6 years of practice would be affected by underemployment and locum. We analyzed the annual average billing totals of orthopedic surgeons, broken down by year of graduation, year of billings, and number of surgeons billing in that year. We analyzed public census data of the Ontario population size as a proxy of orthopedic demand. A 2019 cross-sectional analysis showed that around 15 surgeons per graduating year were billing in Ontario from the 1995 to 2016 cohorts, while 2017 and 2018 saw an increase to 30 and 36 actively billing surgeons, respectively. The number returned to more historical numbers in 2019, with 20 actively billing surgeons. For those surgeons billing in Ontario, billing trends have been roughly stable, with average billings increasing each year for the first 6 years in practice (p < 0.001). Year of graduation did not have an effect on the first 6 years of billings (p > 0.5). Billings were stable after 6 years in practice (p > 0.09). The Ontario health care system has not expanded to support more orthopedic surgeons despite the aging and growing population; despite our growing population, the number of surgeons being trained and retained has not matched this growth. Further research needs to be done to guide optimal health human resource decision-making.

EFFECTIVENESS OF THE INNOVATION PORTFOLIO IN POLISH ENTERPRISES – RESEARCH RESULTS

A single innovation project has a low probability of success, while the company’s chances of gaining a competitive advantage are low. Only a properly diversified innovation portfolio gives a chance for market success. The aim of the article is to analyze the issue of the effectiveness of innovation portfolio management in Polish enterprises, due to various external and internal factors, and also to identify clues determining the success of the innovation portfolio among enterprises in the future. For this purpose, a literature review in the area of the innovation portfolio was conducted. Also the method of analysis and criticism of the literature was applied. In the research process, empirical articles and Statistics Poland reports were analyzed. As a result of the analyses, effective ways to build innovation portfolio efficiency were identified by pointing out factors that can contribute to key optimization decisions (e.g., proper planning and diversification of the innovation project portfolio). Enterprises, in order to achieve innovation portfolio efficiency in difficult market conditions, should reallocate their resources to less risky innovation projects, and at the same time accepting the risks associated with introducing a transformational innovation.

Health Information Seeking Using Tech and Non-Tech Sources Among a Diverse Sample of Caregivers in the Deep South

ABSTRACT Caregivers of people living with dementia (PLWD) are often tasked with making decisions about their loved one’s daily care and healthcare treatment, causing stress and decision-making fatigue. Many caregivers engage in health information seeking to improve their health literacy for optimal decision-making, though there is limited knowledge about the strategies used to increase their health literacy. This study involved a survey of caregivers in Alabama, most of whom were African American and/or living in rural communities that have historically underserved. The findings shed light on caregivers’ experiences in seeking out health-related information and their perceptions of various sources of information.

Task offloading method based on CNN-LSTM-attention for cloud–edge–end collaboration system

Most of the existing task offloading methods in edge computing environments do not fully utilize the processing capabilities of cloud servers and have high computational complexity, making them unsuitable for real-time processing tasks. To address this problem, we propose a cloud–edge–end collaborative task offloading method based on deep learning methods, combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and attention mechanisms. This method models the total cost of the cloud–edge–end collaboration system as a weighted sum function of the time delay and energy consumption of task execution. Then, with the objective of minimizing the total cost of the system, the task offloading problem is formulated as a mixed-integer joint optimization problem with offloading decision and bandwidth allocation, and two subproblems are decomposed from the optimization problem: one focuses on offloading decision and the other on bandwidth allocation. A CNN-LSTM-Attention neural network-based method is proposed to solve the optimal offloading decision efficiently. Based on this, the differential evolution algorithm is used to generate the optimal bandwidth allocation, resulting in efficient task offloading. The simulation experiment results demonstrate that our method enhances system performance and reduces the overall cost of the system, which is significantly better than existing methods.

Towards a defossilized building sector with field tests in the lab: Review, development, and evaluation

The transformation process in the building sector towards future city concepts requires changes in our society that aim for significant emission reduction until 2045. Currently, buildings in Germany are constructed and operated in such a way that no changes need to be made to them for decades. Therefore, any upcoming changes that do not achieve the climate goals must be avoided immediately. However, making the optimal decision for a change is complex due to (a) multi-domain (e.g., design and control), (b) multi-scale (e.g., single-family building and urban energy system), (c) multi-stakeholder (e.g., users and practice), and (d) long-term implications (up to decades). Conventional testing facilities and methods do not account for the integration of (a)-(d). Therefore, their technology readiness level is limited by 6. This paper introduces an approach that aims at (1) a multi-domain and multi-scale approach by applying process systems engineering to the building sector and that aims at (2) stakeholder integration already in the research process by applying the living lab approach. We combine the two methods, yielding a field test in the lab infrastructure pushing the technology readiness level to 7–8. In all, it consists of five test benches (Refrigerant Cycle Lab, FlexFass, RT-Lab, Raumklimalabor, and InFis). This work focuses on the Refrigerant Cycle Lab. Its concept is based on two further Hardware-in-the-Loop test benches which are extended by a safety system that ensures automated operation dedicated to heat pumps with flammable refrigerants. In two case studies, we show that field tests in the lab allow multi-domain and multi-scale testing. Both aspects support accelerating the transformation process towards a defossilized building sector. In the next step, the combination of test benches at different locations is promising to reduce the need to set up identical experiments at different locations. In the long term, the laboratory is designed to include external stakeholders to avoid time-consuming iterations and can, therefore, save resources, time, and money for our complex transformation process.

Knowledge-Based Effective Dispatch for Job Shop Scheduling

Although Deep Reinforcement Learning (DRL) is promising in solving Job Shop Scheduling Problems (JSPs), existing DRL-based methods still have large optimality gaps when learning job-to-machine solutions. This is mainly because: (i) existing state representations autonomously learned from graph-structured data cannot fully capture node information to support agents in making optimal decisions; and (ii) existing reward functions cannot accurately reflect some actions that will seriously worsen the current state. Aiming to address these issues, we propose a knowledge-based DRL method that selects nine well-known priority dispatching rules (PDRs) as state features, which can achieve effective model training. To avoid feature over-redundancy, we discard significantly correlated features based on the Pearson correlation relationship analysis, which can help to identify the key factors that affect the agents’ decision-making. Furthermore, since it is difficult to design a reward function that can accurately distinguish actions, we mask poor-performing actions based on problem-specific knowledge to prevent them from being selected at the current decision point. Comprehensive experimental results demonstrate the superiority of our approach over four PDRs and four state-of-the-art methods on various benchmarks.

To Solve Pythagorean Fuzzy Decagonal Transportation Problem Using Bipartite Graph with Optimal Solution

Objectives: This study aims to solve the Pythagorean fuzzy transportation problem by means of providing a novel approach. Methods: To determine the optimum decision-making, we applied the MODI approach. To illustrate the proposed approach, we applied several types of numerical issues. An algorithm for solving the suggested technique is described. Findings: This study presents a bipartite graph-based strategy to the Pythagorean fuzzy transportation problem. Applying TORA statistical software, the data analysis discovered that the crisp model becomes linear when the membership function is applied. Novelty: This study has paved a way for better optimal results to obtain when the Pythagorean fuzzy set is converted using the score function instead of the standard techniques for Defuzzification. Keywords: Score Function, Pythagorean Fuzzy, Defuzzification, Pythagorean Fuzzy Transportation Problem, Decagonal Fuzzy Number, Ranking Method, Initial Basic Feasible Solution, and Optimal Solution

Study on multi-timescale operation of hydrogen-containing energy coupled with transportation system

Abstract With the widespread attention on hydrogen energy, the operation optimization of the coupling system of hydrogen energy and transportation has become a critical issue. Therefore, this study develops a hydrogen-containing energy transportation coupling system for the system collaborative operation framework, operation mode and equipment modelling of the system. Furthermore, a medium- to long-term operation optimization model and a short-term operation optimization model were constructed considering the differences in the operation of coupled systems at different timescales and operating costs as the objective function and power balance and system equipment as constraints. In the medium- and long-term operation optimization model, the planning scenario reduction method is used to reduce the wind power generation scenario. In the short-term operation optimization model, a multivariate uncertainty model is constructed to represent the uncertainty in the coupling system. Subsequently, the solution method of the model is proposed. Finally, a coupled system is simulated to verify the effectiveness of the model. (i) When the initial scene set is 600, the typical scene reduction method using Latin hypercube sampling and the Wasserstein distance can reduce operating costs by 7.60% and 9.49% compared with K-means reduction and K-media reduction methods. (ii) The sensitivity coefficients of hydrogen sales price, electricity sales price and maintenance rate to operating costs are –0.031%, –1.009% and 0.0105%, respectively. (iii) Considering multiple uncertainties can help make optimal decisions based on the overall consideration of disadvantage scenarios, thereby reducing system operating costs.

Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people’s lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the two-stage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.

A novel multi-source TWD model based on multi-granularity ball for multiple decision makers

Three-way incomplete multi-attribute decision-making model (TWIMADM) for multiple decision makers is an important topic in data science, which is an effective tool for decision making. However, calculation results of neighborhood blocks are still affected by neighborhood radius. In addition, most of the existing models without considering the information from the evaluations of multiple decision makers separately is difficult to obtain a comprehensive assessment. To address these problems, this paper builds a novel multi-source TWD model based on multi-granularity ball. First, the well-established granular ball is extended to multi-granularity, by which a weighted conditional probability is generated to divide the concerned region into three parts: positive, negative and boundary region. The calculation of the multi-granularity ball is independence of the neighborhood radius parameter. Subsequently, the joint entropy is developed by using the defined multi-granularity to obtain the optimal decision. Furthermore, the relative utility function is utilized to capture the preferences of decision makers, the classification and ranking of each object are determined by calculating the threshold value and expected utility value. Evaluation information from all decision makers are combined and fused. Finally, a novel TWD model is developed to address the challenges of incomplete multi-attribute decision-making problems involving multiple decision makers. The comparative case analyses and experimental evaluations have demonstrated the stability, validity, and feasibility of our model.

TBM tunneling strata automatic identification and working conditions decision support

It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving TBM for main drivers. For this purpose, this study takes a TBM diversion tunnel project in Xinjiang as the research background, collects 8633 m length TBM on-site tunneling data, and constructs a TBM tunneling data sample database; The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm was improved using kernel density estimation to achieve TBM tunnel surrounding rock classification based on tunneling performance and automatic identification. Compared with the traditional DBSCAN model, the Sk parameter average decreased by 30.0%, and the Ku parameter average increased by 181.9%; Adopting the opposition-based learning strategy and β-chaotic sequence method improved the MOGWO (multi-objective grey wolf optimization) algorithm to achieve optimal decision-making of TBM tunneling parameters under 12 working conditions. Compared to the traditional MOGWO model, the Pareto optimality solved by the improved MOGWO model increasing the improvement efficiency of the original value and sample mean by 14.82% and 11.46%, respectively. The on-site application results indicate that the assistant decision support system proposed in this study can provide construction guidance for TBM main drivers under different working conditions, improving the tunneling efficiency and safety of TBM.

Two-stage stochastic optimization of virtual power plant with wind power and uncertain demand

ABSTRACT The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

The role of repeat kidney biopsy in patients with lupus nephritis (case report)

Lupus nephritis (LN) occurs in 35–60% of patients with systemic lupus erythematosus (SLE), often in the early stages of the disease. LN is one of the most severe manifestations of SLE and, if not treated promptly and effectively, can lead to rapid and severe loss of kidney function. Despite modern pharmacotherapy, 5–20% of patients develop end-stage renal failure within 10 years of diagnosis of LN. The main principle of LN therapy is to prevent deterioration of renal function, but a consensus on outcome assessment criteria and clinically relevant short- and long-term goals for LN therapy has not yet been reached. There is increasing evidence of the importance of repeat kidney biopsies to assess the outcomes of the initial phase of therapy and to determine the long-term prognosis of renal failure. It is believed that the information obtained from repeat biopsies can help to make optimal treatment decisions and thus increase the likelihood of achieving a complete renal response in the short term and a more favorable renal prognosis in the long term.We describe a clinical case of a young patient with highly active SLE and morphologically confirmed class IV-S LN. Repeat biopsy and clinical and laboratory examination, had confirmed a clinical and laboratory remission of the disease on the background of the therapy. This made it possible to reduce the dose of methylpednisolone and cytostatic drugs (mycophenolate mofetil) and to discontinue therapy with biologic diseasemodifying antirheumatic drug (rituximab).

Resource Allocation for LoRaWAN Network Slicing: Multi-Armed Bandit-based Approaches

Wireless sensor networks have become increasingly popular in recent years due to the growing demand for Internet of Things (IoT) applications, including LoRaWAN networks. However, the effective allocation of resources remains a crucial challenge in LoRaWAN networks due to the limited bandwidth and the diverse demands for multiple services. This paper presents three novel resource allocation solutions for LoRaWAN network slicing to address this challenge. These solutions are based on the Multi-Armed Bandit (MAB) algorithm, which is known for balancing the exploration of available actions with the exploitation of optimal decisions. Our objective is to dynamically and efficiently allocate resources to network slices by treating the resource allocation as a MAB problem. This approach aims to maximize Packet Delivery Rate (PDR) performance while ensuring each service’s Service Level Agreement (SLA). The first solution, UCB-MAB, uses the Upper Confidence Bound (UCB) strategy to balance exploration and exploitation to improve network performance. The second solution, Q-UCB-MAB, continuously updates Q-values using the Q-learning update equation and incorporates the UCB strategy for further optimization. Finally, the third solution, ARIMA-UCB-MAB, leverages the predicted reward value from the Autoregressive Integrated Moving Average (ARIMA) model within the UCB framework to enhance network performance. Our results demonstrate that all three solutions offer efficient resource allocation in terms of PDR and SLA satisfaction. Specifically, the ARIMA-UCB-MAB solution outperforms the two other solutions.