Decision Policy Research Articles

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass’ characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory’s biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

Time for upgrades? In time for consumers and competition

An upward line extension is a new, improved product within an existing product category for the high-end market. Many manufacturers pursue line-extension strategies, but they carry risks due to uncertain consumer taste, the internal competition between products, and the external competition between firms. We use game theory to answer when a firm should allocate production capacity for an upward line extension under competition and consumer taste uncertainty. The firm can either act early when taste is still uncertain to benefit from the first-mover advantage or wait until consumer taste realizes. We measure consumer taste risk by the correlation between the average consumer taste and the density of consumer taste, with a strong correlation meaning a considerable risk exposure, foreshadowing a more likely hit-or-miss result for the new product. The firm’s competitive advantage is based on both firms’ expected marginal revenues of capacity allocation, considering the internal and external competition. We show how the competitive advantage will amplify exponentially in the face of consumer taste uncertainty and characterize the firm’s decision policy by deriving a threshold for the risk index against the competitive advantage index. When the threshold is exceeded, it implies that the firm’s competitive advantage is not significant enough, so it should postpone allocating capacity. Otherwise, it should act early. We apply our results to two industry examples and provide practical guidelines to manufacturers for determining the right capacity timing for an upward line extension.

Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review

Integrated multi-trophic aquaculture (IMTA) systems integrate the cultivation of species from different trophic levels. Uneaten feed, solid wastes, and dissolved nutrients are turned into harvestable and healthy food, making IMTA a driver for ecologically sustainable aquaculture. Its wider sustainability potentials arise from social, environmental, and economic sustainability enhancement options. Biological and economic outcomes are promising, while social equity and acceptance remain to be further investigated in the context of the long-term viability of aquaculture. Sustainable coastal and marine aquaculture development requires a holistic approach that involves social/cultural, economic, as well as environmental sustainability. This article examines IMTA as a pathway to socially, environmentally, and economically sustainable development. We collate evidence that shows that IMTA can minimize the negative environmental effects of aquaculture, assist local economies, and boost competitiveness and long-term economic viability. Available analyses of socio-economic and cost-effectiveness reveal positive prospects for IMTA systems, through product diversification, faster production cycles, and IMTA product prices and show a divergence between financial returns at the level of the entrepreneurial unit and economic returns at the macro level, which inhibits the uptake of IMTA. We conclude that the lack of governance analysis or inappropriateness of institutional development, in terms of aquaculture governance and management laws and regulations, is at the core of the hitherto weak engagement with IMTA. Unsuitable policies, regulations, and public and private sector decision policies and implementation, underlined by the scarcity of analyses of aquaculture governance institutions, are part of the reason for this. The evidence we have aggregated indicates that the relative scarcity of commercially successful coastal IMTA undertakings is not so much an intrinsic feature of the IMTA approach but is likely to have been generated by missing or inappropriate governance structures and procedures in the coastal realm.

Wireless Multiplayer Interactive Virtual Reality Game Systems With Edge Computing: Modeling and Optimization

Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation.

Robust Enhancement of Intrusion Detection Systems Using Deep Reinforcement Learning and Stochastic Game

The incorporation of advanced networking technologies makes modern systems vulnerable to cyber-attacks that can result in a number of harmful outcomes. Due to the increase of security incidents and massive activities on networks, existing works have mainly focused on designing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intrusion Detection Systems (IDSs)</i> based on traditional machine learning and deep learning models. In recent times, state of the art performance has been achieved in various fields through Deep Reinforcement Learning (DRL), which combines deep learning with reinforcement learning. In this paper, we propose a new DRL-based IDS for network traffics using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Markov decision process (MDP)</i> to improve the IDS decision-making performance. In addition, an extensive analysis of the IDS behavior is provided through modeling the interaction between the well-behaving IDS and attacker players using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Stochastic Game Theory</i> . Specifically, we used a non-zero-sum stochastic game, where the transitions between states depend on both the IDS and the attacker’s actions at each stage of the game. We show that our game reaches a Nash Equilibrium upon convergence to seek the optimal solution, which corresponds to the optimal decision policy where both players maximize their profits. We compared the performance of our proposed DRL-IDS to the baseline benchmark of standard reinforcement learning (RL) and several machine learning algorithms using NSL-KDD dataset. As a result, our proposed DRL-IDS outperforms the existing models by improving both the detection rate and the accuracy while reducing false alarms. Results were provided to demonstrate the convergence of the game theory-based IDS under various settings toward equilibrium. This equilibrium corresponds to the safe state where both players are playing their respective best strategies.

Evidential policy in the management decision making mechanism at the macro level

The article presents a comparative analysis of alternative approaches to the formation of state economic policy in the modern world — purely ideological, political-conjunctural and scientific-evidential ones disclosing the unconditional advantages of the latter. Evidential economic policy that comprehensively assess available options for achieving high-priority macroeconomic goals is seen as a way to counter real-world threats to management decision-making at the macro level, based only on the ideological leanings of political parties and their theorists (classics and Keynesians), as well as the fact that opportunistic politicians persistently serve not the public interests, but their own selfi sh ones. Contributing to the formation of the methodological foundations of evidential economic policy, the author emphasizes the need to not limit oneself to careful collection of real facts, to formation of a "register of best practices" in world economic history and to the use of modern econometric methods in order to avoid reproaches in empiricism. Absolute emphasis in this case should be made on identifying cause-eff ect relations in the economic system in their strong correlation. Thus, success in the implementation of the evidential economic policy of the country is mostly associated with the state of macroeconomic theory, its ability to provide comprehensive analysis and scenario forecasting of the economic situation.

Quantifying the impact of delivery day flexibility on last-mile delivery costs

Last-mile delivery operations are often required to uphold rigid delivery dates, such as the day after an order is placed. However, given the inherent temporal stochasticity in customer order placement, this practice can overwhelm the available vehicle fleet with long routes and the need for driver overtime. This work explores the potential benefits of customer flexibility, wherein the delivery day can be chosen by the distributor from among pre-agreed delivery day windows spanning two or more consecutive days, allowing the delivery day to be co-optimized along the associated vehicle routes and leading to distribution cost savings. To that end, we develop a rolling horizon simulation framework that integrates a novel forecasting scheme for sampling order realizations with a branch-and-cut algorithm for solving the multi-period vehicle routing instances arising on each day. Computational studies using real-life industrial data are conducted to compare various decision policies and quantify the long-term cost savings that are possible when allowing such flexibility. In certain cases, we reveal the potential of significant cost savings–up to 12%–compared to the current “next-day” delivery policy. Our study also investigates the extent of discount incentives that may be offered to customers for accepting flexible delivery days.

An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

Background music changes the policy of human decision-making: Evidence from experimental and drift-diffusion model-based approaches on different decision tasks.

Music is ubiquitous in our lives. Although we listen to music as an activity in and of itself, music is frequently played while we are engaged in other activities that rely on decision-making (e.g., driving). Despite its ubiquity, it remains unknown whether and how background music modulates the speed and accuracy of decision-making across different domains. We hypothesized that music could affect decision-making through a subjective-timing distortion or via a policy shift toward less-cautious responding. We analyzed response times and accuracy from more than 100-thousand decisions and mapped the effects of music onto decision-process components with a mechanistic model of decision-making. We found evidence supporting the latter hypothesis, by which decisions-across domains-were faster but less accurate with music, and this trade-off was mainly driven by a less conservative decision policy. Overall, our results suggest that background music shapes our decisions by making us less cautious. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Relation between Covid-19 and the Teledensity of India

Digital connectivity is viewed as a great leveller; however, when access to digital connectivity is skewed, those able to wield it benefit from profitable opportunities while those left behind the times have to play catch up. This gap creates a hindrance to the economic development of a country. This situation can be observed in India, where certain strata of the society are left behind due to geographical, prejudicial, and political reasons. However, recently Covid-19 has posed an anomaly. There has been uncertainty in the telecommunications climate due to the pandemic. Moreover, if the pandemic were to help telecommunications and teledensity, it has to be wielded correctly, supplemented with quick and decisive policies; it would lead to a net positive in India’s efforts to bridge the digital divide. A study using the teledensity data sets of 17 regions from January 2014 through October 2021 was run to analyse the correlation between Covid-19 and the teledensity of these regions. We observed that the relation between Covid-19 cases and teledensity was positive and statistically significant at the 5% level. With a 1% increase in Covid-19 cases, the teledensity increased by 8%, and a rise in Covid-19 cases could explain 27% of the variance in teledensity.

Risk Factors of Prostate Cancer Among Patients Diagnosed at the University College Hospital, Ibadan, Nigeria: A Case-Control Study

Background and aims: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men and its incidence is higher among black folks for reasons yet unclear. Although few risk factors have been linked to the development of PCa among Nigerian men, it remains unclear whether these risk factors modulate the odds of PCa. Methods: The case-control study comprised forty-three cases and one hundred and twenty-nine age-matched controls (±5 years) without PCa by prostatic specific antigen (PSA) examination. Conditional logistic regression analysis was employed to identify risk factors associated with PCa at P&lt;0.05 using SPSS 20. Results: Increasing age (adjusted odds ratio [AOR]: 2.8; 95% CI: 1.7-4.5) was the strongest risk factor for PCa, followed by increasing age at first sex (AOR: 1.2; 95% CI: 1.1-1.4) and sexual activity (AOR: 0.2; 95% CI: 0.1-0.9) as a protective risk factor against PCa in this Nigerian Population. Conclusion: The study confirms age as a recognized risk factor and backed evidence for other hypothesized risk factors. The study recommends findings with other confirmatory studies that can help to guide policies for better health care decisions among Nigerian men and interventions centered on routine screening for PSA with an emphasis on the elderly clinic is encouraged.

Using cognitive models to design dynamic task allocation systems

Many operations in Intelligence, Surveillance, and Reconnaissance (ISR) involve balancing multiple simultaneous interdependent tasks and coordinating between multiple teammates. Recently, autonomous managers (AMs) were proposed as a method for optimizing performance in team-based workflows using dynamic reallocation in response to changes in workload and performance. We demonstrate how cognitive models can be used in the design and evaluation of AMs. Specifically, cognitive models can be used to inform an AM’s decision policy, and to stress test an AM under a wide variety of conditions. Simulation 1 tested the robustness of numerous AMs across a wide range of cognitive agents. We found that a simpler cognitive model in the AM’s decision system was more robust than more complex models. In the second simulation study, we compared dynamic task reallocation and corrective feedback to improve performance of cognitive agents based on the ACT-R cognitive architecture. Our results indicate that both interventions have the potential to improve performance, and that the most robust AM from simulation 1 can improve the performance of a model with realistic learning dynamics. Our simulations demonstrate that cognitive models are useful for designing and evaluating AMs for multiple military applications, including ISR.

Family New Venture Creation: The Impact of External Enablement Characteristics

This research investigates the impact of potentials drawn from the external environment, so-called enablement on the creation of a family new venture. Based on the framework of External Enabler and family capital theory, we posit that three characteristics of enablement (i.e., opacity, agency-intensity, and controllability) affect entrepreneurs’ decision to involve family members in new venture creation. We decompose 1,552 decision policies nested within a sample of potential entrepreneurs. Research findings suggest that entrepreneurs’ willingness to involve their family members in new venture creation increases as the agency-intensity of enablement decreases and the controllability of enablement increases. We further find that entrepreneurs’ prior start-up experience moderate these relationships by enhancing the positive effect of low agency-intensity and mitigating the positive effect of high controllability. We discuss both theoretical and practical implications.

Prenatal malaria exposure and risk of adverse birth outcomes: a prospective cohort study of pregnant women in the Northern Region of Ghana

ObjectiveMalaria remains endemic in most of sub-Saharan Africa and has a negative impact among pregnant women, resulting in morbidity and poor birth outcomes. The purpose of this study was to...

Deep Reinforcement Learning With Graph Representation for Vehicle Repositioning

On-demand ride services, e.g., taxi companies, ridesourcing/transportation network companies (TNCs), play a vital role in nowadays transportation modes. The relocation/repositioning of idle vehicles is an important operational problem for service providers. With the recent success of deep learning, a large and growing body of literature emerged on learning-based transportation problems, including the relocation problem. In this work, we study the idle vehicle relocation problem to achieve better drivers’ profits and customers’ satisfaction. In detail, we formulate the learning problem on real-world road networks while recent repositioning works under a deep reinforcement learning scheme formulate the environment as grids. Meanwhile, a graph formulation enables learning with Graph Neural Network (GNN), which shows promising results in recent transportation studies. We empirically show that preserving the road network structure and learning the data representation with GNN contribute to improving the repositioning decision policy. Our simulation experiments are conducted on two real-world datasets, in New York City and Austin, respectively. The results show our formulation’s advantages in terms of reductions of order rejection rates and passenger waiting time, and increasing of vehicle occupancy rates and driver revenues.

New Venture Decisions of Corporate and Independent Entrepreneurs

Opportunity evaluation is a critical step in the process of entrepreneurship and is the main precursor to entrepreneurial action. This is true for both corporate entrepreneurs and independent entrepreneurs. However, these two groups may evaluate entrepreneurial opportunities in different ways because they operate in different contexts and have different decision-making schemas. In this study, I use an “entrepreneurial cognition” perspective to explore such differences. By using two major theoretical lenses – i.e. resource availability and tolerance for uncertainty – and employing a conjoint experimental design, I compare and contrast decision policies of corporate and independent entrepreneurs captured in real time. The findings of this study shed light on differing cognitions of entrepreneurs in corporate and non-corporate contexts and explain their decision-making priorities. Findings provide evidence that the four opportunity attributes studies – i.e. knowledge of customer demand, resource-relatedness, novelty, and entry scale – significantly affect willingness of entrepreneurs to pursue opportunities but that the effect for all attributes is stronger among independent entrepreneurs. Findings of this study also demonstrate that gender and entrepreneurial experience have significant impacts on opportunity assessments of corporate and independent entrepreneurs, but with varying levels.

Carbon dioxide intensity of GDP and environmental degradation in an emerging country.

This paper investigates whether the carbon dioxide (CO2) intensity of GDP matters for environmental degradation in an emerging country, namely, Turkey, over the period of 1990-2018 while controlling economic growth, foreign direct investment, and renewable energy consumption. The present study uses both linear and nonlinear time series estimators, namely, the Gregory and Hansen cointegration test, bounds test, nonlinear autoregressive distributed lag (NARDL) model, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegrating regressions (CCR), to encapsulate the possible effect of CO2 intensity of GDP, economic growth, foreign direct investment, and renewable energy consumption on environmentaldegradation in Turkey. The empirical finding of the present study reveals that the CO2 intensity of GDP is an important factor to determine environmental degradation in Turkey and the declining CO2 intensity of GDP reduces environmentaldegradation.Moreover, economic growth is the primary environmental sustainability factor in Turkey. The result is vital for policymaking and can perhaps be applied to take decisive policy actions to mitigate environmental issues.

Short Blocklength Process Monitoring and Scheduling: Resolution and Data Freshness

In cyber-physical systems (CPSs) and internet-of-things applications, various sensor-actuator pairs are deployed for control purposes which require timely online communication. The sensors are measuring information about the CPS, e.g., process systems, whereas the actuators are using the information to take control actions. These sensor-actuator pairs usually communicate via the same wireless medium and thus their transmissions need to be scheduled in time. When transmitting the process data, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short blocklength source-channel</i> coding approach is employed to reduce data errors. We investigate the influence of the decision policy consisting of communication parameters and scheduling design on data freshness and accuracy of process monitoring systems. An age-of-information (AoI) metric is used to assess data timeliness, while the mean square error (MSE) is used to assess the precision of the predicted process values. We characterize the AoI and MSE with closed-form expressions for the blocklengths and accuracy levels, for special types of scheduling strategies, namely, round-robin and maximum-age scheduling. We optimize the coding strategies by showing an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">achievability region</i> of AoI and MSE. Other priority-based scheduling policies are also investigated. It is shown that the maximum-age policy provides excellent results in terms of AoI, while priority-based scheduling performs better in terms of MSE.

Assessment of the Endorsers of E-Business Practices for Food Supply Chain Performance Systems

Globalisation and changing lifestyle of the people has escalated the demand for the more product customisation, taste preferences and awareness about the usage of quality food commodities. Recent developments in the field of information technology and its integration with the business practices has emerged as a new term named ‘e-business’ (EB). Increasing consumer base of the food supply chains (FSC), has escalated the demand of technological and operation advancements by mediating ‘EB’ activities. Such, practices become extensively crucial when the world is suffering from the pandemic of COVID-19, leading to distressing of FSC linkages causing frequent market closures. To tackle the same presented work, explores the various endorsers (EDR) of the ‘EB’ in FSC, which are contemplated by hybrid combination of multi-criteria decision making techniques. Outcomes of the present work aids managers to formulate the decision policies and develop a robust framework in the direction to cling the ‘EB’ practices with FSC.

Stochastic Optimization Forests

We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables (e.g., demand). We show how to train forest decision policies for this problem by growing trees that choose splits to directly optimize the downstream decision quality rather than split to improve prediction accuracy as in the standard random forest algorithm. We realize this seemingly computationally intractable problem by developing approximate splitting criteria that use optimization perturbation analysis to eschew burdensome reoptimization for every candidate split, so that our method scales to large-scale problems. We prove that our splitting criteria consistently approximate the true risk and that our method achieves asymptotic optimality. We extensively validate our method empirically, demonstrating the value of optimization-aware construction of forests and the success of our efficient approximations. We show that our approximate splitting criteria can reduce running time hundredfold while achieving performance close to forest algorithms that exactly reoptimize for every candidate split. This paper was accepted by Hamid Nazerzadeh, data science. Funding: This work was supported by the National Science Foundation [Grant 1846210]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4458 .