Dynamic Decision Research Articles

Bibliometric analysis of groupthink in organization: Cases study in the world

A phenomenon that often occurs in an organization is groupthink. Groupthink is a concept that describes the habit of seeking consensus in organizations or groups; most of the results of this process are a terrible final decision and fatal to the organization. Research Objective To analyze groupthink in the organization. The method used in this study is qualitative research with a literature study approach. The data source in this study was taken from the Scopus database by entering the keyword "Groupthink AND organization" with the number of articles, namely, 63 documents. This study found that the global groupthink study from 1982 to 2022 was only 63 articles; this shows that groupthink studies within the organization have not been studied much. At the same time, groupthink is often found in an organization. The group think tank began to be widely studied from 2005 to 2022, although it is understood that it is not significantly experiencing a development trend. Next, during the time bracket from 1982 to 2022, groupthink was studied in terms of behavior, political group dynamics in organizations, decision-making, communication and legal cases in an organization and was widely encountered in the United States of America.

Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye

ABSTRACT The most important key to determining sustainable energy policies is reliable and accurate energy demand forecasting. Grey prediction, one of the demand forecasting methods, makes successful forecasts with limited data and without the need for any prior knowledge. In this study, GM (1,1) and Grey Verhulst from time series models and GM (1, N) model based on cause–effect relationship were used to forecast Türkiye’s natural gas demand. Dynamic grey forecasting models have been developed in order to obtain robust and reliable predictions. All developed models were evaluated according to performance criteria, and the superior model was obtained to be GM (1,5). Within the scope of the study, it was determined that models based on cause–effect relationship should be preferred instead of time series models. For this, population, the amount of electricity generated from natural gas, industrial production index, and building area are determined as independent variables. Three different scenarios were created taking into account the specified independent variables, and natural gas demand until 2025 was obtained. According to the forecasting results, Türkiye’s natural gas demand for 2025 will happen as an interval between 29.61 and 53.62 mtoe based on the low and high scenarios, respectively. According to the expected scenario, this demand would be realized around 40 mtoe for Türkiye in the year 2025. Finally, Grey Dynamic Decision Support System was designed and introduced to easily apply the dynamic grey models by end-users.

A digital twin framework for civil engineering structures

The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availability. This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures. The asset-twin coupled dynamical system is encoded employing a probabilistic graphical model, which allows all relevant sources of uncertainty to be taken into account. In particular, the time-repeating observations-to-decisions flow is modeled using a dynamic Bayesian network. Real-time structural health diagnostics are provided by assimilating sensed data with deep learning models. The digital twin state is continually updated in a sequential Bayesian inference fashion. This is then exploited to inform the optimal planning of maintenance and management actions within a dynamic decision-making framework. A preliminary offline phase involves the population of training datasets through a reduced-order numerical model and the computation of a health-dependent control policy. The strategy is assessed on two synthetic case studies, involving a cantilever beam and a railway bridge, demonstrating the dynamic decision-making capabilities of health-aware digital twins.

Reinforcement learning for humanitarian relief distribution with trucks and UAVs under travel time uncertainty

Effective humanitarian relief operations are challenging in the aftermath of disasters, as trucks are often faced with considerable travel time uncertainties due to damaged transportation networks. Efficient deployment of Unmanned Aerial Vehicles (UAVs) potentially mitigates this problem, supplementing truck fleets in an impactful manner. To plan last-mile relief distribution in this setting, we introduce a multi-trip, split-delivery vehicle routing problem with trucks and UAVs, soft time windows, and stochastic travel times for last-mile relief distribution, formulated as a stochastic dynamic program. Within a finite time horizon, we aim to maximize a weighted objective function comprising the number of goods delivered, the number of different locations visited, and late arrival penalties. Our study offers insights into dealing with travel time uncertainty in humanitarian logistics by (i) deploying Unmanned Aerial Vehicles (UAVs) as partial substitutes for trucks, (ii) evaluating dynamic solutions generated by two deep reinforcement learning (RL) approaches – specifically value function approximation (VFA) and policy function approximation (PFA) – and (iii) comparing the RL solutions with solutions stemming from mathematical programming and dynamic heuristics. Experiments are performed on both Solomon-based instances and two real-world cases. The real-world cases – the 2015 Nepal earthquake and the 2018 Indonesia tsunami – are based on locally collected field data and real-world UAV specifications, and aim to provide practical insights. The experimental results show that dynamic decision-making improves both performance and robustness of humanitarian operations, achieving reductions in lateness penalties of around 85% compared to static solutions based on expected travel times. Furthermore, the results show that replacing half of the trucks with UAVs improves the weighted objective value by 11% to 56%, benefitting both reliability and location coverage. The results indicate that both the deployment of UAVs and the use of dynamic methods successfully mitigate travel time uncertainties in humanitarian operations.

Building Human-Like Artificial Agents: A General Cognitive Algorithm for Emulating Human Decision-Making in Dynamic Environments

One of the early goals of artificial intelligence (AI) was to create algorithms that exhibited behavior indistinguishable from human behavior (i.e., human-like behavior). Today, AI has diverged, often aiming to excel in tasks inspired by human capabilities and outperform humans, rather than replicating human cogntion and action. In this paper, I explore the overarching question of whether computational algorithms have achieved this initial goal of AI. I focus on dynamic decision-making, approaching the question from the perspective of computational cognitive science. I present a general cognitive algorithm that intends to emulate human decision-making in dynamic environments, as defined in instance-based learning theory (IBLT). I use the cognitive steps proposed in IBLT to organize and discuss current evidence that supports some of the human-likeness of the decision-making mechanisms. I also highlight the significant gaps in research that are required to improve current models and to create higher fidelity in computational algorithms to represent human decision processes. I conclude with concrete steps toward advancing the construction of algorithms that exhibit human-like behavior with the ultimate goal of supporting human dynamic decision-making.

Optimizing Green Strategy for Retired Electric Vehicle Battery Recycling: An Evolutionary Game Theory Approach

As the global new energy vehicle (NEV) industry rapidly expands, the disposal and recycling of end-of-life (EOL) power batteries have become imperative. Efficient closed-loop supply chain (CLSC) management, supported by well-designed regulations and strategic investments, plays a crucial role in sustainable waste power battery recycling. In this study, an evolutionary game theory (EGT) methodology is used to construct a tripartite game model to investigate the interactions among manufacturers, recyclers, and the government to study the decision-making dynamics of green investments. In addition, numerical simulations are performed to evaluate the sensitivity of the relevant parameters on the stability of the evolution of the system. The results reveal that government green subsidies can stimulate early period investments in advanced recycling technologies. However, as the battery recycling industry matures, a ‘free-rider’ behavior emerges among enterprises, which can be mitigated through the imposition of a carbon tax. Eventually, as the industry reaches maturity, manufacturers and recyclers autonomously invest for enhanced profitability. This research provides valuable insights for government policy formulation, facilitating the formal recycling of retired batteries and fostering sustainability in the NEV sector.

A framework for measuring the maturity of real-time information management systems (RTIMS) in the mining industry

Real-time information management systems (RTIMS) are emerging for the mining industry. RTIMS are created to enable minimal-latency (real-time) of information delivery and usage to enable dynamic and smart decision making. However, being an emerging concept, it is unclear what the target readiness of RTIMS should be. As a result, system owners may face challenges identifying appropriate RTIMS performance models. In this study, RTIMS is treated as an integration of information, as a resource and as the systems for managing this resource, in real-time, across all phases of the mining value chain. Consequently, the capability maturity assessment tool for RTIMS must consider: information as the critical resource; the management systems for the critical resource; and the access to the resource in real-time. Thus, a hybridised maturity model is proposed for the RTIMS maturity target, which can be further optimised through stakeholder engagement. In our proposed framework, three elements namely, people, the environment, and technology, have been identified as key enablers for RTIMS in the minerals sector. In addition, we propose a six-level capability maturity model, with levels ranging from 0 to 5, signifying the state of no recognition of the processes to an optimised state, where prevention and maintenance are the norm.

The development of a farmer decision-making mind map to inform climate services in Central America

The growing complexity of the relationship between climate information and agricultural decision-making necessitates the development of relevant and timely climate services for farmers. These services can effectively support risk management strategies in agriculture by fostering a comprehensive understanding of the intricacies involved in farmer decision-making dynamics. This paper addresses this critical gap by analyzing the drivers influencing decision-making processes that shape adaptation strategies for staple grain and coffee farming systems in Central America. The study answers the following research questions: (i) Does the mind map tool effectively provide a holistic understanding of farmers' decision-making processes? (ii) How do Central American farmers make decisions within their farm systems at multiple timescales? (iii) Which climate factors trigger these decisions? Employing a combination of systematic literature review and a case study in Honduras, the study identifies 13 critical decisions farmers make throughout their crop cycle and their respective triggers. These decisions were grouped into three clusters (production, household, and environmental) and classified into lead-time categories (operational, tactical, and strategic). Findings reveal that farmers base their decisions regarding future climate expectations on their traditional knowledge, religious dates, and memories of recent past seasons' rainfall patterns, and that one of the most significant factors influencing farmers' decisions is food security shortages resulting from extreme events. For example, recent mid-summer droughts have led farmers to prioritize sowing beans over maize in the Primera season, while during the Postrera season, they face challenges due to excess rainfall and the hurricane season. We conclude that the mind map tool developed in this paper provides an effective and appropriate method and that the variation in farmers' decision-making complexity across systems and landscapes presents a significant opportunity to design mind maps that span multiple timescales, facilitating the exploration of decision spaces. Farmers actively seek tailored weather and climate information while still valuing their existing experience and local knowledge, emphasizing the importance of integrating these elements into the development of climate services.

Rethinking Reliability in Terms of Margins

Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance.
 This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources.
 This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

Mediating the Drive: How Brand Image Shapes the Adoption of Technology and Innovation in Bangkok's Electric Vehicle Market

Background and Aim: The electric vehicle (EV) market in Bangkok has evolved driven by environmental concerns, urban congestion, government incentives, charging infrastructure development, and consumer preferences, making it a promising contributor to a cleaner and sustainable urban mobility landscape. This research explores the dynamics of technological acceptance, innovation, brand image, and decision-making in the context of electric vehicle (EV) adoption. Therefore, this research can be used as a guideline for policymakers, marketers, and industry stakeholders aiming to promote sustainable and technologically advanced transportation solutions. Materials and Methods: The study employs a quantitative approach, utilizing a questionnaire-based survey method, and targets a sample of 200 participants residing in Bangkok with prior knowledge of electric vehicles. Following the principles outlined by Gorsuch (1983), simple random sampling was applied. The index of item-objective congruence (IOC) was between 0.67-1.00. The Cronbach's alpha coefficient was between 0.835 - 0.919. Statistical analysis techniques encompassing percentages, averages, standard deviations, and structural equation modeling (SEM) were employed. Results: The findings of this research reveal that individuals possessing knowledge about electric cars exhibit a significantly high level of acceptance regarding technology, innovation, brand image, and decision-making. Also, they all have high scores in every aspect with a mean of 3.97, 3.81, 3.87, and 3.98, as well as standard deviations of 0.792, 0.764, 0.690, and 0.652, respectively. Furthermore, the study elucidates the factors influencing decision-making (DM) regarding electric vehicle purchases, with technology acceptance (TA) and innovation (IN) emerging as critical determinants. Both TA and IN exert direct effects on DM with values equal to 0.122 and 0.241. Additionally, these factors indirectly impact DM with values equal to 0.159 and 0.495, respectively, which underscore their multifaceted roles in shaping consumer choices. Importantly, TA and IN also have a direct effect of 0.270 and 0.840 on brand image. Notably, brand image has a direct effect on DM with values of 0.589. Conclusion: The findings underscore the significance of informed consumer knowledge and positive brand image in fostering the adoption of electric vehicles, aligning with the broader societal goal of sustainable and environmentally conscious mobility solutions.

Education in perennial crisis: Have we been asking the right questions?

When a crisis is talked about for six decades, it begs the questions: How is the educational crisis being defined? If a crisis continues for sixty years, is it still a crisis; or are some inherent characteristics of the education system and process being misrepresented? In other words, are we asking the right questions? The writer, from five decades of involvement in efforts to enhance the relevance of education to fighting poverty, inequality and exclusion, suggests some likely missteps in conceptualizing and defining education system problems. The experience of Yidan education prize aim of ‘creating a better world through education’ is brought up to illustrate the complexities of navigating through some of the missteps raised in the article. The discussion of the challenges and efforts to address them lead to a two-fold conclusion -- that the narrative of persistent crisis arises from a mismatch of expectations from education and the system’s capacity to deliver in the larger socio-political context; and that political settlement determines chosen activities and priorities, what is implemented or not implemented – not necessarily the technical merits of a solution. Influencing the political dynamics of decision-making is critical for educational transformation, which education scholars and advocates of reform tend to neglect. The pragmatic way ahead lies in major stakeholders in education uniting to build a supportive social compact that changes the political dynamics for realizing the expectations from education.

Research on the tripartite evolutionary game promoting the transformation of traditional buildings to prefabricated buildings.

The practices of prefabricated buildings illustrate the incentives in prefabricated markets as well as the decision-making behavior of stakeholders, which can influence the transformation, upgrading, and sustainability of the buildings sector. The game between the stakeholders in the prefabricated buildings market becomes complicated by the fact that they dynamically adjust their strategies in response to changing market conditions. With the aim of quantitatively studying the implications of the dynamic decision behavior of prefabricated construction stakeholders on the prefabricated construction market, this study introduces component suppliers as participating agents and government subsidies and penalties as the main measures. And a three-way model of the evolutionary game involving government subsidies and punitive schemes is constructed by this study. The essay examines the evolutionary equilibrium strategies of each game subject as well as the effect of original strategies and different parameter choices on the decision-making of each subject using MATLAB. The findings reveal that (1) decision-making behaviors among the government, component suppliers, and developers are both interrelated and constrained, but governments are the dominant agents in the evolution of prefabricated buildings. (2) Parameters such as subsidies, penalties, costs, and benefits affect the stability of the tripartite evolutionary model, which remains consistent with the previous research. (3) Component suppliers have an equally important position in the development of prefabricated buildings as recognized entities such as developers and contractors. (4) When subsidies are kept at 40% of the incremental cost and penalties at 80% of the incremental cost, the model combines a fast evolutionary rate with stable model evolution. In conclusion, the article's research findings not only validate the rationality and feasibility of component suppliers as research subjects and confirm the importance of component suppliers but also propose a more reasonable boundary value for penalties and subsidies, which provides a reference for the government to improve the incentive and punishment measures as well as the dynamic adjustment strategy of stakeholders and also provides a new perspective to achieve the goal of sustainable development of construction.

Robust optimization for a dynamic emergency materials supply chain network under major infectious disease epidemics

ABSTRACT The COVID-19 outbreak has posed serious challenges to logistics and supply chain management. This study examines how living materials can be provided to a population under quarantine control due to the spread of infectious diseases. First, we present a modified SEIR model for predicting the number of people and materials demanded. Considering the dynamic evolution and other characteristics of infectious diseases, and the resulting demand uncertainty, we develop a robust optimization model for a multi-period dynamic emergency materials supply chain network with the objective of minimising total cost. We then transform it into a solvable robust counterpart model, propose a dynamic adjustment strategy, and cope with it using the Rolling Horizon approach to obtain dynamic location and materials distribution decisions. Finally, we conduct a case study of the Shanghai epidemic in 2022 to verify the feasibility and effectiveness of our model and method.

Spatial–temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks

Balancing network traffic among base stations poses a primary challenge for mobile operators because of the escalating demand for enhanced data speeds in large-scale 5G radio applications. Within cellular networks, traffic flow prediction constitutes a pivotal issue in numerous applications, such as resource allocation, load balancing, and network slicing. In this paper, traffic prediction based load balancing framework with reinforcement learning is proposed to optimize neighbor cell relational parameters that can better balance traffic within a defined geographical cluster. Spatial–temporal-event cross attention graph convolution neural network (STECA-GCN) is put forward to predict the precise traffic flow. The model takes event dimension features into account, while also incorporating direct cross-fusion among diverse features. Concurrently, we have developed a strategy based on deep reinforcement learning to facilitate dynamic load balancing decisions. Simulation results show that our proposed load balancing framework can improve overall system performance. In particular, the combination of loading rate and energy efficiency can achieve a 12% improvement. The load balancing of the base station can better deal with social emergencies.

A novel incremental attribute reduction approach for incomplete decision systems

With the increasing diversification and complexity of information, it is vital to mine effective knowledge from information systems. In order to extract information rapidly, we investigate attribute reduction within the framework of dynamic incomplete decision systems. Firstly, we introduce positive knowledge granularity concept which is a novel measurement on information granularity in information systems, and further give the calculation method of core attributes based on positive knowledge granularity. Then, two incremental attribute reduction algorithms are presented for incomplete decision systems with multiple objects added and deleted on the basis of positive knowledge granularity. Furthermore, we adopt some numerical examples to illustrate the effectiveness and rationality of the proposed algorithms. In addition, time complexity of the two algorithms are conducted to demonstrate their advantages. Finally, we extract five datasets from UCI database and successfully run the algorithms to obtain corresponding reduction results.

Underinvestment and optimal capital structure under environmental constraints

We develop a Q-theoretical model for levered firms subject to environmental constraints (regulations). The model highlights the implications of environmental constraints on dynamic investment and optimal capital structure decisions. We find that environmental constraints can lead equityholders to become endogenously risk averse. Moreover, environmental constraints give rise to underinvestment in capital and asset sales, which is attributed to the carbon abatement effect caused by environmental constraints. In addition, firms with environmental constraints use conservative debt for low business risk and choose high leverage for high business risk relative to firms without environmental constraints, which is governed by the trade-off between abatement cost effect and risk-aversion effect caused by environmental constraints. Firms with environmental constraints have high credit spreads. Our theoretical results are consistent with some empirical findings.

Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models

Developing effective multi-agent systems (MASs) is critical for many applications requiring collaboration and coordination with humans. Despite the rapid advance of multi-agent deep reinforcement learning (MADRL) in cooperative MASs, one of the major challenges that remain is the simultaneous learning and interaction of independent agents in dynamic environments in the presence of stochastic rewards. State-of-the-art MADRL models struggle to perform well in Coordinated Multi-agent Object Transportation Problems (CMOTPs) wherein agents must coordinate with each other and learn from stochastic rewards. In contrast, humans often learn rapidly to adapt to non-stationary environments that require coordination among people. In this article, motivated by the demonstrated ability of cognitive models based on Instance-based Learning Theory (IBLT) to capture human decisions in many dynamic decision-making tasks, we propose three variants of multi-agent IBL models (MAIBLs). The idea of these MAIBL algorithms is to combine the cognitive mechanisms of IBLT and the techniques of MADRL models to deal with coordination MASs in stochastic environments from the perspective of independent learners. We demonstrate that the MAIBL models exhibit faster learning and achieve better coordination in a dynamic CMOTP task with various settings of stochastic rewards compared to current MADRL models. We discuss the benefits of integrating cognitive insights into MADRL models.

Research on dynamic decision-making for product assembly sequence based on Connector-Linked Model and deep reinforcement learning

Assembly sequence planning (ASP) is a critical challenge in the manufacturing industry, involving the arrangement of components to maximize production efficiency and reduce costs. Solving the ASP problem is not only hindered by its NP-hard nature, making it difficult to obtain optimal solutions, but also requires making rapid decisions in the face of dynamic changes in resource availability during the actual assembly process. This paper proposes a Deep reinforcement learning (DRL) approach to tackle the ASP problem. DRL learns optimal assembly strategies through interactions between an intelligent agent and its environment, using a reward function as a long-term objective to guide decision-making, ultimately enabling the acquisition of near-optimal solutions. DRL exhibits high scalability and reusability, leveraging previously acquired knowledge to adapt to dynamic changes in the assembly environment. First, a connector-based graph structure is introduced to decompose assembly products into a series of connector-based assembly elements. These elements are then transformed into a matrix format for use as input to the DRL method. Second, an encoder-decoder architecture tailored to the ASP problem is developed. This architecture utilizes mask mechanism to ensure that the generated assembly sequences adhere to assembly priority constraints while adapting to dynamic changes in resource availability during assembly. Finally, policy gradient methods are employed to train the DRL model, enabling it to generate assembly sequences with lower costs and higher efficiency. Experimental results demonstrate the effectiveness of the proposed approach. Furthermore, compared to heuristic algorithms, DRL exhibits faster decision-making capabilities and better adaptability to dynamic resource changes during the assembly process.

Improving the Efficiency of the Organization Based on the Automation Criterion

The modern development of information technologies involves their introduction into the technological processes of enterprises and organizations. At the same time, the head of an enterprise or organization is faced with a stream of problems arising in the work of the system he manages, must make timely decisions to identify the flow of problems arising in the system, identify them and neutralize them as soon as possible, for this he needs to have an analytical dynamic solution model. The published literature currently considers models based on the analysis. Consideration of the model on the basis of a natural-scientific approach will allow you to build a model adequate to the situation under consideration, which will bring results. In this study, an analytical dynamic decision model for a decision maker (LPR) has been developed. Formed on the basis of the synthesis process, the transformation of a management decision into a solution model. The solution model will depend on the basic components of the system under consideration for the occurrence of a flow of problems, identification of a flow of problems and neutralization of a flow of problems. The resulting synthesis indicator of the implementation of the management solution allows you to identify the flow of problems in the managed system and neutralize it, as well as to obtain characteristics about the operation of the managed system. By setting the required indicator of the effectiveness of the implementation of a management decision and solving the inverse problem, the manager can obtain the necessary characteristics of the system to guarantee the achievement of the goal of the activity. As a result, the manager receives the automation criterion and, with the help of the resources available to him, adjusts the work of the organization to the required level of the indicator. The automation criterion obtained in the work will be the rule by which the automation of the enterprise takes place, which will allow solving important tasks for the LPR and timely counteract emerging problems in the management system.

Healthy shopping dynamics: The healthiness of sequential grocery choices

Improving the healthiness of diets can be realized by replacing unhealthy with healthier product alternatives when shopping for groceries. For this strategy to be effective, shoppers need to consistently make healthier choices. However, shoppers may end up balancing the healthiness of their choices throughout the shopping trip, (partly) offsetting the benefits of a healthy product choice (e.g., low-fat milk) by an unhealthy subsequent choice (e.g., sugary cornflakes). Across two studies, one study with purchase data from a brick-and-mortar supermarket and one online experimental study, we empirically demonstrate that the relative healthiness of an initial product choice is indeed inversely related to the relative healthiness of the subsequent choice, regardless of the category of both products. That means: a relatively healthy choice is followed by a relatively unhealthier choice, and vice versa. Furthermore, the strength of this balancing effect differs depending on the nature of the product category; the dynamic effect is less pronounced when subsequently choosing within a vice (vs. virtue) product category. In the brick-and-mortar supermarket, the dynamics also become less pronounced as the shopping trip progresses. These findings contribute to literature on in-store decision-making and within-trip dynamics, and underscore the need for retailers to have a thorough understanding of these healthy shopping dynamics in order to effectively promote healthier baskets in support of the growing demand for healthy diets.