Optimal Decision Research Articles

Analysis for sparse channel representation based on dictionary learning in massive MIMO systems

AbstractThe accuracy analysis of dictionary sparse representation for channels in massive MIMO systems is a relatively unexplored field. Existing research has primarily focused on investigating the accuracy of dictionary sparse representation using simulation in massive MIMO systems, but has not provided quantitative accuracy analysis. To address this gap, the correlation numerical proportional factor is proposed to represent the accuracy performance of non‐zero elements in the coefficient matrix. Additionally, a qualitative analytical formula for dictionary sparse representation accuracy is provided and an optimal upper bound for the correlation numerical proportional factor is established. Furthermore, the innovation indicates that the accuracy of dictionary sparse representation is mainly influenced by the cross‐correlation between the pilots matrix and the dictionary matrix, as well as sparsity. The author has also developed a method for minimizing the correlation numerical proportional factor. In order to obtain an optimal sparse representation coefficient matrix, a cross‐correlation matrix is constructed and an analytical expression is derived for it as well as its use as an optimal hard decision threshold is determined. Finally, a sparse representation coefficient optimization algorithm is proposed using this optimal threshold. Simulation results demonstrate that this algorithm can significantly improve channel sparse dictionary representation accuracy.

Optimizing Energy Efficiency in Smart Home Automation through Reinforcement Learning and Iot

This thesis explores an innovative approach to optimizing energy efficiency in smart home environments by leveraging reinforcement learning (RL) and Internet of Things (IoT) technologies. As global energy demand rises and concerns over environmental sustainability intensify, smart homes offer a promising solution to reduce residential energy consumption while enhancing user comfort. The study presents a comprehensive architecture integrating IoT devices with RL algorithms, allowing for real-time monitoring and intelligent energy management. Through data collected from smart sensors, RL agents continuously learn and adapt to occupant behaviors and environmental changes, making optimal decisions to minimize energy usage without compromising user comfort. A real word-based analysis demonstrates that the proposed system achieves significant energy savings compared to traditional rule-based methods. The results underscore the effectiveness of combining RL and IoT for adaptive energy management, paving the way for scalable solutions that could extend to smart cities and renewable energy systems. This research provides valuable insights into how emerging technologies can contribute to sustainable energy practices in the residential sector.

HPB SO38 - Assessing current pancreatic cancer reporting practice against the PACT-UK Synoptic Reporting Template: An initial clinical audit

Abstract Background In the last decade, the incidence of pancreatic cancer has increased by 9% in the UK. The gold standard for initial assessment of pancreatic cancer (or even peri-ampullary cancer) resectability is pancreatic-protocol CT scan. The PACT-UK project introduced a synoptic template for pancreatic cancer to standardise CT reporting practice across UK and enable consistent reporting of pertinent aspects of resectability status such as vascular involvement of the tumour. The aim of this audit is to assess the quality of current CT reports received by a tertiary hepato-pancreato-biliary (HPB) unit against the standards set out by PACT-UK. Method A retrospective analysis of 66 patients who underwent pancreatic resection between September 2023 and May 2024 at a tertiary HPB centre was undertaken. Patients were excluded from the analysis if they underwent resection for benign pancreatic pathology, were diagnosed with pathology other than peri-ampullary cancer or did not have a formal report of their CT scan. Initial CT scans performed at presentation and if available, subsequent up-to-date staging scan prior to surgery were assessed against the PACT-UK reporting template, in accordance with their guidance. The audit was registered with our clinical audit team (no. 11643). Results In total, 42 patients with peri-ampullary cancers were included in this audit. All patients had a reported CT scan on presentation. Additionally, 24 patients had a reported staging CT scan. The compliance (i.e. more than 90% information available) with PACT-UK reporting standards was 0% for initial presentation scans and 4.2% (n=1) for staging scans. The only staging scan with >90% compliance was reported using the PACT-UK template. Majority (more than 80%) of CT scan reports lacked information on aspects such as pancreatic duct size, predicted tumour type and radiological stage, variant vascular anomalies, and arterial/venous involvement. Conclusion This audit highlights the substandard quality of current CT reporting for peri-ampullary cancer. There is an urgent need to adopt a structured and dedicated reporting template to address the issue. Utilising a template such as the one developed by PACT-UK will help ensure that relevant information is formally reported to facilitate optimal decision-making at MDT meetings, and assist surgeons in operative planning and oncologists in assessing treatment response.

Evolutionary origins of temporal discounting: Modeling how time and uncertainty constrain optimal decision-making strategies across taxa

The propensity of humans and non-human animals to discount future returns for short-term benefits is well established. This contrasts with the ability of organisms to unfold complex developmental sequences over months or years efficiently. Research has focused on various descriptive and predictive parameters of ‘temporal discounting’ in behavior, and researchers have proposed models to explain temporal preference in terms of fitness-maximizing outcomes. Still, the underlying ultimate cause of this phenomenon has not been deeply explored across taxa. Here, we propose an ultimate (i.e., evolutionary) causal explanation for the selection of temporal discounting largely conserved across taxa. We propose that preference for a short-term reward (e.g., heightened impulsivity) often is less than optimal and likely is the product of constraints imposed on natural selection with respect to predicting events in a temporal framework in the context of future uncertainty. Using a simple Newtonian model for time across a fitness landscape in which movement by organisms is only possible in one direction, we examine several factors that influence the ability of an organism to choose a distant reward over a more temporally proximate reward: including the temporal distance of the far reward, the relative value of the distant reward, and the effect of uncertainty about the value and presence of the distant reward. Our results indicate that an organism may choose a more distant reward, but only if it is not too far into the future and has a substantially higher-value fitness payoff relative to the short-term reward. Notably, any uncertainty about the distant reward made it extremely unlikely for an organism to choose the delayed reward strategy compared to choosing a closer reward, even if the distant reward had a much higher payoff because events that are uncertain are only partially visible to natural selection pressures. The results help explain why natural selection is constrained to promote more optimal behavioral strategies and why it has difficulty selecting a distant reward over a lower-value short-term reward. The degree of uncertainty is an especially salient ecological variable in promoting and preferencing short-term behavioral strategies across taxa. These results further help illustrate why, from an ultimate causal perspective, human and non-human taxa have difficulty making more optimal long-term decisions.

A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension funds. Traditional methods often face challenges in computational efficiency and applicability to different market conditions. This study aimed to innovatively transplant an algorithm from reinforcement learning that enhances the efficiency and accuracy of life cycle asset allocation. We synergized tabular methods with Monte Carlo simulations to solve the pension problem. This algorithm was designed to correspond states in reinforcement learning to key variables in the pension model: wealth, labor income, consumption level, and proportion of risky assets. Additionally, we used cleaned and modeled survey data from Chinese consumers to validate the model’s optimal decision-making in the Chinese market. Furthermore, we optimized the algorithm using parallel computing to significantly reduce computation time. The proposed algorithm demonstrated superior efficiency compared to the traditional value iteration method. Serial execution of our algorithm took 29.88 min, while parallel execution reduced this to 1.42 min, compared to the 41.15 min required by the value iteration method. These innovations suggest significant potential for improving pension fund management strategies, particularly in the context of the Chinese market.

Real Options Analysis of Constructed Wetlands as Nature-Based Solutions to Wastewater Treatment Under Multiple Uncertainties: A Case Study in the Philippines

Constructed wetlands (CWs) are man-made ecosystems that mimic the properties of natural wetlands. They are being utilized to treat various types of wastewater, from domestic to agricultural, municipal, commercial, and industrial effluents. Despite their economic viability and environmental benefits, their widespread adoption is challenged with several uncertainties, including public support, technology learning, and the impacts of climate change. This study proposes a valuation framework that considers these uncertainties to analyze the feasibility of CWs. Using existing CWs in the Philippines as a case, this study employs the real options approach to (1) evaluate the feasibility of CW projects using cost–benefit analysis, (2) calculate the value of postponing decisions to implement CWs projects using real options analysis, and (3) identify the optimal investment decisions for CWs considering the opportunity costs of waiting and uncertainties in public support and the impacts of climate change. Results found that the project is feasible with a net present value of USD 88,968. Yet, the real options value at USD 208,865 indicates that postponing the project may be a more optimal decision. Considering the cost of waiting, the valuation identified the threshold at 5.56% to immediately implement the project. The calculated values increase with uncertainty in public support but decrease with uncertainty in climate change’s impacts. Yet, these uncertainties prolong the decision to implement CW projects until they are resolved. The findings from this case study provide a basis for recommendations to support the adoption of CWs as nature-based water treatment for a more sustainable future.

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Optimal Inspection and Maintenance Policy: Integrating a Continuous-Time Markov Chain into a Homing Problem

The state of a machine is modeled as a controlled continuous-time Markov chain. Moreover, the machine is being serviced at random times. The aim is to maximize the time until the machine must be repaired, while taking the maintenance costs into account. The dynamic programming equation satisfied by the value function is derived, enabling optimal decision-making regarding inspection rates, and special problems are solved explicitly. This approach minimizes direct maintenance costs along with potential failure expenses, establishing a robust methodology for determining inspection frequencies in reliability-centered maintenance. The results contribute to the advancement of maintenance strategies and provide explicit solutions for particular cases, offering ideas for application in reliability engineering.

Investigating the role of thrombosis and false lumen orbital orientation in the hemodynamics of Type B aortic dissection

While much about the fundamental mechanisms behind the initiation and progression of Type B aortic dissection (TBAD) is still unknown, predictive models based on patient-specific fluid-structure interaction (FSI) simulations can help in risk stratification and optimal clinical decision-making. Aiming at the development of personalized treatment, FSI models can be leveraged to investigate the interplay between complex aortic flow patterns and anatomical features, while considering the deformation of the arterial wall and the dissection flap. In this study, the hemodynamics of false lumen thrombosis, a large fenestration, and the orbital orientation of the false lumen is studied through image-based FSI simulations on three TBAD patient-specific geometries. A new pipeline is developed leveraging the open-source software SimVascular and ParaView to analyze multiple patients simultaneously and to achieve large-scale parallelization in FSI results based on patients’ computed tomography (CT) images. The results of this study suggest that the internal orbital orientation of the false lumen contributes to maintaining a positive luminal pressure difference ΔPTL-FL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta P_{TL-FL}$$\\end{document} = PTL-PFL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_{TL}-P_{FL}$$\\end{document} between the true lumen (TL) and the false lumen (FL), despite an impingement area in the false lumen near the entry tear. A positive and high luminal pressure difference is thought to promote TL expansion and FL compression. Moreover, it was also found that FL thrombosis at the entry tear region reduce the magnitude of the negative luminal pressure difference, which in turn may reduce FL expansion and the risk of unstable aortic growth. Finally, this FSI study suggests that the aortic wall and dissection flap stiffness determines the effects of a large fenestration in the descending thoracic aorta on the luminal pressure difference.

Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

Abstract In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, porosity and strength of apatite-biopolymer composites. The scientific direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization and training is proposed. The new knowledge obtained as a result of the project will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, cranio-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of apatite-biopolymer composites. Software development of an intelligent decision support system will be used to design bone substitutes with controlled composition, structure, porosity and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopedics and traumatology.

Differential Game Analysis of Carbon Emission Reduction Effect and Price Strategy Considering Government Intervention and Manufacturer Competition

ABSTRACTGovernments and enterprises are paying more and more attention to carbon emissions. Considering the dynamic of carbon emissions reduction (CER) and the government intervention, this study discusses the optimal CER effort level, price, and government intervention intensity in a two‐echelon supply chain consisting of a government and two manufacturers. The two manufacturers have two competitive behaviors: Cournot and Stackelberg. Two differential game models are constructed for the two different behaviors, and the optimal decisions under the two models are obtained. The comparisons of these optimal solutions are analyzed, and the influence of some parameters on the optimal solution in the two models is investigated under two scenarios. Furthermore, the optimal government intervention intensity is obtained with the goal of maximizing government utility. The results show that the Stackelberg game allows manufacturers to achieve higher profits and CER but is disadvantageous to consumers, and the manufacturer as the leader has a first‐mover advantage. Fierce market competition leads to greater CER and profits, but higher prices reduce consumer surplus. Larger penalties can promote enterprises to reduce carbon emissions when carbon emissions are large. Compared with the Cournot behavior game, the Stackelberg allows manufacturers to obtain higher profits and CER, but the prices are higher that are detrimental for consumers. The fierce market competition is good for manufacturers, the environment, and the society, but it reduces the consumer surplus. The low CER efficiency causes high costs and reduces the manufacturer's motivation to CER, which harm the environment and reduce profits. The government intervention is negatively correlated with the intensity of market competition and the sensitivity of manufacturers to policies.

Research on Emission Reduction Decisions in Supply Chains Considering Vertical Spillover Effects and Low-Carbon Preferences

The growth in carbon emissions is increasingly exacerbating global warming. As the principal source of carbon emissions, companies can effectively enhance their emission reduction levels through the vertical spillover of emission reduction technologies to investigate the impact of vertical spillover rates, consumers’ low-carbon preference coefficients, emission reduction cost coefficients on optimal supply chain decisions, and profits in centralized and decentralized decision-making environments. Considering consumers’ preferences for low-carbon options, this paper constructs a Stackelberg game model under centralized and decentralized supply chain decision-making scenarios. It examines the effects of considering the vertical spillover effects of emission reduction versus not taking them into account. Using backward induction, this study optimizes the emission reduction levels of leading suppliers and manufacturers. The results indicate that an increase in consumers’ low-carbon preference levels and vertical spillover rate not only enhances the emission reduction levels of suppliers and manufacturers but also increases their profits. Conversely, an increase in emission reduction cost coefficients impedes emission reductions, with a more pronounced effect under vertical spillover conditions. In centralized decision-making, increases in vertical spillover rates, consumers’ low-carbon preferences, and decreases in emission reduction cost coefficients create a synergistic effect, resulting in greater increases in emission reduction levels and profits for suppliers and manufacturers compared to the sum of the effects of changes in individual coefficients. This finding provides new insights for governments in formulating relevant policies to promote corporate emission reductions.

Medical and Engineering Applications for Estimation and Prediction of a New Competing Risks Model: A Bayesian Approach

The Bayesian approach offers a flexible, interpretable and powerful framework for statistical analysis, making it a valuable tool to help in making optimal decisions under uncertainty. It incorporates prior knowledge or beliefs about the parameters, which can lead to more accurate and informative results. Also, it offers credible intervals as a measure of uncertainty, which are often more interpretable than confidence intervals. Hence, the Bayesian approach is utilized to estimate the parameters, reliability function, hazard rate function and reversed hazard rate function of a new competing risks model. A squared error loss function as a symmetric loss function and a linear exponential loss function as an asymmetric loss function are employed to derive the Bayesian estimators. Credible intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are obtained. Predicting future observations is important in many fields, from finance and weather forecasting to healthcare and engineering. Thus, two-sample prediction (as a special case of the multi-sample prediction) for future observation is considered. An adaptive Metropolis algorithm is applied to conduct a simulation study to evaluate the performance of the Bayes estimates and predictors. Moreover, two applications of medical and engineering data sets are used to test and validate the theoretical results, ensuring that they are accurate, applicable to real-world scenarios and contribute to the understanding of the world and inform decision-making.

Exploring the efficacy of Multi-Armed Bandit Algorithms in dynamic decision-making

Abstract. Originating from the scenario of gambling machines in casinos, the Multi-Armed Bandit problem aims to optimize decision-making processes under limited resources to achieve maximum returns. This article delves into the principles, classifications, and practical applications of this problem. Researchers have proposed various algorithms to address this issue, including -greedy, Upper Confidence Bound, and Thompson Sampling, which have demonstrated good performance across different scenarios. The article further elaborates on the fundamental principles of Multi-Armed Bandit algorithms, encompassing the trade-off between exploration and exploitation, and provides a detailed classification of algorithms based on probability (e.g., -greedy) and value (e.g., UCB). These algorithms not only provide a framework for addressing real-world problems such as advertisement placement and resource allocation, but also possess significant theoretical value in the fields of machine learning and reinforcement learning. By balancing exploration and exploitation, Multi-Armed Bandit algorithms offer effective tools for making optimal decisions in uncertain environments, thus driving the development of related fields.

Improve the Sustainability of Supply Chain: the Role of Carbon Abatement and Blockchain Technology

This paper constructs a sustainable supply chain consisting of a manufacturer and a retail platform. This paper explores the manufacturer’s decision of whether to develop green technology to reduce carbon emission and the platform’s option of whether to adopt blockchain technology to disclose information. According to the selling format in the supply chain (i.e., reselling or agency selling format), this study divides the research into two scenarios: Scenarios R and A. Through analyses, the paper first finds that two parameters play decisive roles in the manufacturer’s and the platform’s decisions: the consumer’s acceptance of product and the consumer’s environmental sensitivity. As the consumer’s acceptance of product increases, the supply chain members’ mode preferences change from Mode RT (Mode AT) to Mode RB (Mode AB). And the increase of the consumer’s environmental sensitivity leads the supply chain members less likely to take actions (reducing carbon emission or disclosing information). Specifically, this paper discovers that the manufacturer’s and the platform’s mode preferences are always analogous with each other. Next, this paper finds that the two decisive parameters’ (i.e., the consumer’s acceptance of product and the consumer’s environmental sensitivity) joint impact on the platform’s optimal decision is more drastic than on the manufacturer’s. Finally, the selling format in the supply chain interacts with the consumer’s acceptance of product exerting influence on the supply chain members’ optimal decisions.

Optimal decisions in a capital-constrained supply chain under credit guarantee and revenue-sharing contracts

This study proposes a novel Supply Chain Finance scheme (CG-RS), integrating credit guarantees (CG), and revenue-sharing contracts (RS). We analyze a three-party Stackelberg game between a capital-constrained retailer, supplier, and bank. We derived equilibrium strategies under both monopolistic and competitive bank markets to investigate the impact of the CG-RS scheme on the financial performance of each party. From the numerical analysis, we find that all parties can benefit from the CG-RS scheme by mitigating default risk and aligning incentives. The supplier's guarantee coefficient and the retailer's initial capital significantly influence the scheme's outcomes, while the revenue-sharing proportion plays a crucial role in balancing interests. Our findings suggest that the CG-RS scheme offers a promising solution for addressing capital constraints and enhancing supply chain performance.

Supply Chain Optimization Decisions for Environmentally Conscious Products with Preservation Investment and Carbon Tax Policy

Supply Chain Optimization Decisions for Environmentally Conscious Products with Preservation Investment and Carbon Tax Policy

Pricing and Financing Strategies in a Dual-Channel Low-Carbon Supply Chain for Bilateral Capital-Constrained Retailers

In the context of low-carbon economy, financial constraints are prevalent among both upstream and downstream enterprises, exacerbated by the trend of online retailers extending into offline physical stores. This study first proposes and evaluates three financing combination models: “bank lending + advance payment,” (in short, AF), “bank lending + delayed payment,” (in short, DF), and “bilateral bank lending.” (in short, TF). It then examines the retailer’s dual-channel supply chain pricing and financing decision-making models under unified pricing strategy and independent self-pricing strategy respectively. The evaluation results indicate that among various financing models, the independent self-pricing strategy has the potential to enhance the retailer's profitability, outperforming the unified pricing strategy. In addition, when channel preference is significant, the retailer should prioritize the independent self-pricing strategy. A comparison of the optimal decisions across the three financing combination models indicates that both the cost coefficient of carbon emission reduction and the loan interest rate exert a limiting influence on the decision-making and income of all supply chain parties. Furthermore, the DF model emerges as a financing equilibrium strategy within the supply chain, with a shift to the AF model being considered only when the manufacturer’s loan interest rate is relatively high.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

Degradation-infused energy portfolio allocation framework: Risk-averse fair storage participation

This work proposes a novel degradation-infused energy portfolio allocation (DI-EPA) framework for enabling the participation of battery energy storage systems in multi-service electricity markets. The proposed framework attempts to address the challenge of including the rainflow algorithm for cycle counting by directly developing a closed-form of marginal degradation as a function of dispatch decisions. Further, this closed-form degradation profile is embedded into an energy portfolio allocation (EPA) problem designed for making the optimal dispatch decisions for all the batteries together, in a shared economy manner. We term the entity taking these decisions as ‘facilitator’ which works as a link between storage units and market operators. The proposed EPA formulation is quipped with a conditional-value-at-risk (CVaR)-based mechanism to bring risk-averseness against uncertainty in market prices. The proposed DI-EPA problem introduces fairness by dividing the profits into various units using the idea of marginal contribution. Simulation results regarding the accuracy of the closed-form of degradation, effectiveness of CVaR in handling uncertainty within the EPA problem, and fairness in the context of degradation awareness are discussed. Numerical results indicate that the DI-EPA framework improves the net profit of the storage units by considering the effect of degradation in optimal market participation.