Utility Maximization Research Articles

Online Worker Scheduling for Maximizing Long-Term Utility in Crowdsourcing with Unknown Quality

Spatiotemporal Mobile CrowdSourcing (MCS) is a new intelligent sensing paradigm for large-scale data acquisition where requesters can recruit a crowd of workers to perform data collection tasks. How to recruit suitable workers in a dynamic environment to maximize platform utility is a key issue and has become a research hotspot. Many past studies have made great efforts in this regard, but most of them either assume that the worker quality is known in advance or ignore the limitations of workers’ short-term ability to provide resources. In this paper, we consider a platform-centered online spatiotemporal MCS system where mobile workers have both long-term and short-term constraints for providing resources, and their quality is unknown to the platform, while the platform has a long-term budget constraint for recruiting workers. We aim to find an online worker scheduling scheme to maximize the platform’s long-term utility without violating the constraints of both workers and the platform. To address this problem, we first transform the long-term utility maximization problem into a real-time utility maximization problem by leveraging the Lyapunov optimization, then design algorithms based on the Upper Confidence Bound (UCB) and Markov approximation to solve each real-time utility maximization problem with unknown worker quality. We demonstrate that our UCB-based algorithm has a sublinear regret and prove that our proposed framework has a performance guarantee for the addressed problem. Finally, we evaluate our design through numerical simulation experiments, and the results demonstrate the effectiveness of our algorithm.

Heuristics Identified in Health Data-Sharing Preferences of Patients With Cancer: Qualitative Focus Group Study.

Evaluating precision oncology outcomes requires access to real-world and clinical trial data. Access is based on consent, and consent is based on patients' informed preferences when deciding to share their data. Decision-making is often modeled using utility theory, but a complex decision context calls for a consideration of how heuristic, intuitive thought processes interact with rational utility maximization. Data-sharing decision-making has been studied using heuristic theory, but almost no heuristic research exists in the health data context. This study explores this evidence gap, applying a qualitative approach to probe for evidence of heuristic mechanisms behind the health data-sharing preferences of those who have experienced cancer. Exploring qualitative decision-making reveals the types of heuristics used and how they are related to the process of decision-making to better understand whether consent mechanisms should consider nonrational processes to better serve patient decision-making. This study aimed to explore how patients with cancer use heuristics when deciding whether to share their data for research. The researchers conducted a focus group study of Canadians who have experienced cancer. We recruited participants through an online advertisement, screening individuals based on their ability to increase demographic diversity in the sample. We reviewed the literature on data-sharing platforms to develop a semistructured topic guide on concerns about data sharing, incentives to share, and consent and control. Focus group facilitators led the open-ended discussions about data-sharing preferences that revealed underlying heuristics. Two qualitative analysts coded transcripts using a heuristic framework developed from a review of the literature. Transcripts were analyzed for heuristic instances which were grouped according to sociocultural categories. Using thematic analysis, the analysts generated reflexive themes through norming sessions and consultations. A total of 3 focus groups were held with 19 participants in total. The analysis identified 12 heuristics underlying intentions to share data. From the thematic analysis, we identified how the heuristics of social norms and community building were expressed through altruism; the recognition, reputation, and authority heuristics led to (dis)trust in certain institutions; the need for security prompted the illusion of control and transparency heuristics; and the availability and affect heuristics influenced attitudes around risk and benefit. These thematic relationships all had impacts on the participants' intentions to share their health data. The findings provide a novel qualitative understanding of how health data-sharing decisions and preferences may be based on heuristic processing. As patients consider the extent of risks and benefits, heuristic processes influence their assessment of anticipated outcomes, which may not result in rational, truly informed consent. This study shows how considering heuristic processing when designing current consent mechanisms opens up the opportunity for more meaningful and realistic interactions with the complex decision-making context.

Developing CUSTOM framework: explore telecommuting-induced activity-travel demands with mode choice

This paper presents an econometric framework of jointly modelling daily activity scheduling (activity type, time expenditure, and location choices known as the CUSTOM system) and travel mode choice considering Random Utility Maximization (RUM) behaviour. The joint model is applied to model workers’ daily activity-travel demand with flexible work arrangement choices. The joint framework is flexible to capture workers’ activity-travel patterns under different workplace (telecommuting, not telecommuting, or hybrid) arrangement options. The model is empirically estimated using datasets collected in the Greater Toronto Area (GTA) in 2021. The analysis explores how different workplace arrangements affect activity-travel demand. The model is calibrated to simulate scenarios where the distribution of work-from-home workers varied between the levels observed from 2016 to 2021. The scenario analysis validates the behavioural predictability of the joint CUSTOM system. This highlights the potential of the agent-based activity-based modelling system to forecast the influence of disruptive events on travel behaviours.

From Single Atom Photocatalysts to Synergistic Photocatalysts: Design Principles and Applications

AbstractPhotocatalysis represents a solar‐to‐chemical energy transformation process including three processes, light absorption, charge separation/transfer, and surface reactions. Owing to the merits of single‐atom catalysts (SACs) toward maximal atom utilization, unsaturated coordination structure, and tunable electronic configuration, single‐atom photocatalysts (SAPs) exhibit extraordinary photocatalytic performance toward a series of sustainable reactions. Accompanied by the complexity of photocatalytic processes and the realistic demand for tandem reactions as well as the promotion of intricate reactions with multiple reaction routes and intermediates, significant efforts are desired to gain in‐depth insights into the design and fabrication of synergistic photocatalysts. In this review, the first part discusses the design principles from traditional semiconductor‐based photocatalysts to SAPs. Moreover, six basic models of synergistic photocatalysts including remote dual atoms, bridged dual atoms, adjacent dual atoms, single atoms + clusters/nanoparticles (NPs), single atoms + defects, NPs + NPs, are highlighted and distinguished by their structure features. Second, specific examples of SAPs and synergistic photocatalysts are appreciated under the category of CO2 reduction reaction (CO2RR), hydrogen evolution reaction (HER), nitrogen reduction reaction (NRR), and pollutants degradation. Finally, this review will conclude by discussing the challenges and future perspectives of SAPs and synergistic photocatalysts for sustainable applications.

MONOPSONY POWER AND WORKER UTILITY IN THE 6X1 LABOR SCHEDULE

This study analyzes the 6x1 work schedule, characterized by six consecutive workdays followed by one rest day, highlighting its economic and social impacts. It explores the interaction between firm-level profit maximization and worker utility, incorporating monopsony power in wage-setting and diminishing returns to labor productivity. Using a theoretical model, the study examines how long working hours affect worker health, morale, and productivity while influencing organizational outcomes. Findings reveal significant distortions in equilibrium wages and hours due to monopsony power, emphasizing the trade-offs between economic efficiency and worker well-being. The study calls for policies addressing these inefficiencies to ensure sustainable labor practices.

Optimal control and cost-effectiveness analysis for bacterial meningitis disease

In this study, we developed an optimal control deterministic model for the dynamics of bacterial meningitis disease. The objective was to investigate the efficiency and cost-effectiveness of the three controls (prevention, treatment, and screening) in curtailing the spread of bacterial meningitis. To accomplish this, we applied Pontryagin's maximum principle to derive the optimality system. We examined different combination strategies to investigate the effect of the interventions on the spread of bacterial meningitis. We used an incremental cost-effectiveness ratio (ICER) to examine which control technique was the most successful. The simulation results show that combining prevention and screening is the most cost-effective strategy. The objective function and the incremental cost-effectiveness ratio further support this result, indicating that maximum utilization of prevention and screening is required for the entire period.

Maximization utilization of DC‐link voltage in variable flux reluctance machine with field winding driven by single‐leg converter

AbstractIn order to improve the torque/speed characteristics of the variable flux reluctance machines (VFRMs), an optimal voltage distribution method is proposed to maximize the utilization of the DC‐link voltage. According to the investigation of operating range VFRM driven by single‐leg converter, the optimal armature voltage and the zero‐sequence voltage can be obtained to generate adjustable zero‐sequence current. Thus, the DC‐link voltage utilization and output capacity of VFRM can be significantly improved by optimizing the zero‐sequence current. For verification, the test results on a prototype 6/4 VFRM with single‐leg excitation are presented. The experimental results indicate that, compared with the constant zero‐sequence current control, the DC‐link voltage utilization of the VFRM with proposed method increases by 11.13% together with 36.67% higher output torque at rated speed.

Optimal consumption under a drawdown constraint over a finite horizon

This paper studies a finite horizon utility maximization problem on excessive consumption under a drawdown constraint. Our control problem is an extension of the one considered in Angoshtari et al. (2019) to the model with a finite horizon and an extension of the one considered in Jeon and Oh (2022) to the model with zero interest rate. Contrary to Angoshtari et al. (2019), we encounter a parabolic nonlinear HJB variational inequality with a gradient constraint, in which some time-dependent free boundaries complicate the analysis significantly. Meanwhile, our methodology is built on technical PDE arguments, which differs from the martingale approach in Jeon and Oh (2022). Using the dual transform and considering the auxiliary variational inequality with gradient and function constraints, we establish the existence and uniqueness of the classical solution to the HJB variational inequality after the dimension reduction, and the associated free boundaries can be characterized in analytical form. Consequently, the piecewise optimal feedback controls and the time-dependent thresholds for the ratio of wealth and historical consumption peak can be obtained.

Spatial multi-attribute decision making: An axiomatic incomplete preference information coverage model

In addressing the spatial multi-attribute decision making with conflicting incomplete preference information, this study presents an axiomatic preference information coverage model, which can determine the group’s collective stance without distorting individual’s original spatial preferences. First, with a Preference Information (PI) set to encapsulate the incomplete spatial preference information, we extend existing additive spatial value function with a stochastic weight space to assign utilities to alternatives. Dominance relations among pairwise alternatives based on expected utility maximization inform the alternatives priority sequences. Within this context, a novel preference information measure is introduced, serving as the basis for defining the coverage degree associated with alternatives priority sequences relative to the PI. In alignment with the axiomatic foundations governing coverage degree and the principle of maximizing this metric, a dominant rule is formulated to dictate the hierarchy among alternatives priority sequences. The model’s applicability is demonstrated via a numerical example, followed by simulation analyses that probe the influence of different PIs on coverage degree.

Optimal Portfolio and Retirement Decisions with Costly Job Switching Options

In this paper, we consider the utility maximization problem of an agent regarding optimal consumption-investment, job-switching strategy, and the optimal early retirement date. The agent can switch between two jobs or job categories at any time before retirement, but incurs a cost when switching to a position offering higher labor income. The agent's utility maximization involves a combination of stochastic control for consumption and investment, switching control for job-switching, and optimal stopping for early retirement decisions, making it a non-trivial and highly challenging problem. By utilizing the dynamic programming principle, we can derive the nonlinear Hamilton-Jacobi-Bellman (HJB) equation in the form of a system of variational inequalities with obstacle constraints, which arises from the agent's optimization problem. We employ guess and verify methods based on economic intuition to derive the closed-form solution of this HJB equation and demonstrate, through a verification theorem, that this solution aligns with the solution to the agent's utility maximization problem.

A sustainable waveguide-based design strategy for improving the energy efficiency of microwave hybrid heating systems: A combined theoretical and multi-physics simulation approach

The microwave hybrid heating (MHH) based processing has emerged as energy-efficient and productive manufacturing process. The efficiency of MHH depends upon the ability of waveguide system to transmit the electromagnetic radiation with minimal loss. Thus to improve the efficiency of MHH it is of paramount importance to understand the potential of waveguide systems. In the present work, the influence of different waveguide systems, their positioning, and different modes of operation on MHH efficiency were studied via theoretical and simulation investigations followed by experimental validation. Based on the study, the optimum waveguide system was identified as WR430. To assess the efficiency and effectiveness of the waveguide positioning on MHH, 12 different locations were considered, and the optimum location was identified as (+35, −35), with a maximum microwave utilization efficiency of 44.5 %. Subsequently, the simulation model (1049 °C) was validated using experimental data (1017 °C) with an error of less than 10 %. Also, MHH is sensitive to different waveguide operating modes, SiC susceptor positioning and microwave power. A microwave cavity operating with two waveguides on both sides is found to provide optimum results in terms of heating uniformity, electric field distribution, and microwave energy absorption. The SiC susceptor positioned at the centre of the alumina refractory yields maximum heat evolution of 1020 °C. In addition, as the microwave power grows, the temperature differential also increases, implying thermal heterogeneity inside the SiC susceptor. Thus, low microwave power is identified for improved thermal uniformity and energy efficiency, while high power is proven for rapid differential microwave heating.

Fostering the Regional Circulating and Ecological Sphere approach to translate global goals into local actions: Lessons from national scoping workshops in ASEAN Countries

The Regional Circulating and Ecological Sphere (Regional-CES) is a guiding concept for environmental policies to support the achievement of the Sustainable Development Goals (SDGs) in urban and rural communities with maximum utilisation of local strengths and resources. To promote the Regional-CES concept as a useful guiding principle, a scoping project was designed to engage key stakeholders in selected Southeast Asian countries, namely, the Philippines, Thailand and Indonesia, by organising workshops in these countries. These workshops consisted of sessions to explain the Regional-CES concept, discuss and identify opportunities for applying Regional-CES based on each country’s context, and share practices that may have been conducted under similar frameworks or concepts. Common obstacles include limited scientific understanding, ignorance of local needs and reality in the top-down decision-making process, and limited capacity of local agencies and society. Participants acknowledged the potential of the Regional-CES approach to advance transformative actions towards low-carbon society, resource circulation, and living in harmony with nature at regional, national and local levels, and the importance of aligning the Regional-CES concept to specific local contexts, such as local needs, policy relevance and priorities that could address social, economic and environmental challenges.

A VIKOR-Based Sequential Three-Way Classification Ranking Method

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions.

Fractional order pole fixed second order generalized integrator based control for grid connected solar photovoltaic system

AbstractIn this article, the development of multi‐functional grid connected solar photovoltaic (PV) system using improved Fractional order Control theory‐based Pole fixed Second Order Generalized Integrator (FO‐PFSOGI) is proposed. The fractional order (FO) control technique offers an advantage to adjust the fixed structure of the integer order and provide additional degree of freedom to achieve accurate response during the system operation. The FO‐PFSOGI control technique is used to extricate fundamental constituent of the non‐sinusoidal load current. The PV system is competent of feeding the local load requisite and may also inject surplus power into the grid. The voltage source converter (VSC) utilized in the grid‐connected system can also be operated as power quality compensator, taking care of harmonics, unbalancing and excess reactive power demand of the local load. The feasibility of multifunctional operations of the converter is established in this article, thus achieving maximum utilization of the power electronics used in the system and reducing the overall cost. A performance comparison of the developed control technique is presented with the conventional techniques in terms of harmonic compensation, weight convergence and computational complexity. The implementation of the controller is modest and extracts fundamental quantities without any phase delay under different loading conditions. The developed system is validated using both simulation and experimental study. A scaled down experimental setup of grid‐connected PV system is implemented in the laboratory and real time performance of the FO‐PFSOGI is demonstrated using variations in system load and PV irradiance.

Strategies for Achieving Carbon Neutrality: Dual-Atom Catalysts in Focus.

Carbon neutrality is a fundamental strategy for achieving the sustainable development of human society. Catalyzing CO2 reduction into various high-value-added fuels serves as an effective pathway to achieve this strategic objective. Atom-dispersed catalysts have received extensive attention due to their maximum atomic utilization, high catalytic selectivity, and exceptional catalytic performance. Dual-atom catalysts (DACs), as an extension of single-atom catalysts (SACs), not only retain the advantages of SACs, but also produce many new properties. This review initiates its exploration by elucidating the mechanism of CO2 reduction reaction (CO2RR) from CO2 adsorption and CO2 activation. Then, a comprehensive summary of recently developed preparation methods of DACs is presented. Importantly, the mechanisms underlying the promoted catalytic performance of DACs in comparison to SACs are subjected to a comprehensive analysis from adjustable adsorption capacity, tunable electronic structure, strong synergistic effect, and enhanced spacing effect, elucidating their respective superiorities in CO2RR. Subsequently, the application of DACs in CO2RR is discussed in detail. Conclusively, the prospective trajectories and inherent challenges of CO2RR are expounded upon concerning the continued advancement of DACs. This thorough review not only enhances the comprehension of DACs within CO2RR but also accentuates the prospective developments in the design of sophisticated catalytic materials.

Research on Decoration of Tourist Homestays and Hotels Based on Digital Image Network Computing

In today’s digital age, the tourism and homestay industry is facing unprecedented opportunities and challenges. With the continuous expansion of the tourism market, tourists’ demands for accommodation experience are becoming increasingly diverse and personalized. Traditional design methods often struggle to quickly respond to these changes, fully showcase regional characteristics, and achieve efficient personalized customization. At the same time, how to integrate modern technology while retaining traditional charm to enhance the attractiveness and comfort of homestays has become an urgent problem to be solved. This paper aims to explore in depth how to use this technological framework to optimize the decoration design of tourist homestays, in order to create living spaces that retain traditional charm and are rich in modern technology. By utilizing digital image network computing technology, it is possible to conduct 3D modeling and virtual simulation of the indoor space of homestays. By simulating different furniture placement and spatial separation schemes, combined with tourist behavior analysis data, the optimal spatial layout scheme is automatically recommended to achieve maximum space utilization and a comfortable living experience. This technology can analyze a large number of color matching cases, combined with the cultural characteristics of the area where the homestay is located, and apply the principles of color psychology to provide personalized color schemes for homestay design. Meanwhile, through style recognition and recommendation algorithms, designers can quickly locate and integrate traditional and modern elements to create unique decorative styles.

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by 10.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10.5\\%$$\\end{document} and 11.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$11.2\\%$$\\end{document}, respectively. The performance of the average number of active sensor nodes decreased by 13.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$13.2\\%$$\\end{document} and 16.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$16.4\\%$$\\end{document} compared to existing algorithms, while the average coverage redundancy decreased by 23.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$23.2\\%$$\\end{document} and 21.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$21.6\\%$$\\end{document} relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

AESware: Developing AES-enabled low-power multicore processors leveraging open RISC-V cores with a shared lightweight AES accelerator

As open-source RISC-V cores continue to be released, the development of low-power multicore processors utilizing these cores is invigorating the edge/IoT device market. Nevertheless, comprehensive research on developing low-power multicore processors with integrated security features using existing open RISC-V cores remains limited. This study addresses this gap by introducing AESware, a dedicated lightweight hardware designed for energy-efficient AES (Advanced Encryption Standard) task execution, contributing to the development of AES-specific low-power RISC-V multicore processors. AESware supports variable key lengths and ensures minimal power consumption with a compact design. This standalone IP (Intellectual Property) is compatible with various open RISC-V cores, offering scalability and convenience. And importantly, we propose the most energy-efficient architecture for multicore processors equipped with AESware. Instead of assigning dedicated AESware to each core, we introduce a shared AESware architecture to maximize energy efficiency. We develop an operational algorithm for task scheduling in AESware, achieving maximum utilization and minimal latency while maintaining its lightweight nature. To evaluate our solution, we developed 24 processors into three groups: AESware-equipped, baseline, and those with an external AES accelerator per core. After FPGA (Field-Programmable Gate Array) prototyping for functional verification and power consumption analysis via 45 nm process technology synthesis, our findings revealed significant energy savings. AESware-equipped processors achieved up to 76%, 47%, and 33% energy savings at dual-, quad-, and octa-core configurations compared to baseline, respectively, and were more energy-efficient in running AES applications than those with individual accelerators.

Improved adaptive genetic algorithm for dynamic multi-specification one-dimensional cutting problem

Rebar is an essential material in the construction of bridges and houses. Rebar cutting is an important link in rebar processing, but it is usually completed by manual experience, which is not only time-consuming, but also causes serious waste and reduces the economic benefits. As the country vigorously promotes the green construction method, the traditional rebar cutting method is difficult to meet the development requirements. So dynamic multi-specification one-dimensional cutting problem is studied in this paper. A mathematical model aiming at the maximum utilization rate of raw material is established, and an improved adaptive genetic algorithm is proposed. Large-scale, small-scale, single-specification masterbatch and multi-specification masterbatch are selected for simulation experiments, respectively. The results show that the proposed algorithm can deal with both large- and small-scale multi-specification or single-specification masterbatch cutting problems. Moreover, the algorithm has good performance in solving accuracy and convergence speed, which verifies its feasibility, effectiveness, and stability. Finally, aiming at the problem of dynamic insertion of orders, one-dimensional cutting software is developed, rapid and real-time cutting of rebars is realized, and the utilization rate of building rebars is improved, which plays a positive role in promoting the high-quality development of construction industries such as bridges.

Optimal Consumption and Investment with Income Adjustment and Borrowing Constraints

In this paper, we address the utility maximization problem of an infinitely lived agent who has the option to increase their income. The agent can increase their income at any time, but doing so incurs a wealth cost proportional to the amount of the increase. To prevent the agent from infinitely increasing their income and borrowing against future income, we additionally consider a non-negative wealth constraint that prohibits borrowing based on future income. This utility maximization problem is a mixture of stochastic control, where the agent chooses consumption and investment, and singular control, where the agent chooses a non-decreasing income process. To solve this non-trivial and challenging problem, we derive the Hamilton–Jacobi–Bellman (HJB) equation with a gradient constraint using the dynamic programming principle (DPP). Then, using the guess-and-verify method and a linearization technique, we obtain a closed-form solution to the HJB equation and, based on this, find the optimal strategy.