Long-term Decisions Research Articles

A regret-based robust optimization model for municipal water distribution network redesign under disruption risks

In recent decades, the increasing demand for water and resource limitations have posed challenges to water resources management. This study proposes a comprehensive model for redesigning urban water distribution network under disruptions, focusing on household consumption. The proposed model integrates strategic decisions for network, and addresses disruption risks. To ensure the resilience of the network against disruptions caused by earthquakes, we developed a regret-based robust optimization model. The model minimizes redesign and operational costs by optimizing water flow, construction locations, and pipeline connections. Numerical results demonstrate the efficacy of the proposed model, revealing a reduction in average costs—approximately 0.6 %—compared to the deterministic model. This reduction is noteworthy given the scale of municipal water distribution configuration costs. Also, resiliency analysis demonstrates the superiority of the robust solution compared to the deterministic structure. The model supports policymakers for operational and long-term decisions, considering complex hydraulic constraints and horizontal transfers between reservoirs.

Long-Term Fair Decision Making through Deep Generative Models

This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems. To define long-term fairness, we leverage the temporal causal graph and use the 1-Wasserstein distance between the interventional distributions of different demographic groups at a sufficiently large time step as the quantitative metric. Then, we propose a three-phase learning framework where the decision model is trained on high-fidelity data generated by a deep generative model. We formulate the optimization problem as a performative risk minimization and adopt the repeated gradient descent algorithm for learning. The empirical evaluation shows the efficacy of the proposed method using both synthetic and semi-synthetic datasets.

Can Technological Advancements be Effective for the Economic Value of the Companies? Insights from the DSE Listed Food and Allied Companies

Purposes: Adopting technology in the production process increases the overall efficiency of inputs used in the production, and enhances a firm's economic value. Methodology: This study tries to explore the impact of technology on firm performance from 8 listed Food and Allied companies spanning 2015-2021 listed on DSE. Total factor productivity (TFP) is an inclusive measure that apprehends the contribution of all aspects that improve the efficiency of production including technology. Therefore, the contribution of technical advancement to the firm is captured by TFP, applying the Solow residual method. TFP is the main study variable and some other control variables, such as tangibility, liquidity, firm size, and growth; long-term leverage ratio and net debt tax shield are used as exogenous variables to increase the robustness of the model and improve the value of both the firm performance ROA and ROE. Results: Finally, this study concluded that investment in technology in the food and allied sectors has a notable impact on a firm’s performance. Therefore, the study indicates that economists, policymakers, and business management can use TFP growth as an indicator for making their long-term decision policy and increase the overall sustainable economic productivity, growth and efficiency of the firm and can contribute to accelerating the economic growth of the country.

Supporting Decision-Making for Promoting Teaching and Learning Innovation

The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this study, we evaluate an analytic tool called PROF-XXI that supports strategic decision-making of teaching and learning centres (TLCs) by identifying their competencies in teaching and learning innovation. Through a multiple case study conducted with three Latin American universities and supported by quantitative and qualitative data, we observed how this tool is used and how it facilitates strategic decision-making. Our findings indicate that the tool is accessible, user-friendly, and effective in 1) initiating identification and systematic reflection of institutional competency levels in teaching and learning innovation, 2) enhancing understanding of strengths and weaknesses as well as identifying opportunities for innovation, 3) supporting TLCs with short- and long-term decision-making, and 4) continuously evaluating their strategies, programs, and initiatives. This research can benefit policymakers in higher education who are involved in measuring institutional competencies to improve teaching quality or in making strategic decisions related to teaching and learning innovation.

Enhancing seasonal influenza projections: A mechanistic metapopulation model for long-term scenario planning

In temperate regions, annual preparation by public health officials for seasonal influenza requires early-season long-term projections. These projections are different from short-term (e.g., 1–4 weeks ahead) forecasts that are typically updated weekly. Whereas short-term forecasts estimate what “will” likely happen in the near term, the goal of scenario projections is to guide long-term decision-making using “what if” scenarios. We developed a mechanistic metapopulation model and used it to provide long-term influenza projections to the Flu Scenario Modeling Hub. The scenarios differed in their assumptions about influenza vaccine effectiveness and prior immunity. The parameters of the model were inferred from early season hospitalization data and then simulated forward in time until June 3, 2023. We submitted two rounds of projections (mid-November and early December), with the second round being a repeat of the first with three more weeks of data (and consequently different model parameters). In this study, we describe the model, its calibration, and projections targets. The scenario projection outcomes for two rounds are compared with each other at state and national level reported daily hospitalizations. We show that although Rounds 2 and 3 were identical in definition, the addition of three weeks of data produced an improvement to model fits. These changes resulted in earlier projections for peak incidence, lower projections for peak magnitude and relatively small changes to cumulative projections. In both rounds, all four scenarios presented conceivable outcomes, with some scenarios agreeing well with observations. We discuss how to interpret this agreement, emphasizing that this does not imply that one scenario or another provides the ground truth. Our model's performance suggests that its underlying assumptions provided plausible bounds for what could happen during an influenza season following two seasons of low circulation. We suggest that such projections would provide actionable estimates for public health officials.

Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures.

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.

Impact of the Breast Cancer Index for Extended Endocrine Decision-Making: First Results of the Prospective BCI Registry Study.

The Breast Cancer Index (BCI) test assay provides an individualized risk of late distant recurrence (5-10 years) and predicts the likelihood of benefitting from extended endocrine therapy (EET) in hormone receptor-positive early-stage breast cancer. This analysis aimed to assess the impact of BCI on EET decision-making in current clinical practice. The BCI Registry study evaluates long-term outcomes, decision impact, and medication adherence in patients receiving BCI testing as part of routine clinical care. Physicians and patients completed pre-BCI and post-BCI test questionnaires to assess a range of questions, including physician decision-making and confidence regarding EET; patient preferences and concerns about the cost, side effects, drug safety, and benefit of EET; and patient satisfaction regarding treatment recommendations. Pre-BCI and post-BCI test responses were compared using McNemar's test and Wilcoxon signed rank test. Pre-BCI and post-BCI questionnaires were completed for 843 physicians and 823 patients. The mean age at enrollment was 65 years, and 88.4% of patients were postmenopausal. Of the tumors, 74.7% were T1, 53.4% were grade 2, 76.0% were N0, and 13.8% were HER2-positive. Following BCI testing, physicians changed EET recommendations in 40.1% of patients (P<.0001), and 45.1% of patients changed their preferences for EET (P<.0001). In addition, 38.8% of physicians felt more confident in their recommendation (P<.0001), and 41.4% of patients felt more comfortable with their EET decision (P<.0001). Compared with baseline, significantly more patients were less concerned about the cost (20.9%; P<.0001), drug safety (25.4%; P=.0014), and benefit of EET (29.3%; P=.0002). This analysis in a large patient cohort of the BCI Registry confirms and extends previous findings on the significant decision-making impact of BCI on EET. Incorporating BCI into clinical practice resulted in changes in physician recommendations, increased physician confidence, improved patient satisfaction, and reduced patient concerns regarding the cost, drug safety, and benefit of EET.

Exploration of research and development, foreign ownership, and carbon emissions disclosure in Indonesia

Carbon emission disclosure (CED) is one of the corporate world's efforts to assist environmental conservation while also improving the company's public image. Efforts are being made to make firm operations greener by optimizing research and development (R&amp;D), which leads to environmental improvements. Several prior studies investigated research and development on corporate social responsibility in general. The purpose of this study is to investigate the the impacts of R&amp;D and foreign ownership on carbon emissions disclosure in Indonesian companies. This research also examines proactive steps done by firms to significantly reduce emissions through green research and development and supported by large foreign share ownership, which is regarded to be involved with high concern. The empirical analysis applies multiple regression and moderated regression, with purposive sampling on the Indonesia Stock Exchange (BEI) company between 2016 and 2019, obtained 522 samples. Carbon emissions disclosure is measured using a checklist index based on the Carbon Disclosure Project (CDP) and content analysis of the company's sustainability report. Based on the findings, RnD intensity has a positive impact on carbon emissions disclosure (CED), however foreign ownership in Indonesia has no effect on the relationship between RnD intensity and CED. This suggests that R&amp;D will enhance the amount of carbon emissions disclosure, yet foreign ownership in Indonesia remained minimal, hence it has minimal effect on the company's long-term decisions. Research has limitations in recognizing RnD that implemented proactive approaches, so further studies on each company are required for future research.

Heterogeneous Market Hypothesis in Major European Stock Exchanges

The aim of this study is to investigate heterogeneous market efficiency in European stock exchanges using Augmented HAR-RV model. According to the heterogeneous market efficiency hypothesis, investors create portfolios according to different time horizons and different market situations may arise in the reflection of information on price. We find evidence of the validity of the heterogeneous market efficiency model in European stock exchanges. Investors interpret information differently at different time horizons. Medium- and long-term investment decisions are a major influence. These results help explain the volatility that may occur in different time horizons. Portfolio diversification should also be made according to different investments in different horizons. Short-term global volatility shock has been effective on European stock markets.

The Influence of E-Service Quality and Food Quality on Behavioral Intention through Attitude: Study on Millennial Moms as Online Grocery Shoppers in West Java

This study aims to examine the effect of e-service quality and food quality on behavioral intention through attitude among millennial moms as online grocery shoppers in West Java, Indonesia. Purposive sampling was used to obtain a sample of 298 respondents who met the research criteria. SEM-PLS testing was used to analyze the data. What was found in the course of the work? This will refer to analysis, discussion, or results. The results of this study indicate that the e-service quality and food quality variables have a significant positive influence on attitude. Additionally, attitude was found to have a significant positive effect on behavioral intention also playing a role as mediator. This study specifically examines the behavior of millennial mothers in West Java, with data collection conducted immediately after the pandemic period. Differences in behavior are highly likely to be found when research is conducted subsequently. The findings of this study can provide valuable insights into the influence of e-service quality and food quality on behavioral intention in the Indonesian online grocery industry specifically for West Java Market. It offers companies a deeper understanding of consumer behavior and the crucial factors that drive long-term decision-making in online grocery services. This study presents a unique perspective by examining the impact of the post-pandemic digitalization trend on online grocery shopping. The special emphasis lies in the researcher's ability to provide insights into post-pandemic behavioral changes, positioning the pandemic as a catalyst for shifts in digitalization trends. In contrast to other studies focusing on behavioral aspects during the pandemic, this research sheds light on behavior after the pandemic.

The Importance of a Coordinated Career Guidance System in Addressing the Rural NEETs Issue

A rapidly ageing population, the dual transition, major changes in the job market, and the coronavirus and its effects amplify existing disparities (gender gap, urban-rural cleavages), posing a considerable challenge for peripheral regions. In these circumstances, the labour market integration of young people who are neither in employment nor in education or training (NEET) is becoming increasingly urgent for peripheral regions such as rural areas. Various legislation, policies, and community-based interventions play a significant role in promoting integration. Still, subjective factors such as self-efficacy beliefs affect perceptions of career opportunities and can even prevent some from seizing opportunities. As a result, approaches to tackling the rural NEET issue need to focus on a long-term, sustainable solution. One could be career guidance, which helps individuals take charge of their lives and choose meaningful careers and educational paths. Consequently, our research aimed to identify the conditions in 22 European countries related to career guidance that lead to low rural NEET rates among 25–29-year-olds. Career guidance systems were explored through content analysis of country-specific reports on lifelong guidance systems and then analysed using the fuzzy-set qualitative comparative analysis. The research results draw attention to the importance of coordinated career guidance systems in preventing and (re)integrating NEETs, as it helps make informed, meaningful, and long-term career decisions.

Design of a model for improving emergency room performance using a colored Petri net

Emergency rooms are one of the most complex and vital areas of healthcare institutions, which have presented overcrowding, long waiting, and length of stay times, affecting the timeliness, responsiveness, and quality of service. This research aimed to design a detailed patient flow model to improve emergency room performance using the hierarchical timed colored Petri nets. Then, the model was simulated to evaluate scenarios considering tactical decisions such as physician staff planning, operational decisions such as adjusting work schedules, and strategic decisions such as increasing observation beds. The best scenario would reduce the average waiting times for triage II patients by 17.30 % and 47.57 %, and triage III by 33.49 % and 43.49 % for medical consultation in the office or the minor surgery room, respectively. In addition, the waiting time in observation and the rate of patients left without being seen by a physician would be reduced by 92.45 % and 74.67 %, respectively. These results improve the quality and timeliness of the service and avoid putting the patient's health and life at risk. The designed model included more attributes for patients concerning the place of medical care in the emergency room, the number of visits to the physician, and the physician who will care for the patient. Moreover, the simulation model includes observation beds as a limited resource blocking new patient admission. Finally, this model is a tool to support emergency room managers in making short, medium, and long-term decisions to address problems such as overcrowding, long waiting and length of stay times, and high rates of patients left without being seen by a physician

A System for Individual Environmental Risk Assessment and Management with IoT Based on the Worker’s Health History

This paper presents a monitoring system to measure dust, noise, ultraviolet radiation, illuminance, temperature, and humidity and to check the presence of flammable gases—liquefied petroleum gas (LPG), propane, hydrogen, butane, methane, and carbon monoxide. The system is composed of monitoring devices, a server to process data received from the devices and that runs a web application for employers, and a mobile application for workers to check their exposure data. During employee registration, the health history of the worker concerning common diseases and symptoms related to the monitored agents must be informed. This information is analyzed through a set of predefined rules to generate alerts that the company can consider for planning professional activities to minimize exposure to risk agents. The tests presented in this work are focused on verifying the functionalities of the web interface for employee registration, especially regarding the provision of health information, the updating of this information, and the generation of corresponding alerts. At this stage, fictitious employee information was used. The results showed that the system adequately performs the proposed functionalities, and it was concluded that the solution is viable for testing in a work environment in the future stages of development. The innovative features of this study concern the correlation between workers’ health history and harmful agents to build suggestions and alerts to guide long-term OSH-related decisions.

Integrated Stochastic Underground Mine Planning with Long-Term Stockpiling: Method and Impacts of Using High-Order Sequential Simulations

The integrated optimization of stope design and underground mine production scheduling is an approach that has been shown to effectively leverage the synergies among these two underground mine planning components to generate truly optimal stope layouts and extraction sequences. The existing stochastic integrated methods, however, do not include several elements of a mining complex, such as stockpiles, due to the computational complexity and non-linearity that they might add to the optimization of the long-term mine plan. Additionally, sequential simulation methods that rely on two-point statistics and Gaussian distribution assumptions are commonly used to generate the input realizations of the mineral deposit. These methods, however, are not able to properly characterize complex spatial geometries or the high-grade connectivity of non-Gaussian and non-linear natural phenomena. The present work proposes an extension of previous developments on the integrated stope design and underground mine scheduling optimization through an expanded stochastic integer programming formulation that incorporates long-term stockpiling decisions. An application of the proposed method at an operating underground copper mine compares the cases in which the geological simulated orebody models are based on high-order and Gaussian sequential simulation methods. The extraction sequence and related final stope design are shown to be physically different. It is seen that the optimization process takes advantage of the better representation of high-grade connectivity when high-order sequential simulations are used, by targeting the areas with grades that follow the mill’s blending requirements and by making less use of the stockpiles. Overall, a 4% higher copper metal production and a resultant 6% higher net present value are observed when high-order sequential simulations are used.

Cost Analysis the Implementation of New Material: The Case Study of Cement Product

This study will perform a cost analysis of using synthetic and natural gypsum by using the traditional costing method and to determine the investment analysis of the initiative strategy of gypsum synthetic. The research purpose is to compare the impact of gypsum synthetic implementation cost and gypsum natural consumption cost, and to find out whether the savings cost from gypsum synthetic consumption can cover the investment costs of implementing strategy. The existing calculation cost of material consumption products uses traditional costing methods, so this research is using this method that will compare the consumption costs between both Gypsum. Net present value and interest rate return methods are to determine the levels of investments needed to overcome problems in the consumption of synthetic gypsum. According to this research, the result of the net present value methods was IDR 1,543,004,654 and the interest rate return reaches 17.09%, for the planned case of 10 years, which means that this initiative is accepted and feasible to apply, it means that to produce cement, for the application of gypsum synthetic consumption as initiative strategy and application of 3 alternative strategies for these investment will result in positive impact for long-term decision.&#x0D; Keywords: cement industry, gypsum synthetic, production, cost analysis, traditional costing, net present value, internal rate return

Bittersweet memories and somatic marker hypothesis: adaptive control in emotional recall facilitates long-term decision-making in the Iowa Gambling Task.

The somatic marker hypothesis states that emotional recall and its somatic influence guide long-term decision-making. However, the mechanism through which decision-making benefits from emotional recall is unclear; whether emotional recall and the induced affect increase the regulatory demand or amplify the affect state that requires inhibition. It is unclear if controlling the automatic flow of emotion in recall improves adaptive decision-making. Two studies examine the hypothesis that affect control in emotional recall facilitates inhibitory control and benefits long-term decision-making. In Experiment 1 (n = 137), affect control was assessed in emotional recall to examine if switching of affect in recall of positive and negative valence (order: positive-negative memory recall vs. negative-positive memory recall) is linked with long-term decision-making. Results for long-term decision-making showed that negative-positive recall sequence was associated with higher long-term decision-making, whereas automatic frequency-based decision-making remained unaffected by the recall sequence. In experiment 2 (n = 71, all male), emotional recall (positive vs. negative), recall specificity (i.e., specific vs. overgeneralized recall), and post-recall mood regulation (post-recall positive mood regulation vs. no regulation) was expected to facilitate long-term decision-making. Results showed that emotional recall and post-recall mood regulation (i.e., negative recall - positive mood and positive recall - negative mood) were associated with higher long-term decision-making (decks C' and D'). Results of frequency decision-making showed that positive emotional recall, and poor recall specificity led to infrequent punishment deck choices (decks B' and D'). Hierarchical regression indicated that emotional recall increased infrequent deck choices and accounted for 10% of choices made, recall specificity increased the explanatory power to 19%, and higher recall specificity was associated with fewer infrequent punishment deck choices. Affect control engaged via negative emotional recall, post-recall mood regulation, and recall specificity might be a potential mechanism through which affect control in emotional recall might facilitate long-term decision-making.

Determinants of Financial Security of European Union Farms—A Factor Analysis Model Approach

The objective of this study was to assess the level of financial security of farms and identify its determinants based on factor analysis. The data used in this research were obtained from the European FADN (Farm Accountancy Data Network). Factor analysis (FA) was employed to reduce the number of variables that potentially determine the financial security of farms. The results indicate that the surveyed entities maintained financial security between 2014 and 2021. This study suggests that it is necessary to examine these factors separately for farms engaged in crop farming and animal production. The results obtained for all farms were less satisfactory than those that took into account the specifics of agricultural production. This study addresses a gap in the literature by including highly correlated variables in the analysis of the determinants of financial security. Factor analysis is used to reduce the number of variables without losing important information. Firstly, seventeen variables related to the financial security of all farms were assigned to six factors. These were income and self-financing of operations; area and subsidies; long-term investments and financial decisions consequences; economic size, taxes, and non-breeding livestocks; investment activity; and inputs, stock, short-term loans, and labor. Then, the determinants of the financial security of farms were examined, taking into account the specialization of activities. For crop-producing farms, six factors were identified, including three that were identical to those for all farms: income and self-financing of operations; long-term investment and financial decisions consequences; and investment activity. In addition, the following items were specified: area, subsidies, non-breeding livestocks, and taxes; economic size, inputs, and labor; and stock and short-term loans. The correlated variables in the case of livestock production combined into factors in a different way. In this case, four factors were distinguished: economic size, non-breeding livestocks, income, and self-financing of operations; operational activities of animal production; long-term investment and financial decisions consequences; and investment activity. Financial security is a complex matter that can be affected by a range of factors related to agricultural activities.

Ownership structure and carbon emissions reduction

This study investigates the relationship between ownership structure and reductions in carbon emissions. The Taiwan government passed the Greenhouse Gas Reduction and Management Act in July 2015, requiring carbon-intensive firms to disclose their annual direct and indirect carbon emissions. To mitigate endogeneity concerns, we use Taiwan's inheritance and estate tax reform in 2017 as an exogenous shock to a firm's ownership structure to conduct two-stage least squares analyses. We find that the higher the controlling shareholder's cash flow rights, the more a firm's direct carbon emissions will be reduced, consistent with the positive incentive effect, long-term decision-making, and socio-emotional wealth argument. Furthermore, we also find that when the controlling shareholder's board seat ratio highly deviates from their cash flow rights, the controlling shareholder is less concerned about carbon risk and even increases direct carbon emissions, consistent with the negative entrenchment effect.

Long-term multi-objective optimal scheduling for large cascaded hydro-wind-photovoltaic complementary systems considering short-term peak-shaving demands

The effective synergy among hydropower, wind power, and photovoltaic enhances variable renewable energy (VRE) utilization. However, existing long-term hydropower operating rules that consider runoff uncertainty neglect escalating peak-shaving demands. This paper optimizes the long-term decision-making of hydropower by supplementing the demand to buffer short-term VRE uncertainty. Latin hypercube sampling and K-means clustering reduction are utilized to generate coupling scenario sets to quantify long-term runoff and short-term VRE uncertainties. Then, a double-layer multi-objective optimization model is developed, the outer layer maximizes the long-term expected energy production of the whole system, and the inner layer minimizes hourly residual load fluctuation of power grids under daily electricity boundaries. The daily electricity is dependent on the load profile and monthly generation. Moreover, A power-electricity coupling approach is proposed to bridge long- and short-term operations and reduce the computational burden. An optimal set of pareto solutions is finally obtained with different constraint boundaries, where a compromise solution is selected by a fuzzy decision method. The model has been implemented on the Lancang River hydro-wind-photovoltaic complementary system in Southwest China. The results indicate that: (1) the model can transfer hydropower electricity from flood season to dry season, facilitate VRE integration, increase overall benefits, and improve short-term hydropower regulation capacity, especially in wet years; (2) the obtained Pareto solution set has significant advantages. Under multi-year average conditions, energy production increases by 11 million kWh, hydropower curtailment decreases by 751 million kWh, and residual load fluctuations reduce by 2200 MW, and (3) increasing transmission capacity is more conducive to improving system efficiency in wet years than in dry years, and is helpful to enhance hydropower flexibility.

Econometric issues in the estimation of the natural rate of interest

The natural rate of interest is a key benchmark steady-state rate of return that is used by central banks to measure the stance of monetary policy. Asset managers rely on estimates of the natural rate to make long-term investment decisions for their clients. This paper outlines a number of important econometric issues in the estimation of the natural rate of interest from a widely used structural model. Using a simulation experiment as well as empirical data for the U.S., I show that one of the key parameters of the structural model is overestimated and leads to a spuriously amplified downward trend in the natural rate. The paper shows how the overestimation can be remedied and provides alternative estimates of the natural rate. Various other issues are discussed that policy makers should be aware of when utilizing this model’s natural rate estimates for policy decision.