Recent advances in persona-centric memory have revealed the powerful capability of multi-agent systems in managing persona memory, especially in conversational scenarios. However, these complex frameworks often suffer from information loss and are fragile across varying scenarios, resulting in suboptimal performance. In this paper, we propose DeltaMem, an agentic memory management system that formulates persona-centric memory management as an end-to-end task within a single-agent setting. To further improve the performance of our agentic memory manager, we draw inspiration from the evolution of human memory and synthesize a user-assistant dialogue dataset along with corresponding operation-level memory updating labels. Building on this, we introduce a novel Memory-based Levenshtein Distance to formalize the memory updating reward, and propose a tailored reinforcement learning framework to further enhance the management capabilities of DeltaMem. Extensive experiments show that both training-free and RL-trained DeltaMem outperform all product-level baselines across diverse long-term memory benchmarks, including LoCoMo, HaluMem, and PersonaMem.
Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving toward predictive approaches relying on historical data. In this paper, we create a nonparametric model using a spatiotemporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.
Real-time feedback applications are reshaping employee performance feedback in operations management. Their design and implementation significantly influence employee engagement, which is a key factor in the success of technology-driven business innovations. This study investigates an implementation strategy to optimize feedback app use by employing digital nudges to encourage regular engagement. Drawing on social norms theory and cognitive load theory, we examine how message framing and users’ cognitive states affect the quantity and content features of feedback, including ratings, review length, surprise, and recognition. We conducted two randomized experiments to evaluate the effectiveness of digital nudges. Experiment 1, a field study with over 250 users of the DevelapMe app in a financial firm, tested how message framing and timing influenced cognitive load. Experiment 2, an online randomized controlled trial, explored underlying mechanisms and validated cognitive load measures. The results show that while social norms-based nudges increase feedback volume, they are associated with shorter reviews and reduced recognition and surprise. Employees experiencing lower cognitive load provided more feedback but tended to give lower ratings. Importantly, the interaction between social norms and low cognitive load resulted in higher ratings and more detailed reviews. This suggests that reducing cognitive load can enhance the positive effects of social norms on feedback quality. This study underscores the moderating role of cognitive load in the effectiveness of digital nudges and offers insights into feedback design that promotes both participation and quality. Theoretically, it contributes to research on digital feedback systems by integrating social norms and cognitive load theories to explain employee feedback behavior. Practically, it provides guidance for managers in designing real-time feedback tools that strategically use digital nudges while minimizing cognitive load, fostering a culture of continuous feedback, strengthening engagement, and improving performance management systems.
Garros Gong, Stanko Dimitrov, Michael R. Bartolacci
This work addresses the operational conflicts between visibility-driven mobilization and cost efficiency in disaster management scenarios involving wildfires. Using official wildfire reporting on the social media platform Twitter (now X), we develop a temporal gravity model to extract a signal of public attention for California wildfires (2007–2021) without the “noise” of spurious content. Interpreting this signal through the lens of behavioral disaster management operations, our analysis finds a "Visibility-Efficiency Paradox". This paradox shows that while social media visibility functions as a potent mobilization signal to the general public during wildfires and is associated with greater resource deployment, it simultaneously correlates with reduced cost efficiency under high resource use loads. We identify resource saturation as a boundary condition where heuristic signals appear to shift from valuable inputs to potential stressors. These findings challenge the assumption that high visibility of responders in a wildfire emergency is a direct proxy for operational urgency and effectiveness. We propose actionable strategies, including reverse audits and decoupling, in order to help counteract salience bias; thus, highlighting the potential for algorithmic governance to align public attention with sustainable resource management.
The success of a gig platform is crucially driven by its ability to compete for labor supply. However, gig workers are independent contractors whose working schedules are not fully controlled by the platform. To overcome this challenge, gig platforms have commonly relied on bonus strategies to drive the participation of gig workers. We study the impact of bonus strategies on gig platforms and their welfare implications. We consider two types of bonus strategies used by gig platforms: (1) fixed bonus that is paid in addition to commissions as long as a service provider participates and (2) contingent bonus that is paid only if a service provider participates consistently over time. We develop a game theory model to study platform competition with bonus strategies. Our analysis shows that the two types of bonuses will arise in equilibrium under different market conditions. First, when labor supply is thick, fixed bonus will be offered. In this case, fixed bonus improves platform profit by eliminating a prisoner’s dilemma that arises when the platforms compete only on commissions. However, social welfare will be reduced because the utilization of the labor supply is reduced due to the softened platform competition. Second, when labor supply is thin, contingent bonus will be offered. In this case, contingent bonus reduces platform profit because it intensifies platform competition and traps the platforms in a prisoner’s dilemma where they are forced to offer too much bonus. It further causes inefficiency in matching labor supply with demand and hence reduces social welfare.
Benkhoff, Lana, Straub, Natalia, Thayaparan, Thanushan
et al.
With the increasing automation of production and logistics systems, the demand for autonomous mobile robots (AMRs) has risen significantly in recent years. They enable an efficient and flexible organization to optimize material flow. Besides greenfield projects, AMRs are particularly suited for brownfield applications due to their ability to integrate into existing plants and operating structures. These abilities come with particular planning and implementation challenges as they encompass both socio-technical and organizational aspects due to the interference with ongoing operations in established structures. Many companies struggle to find qualified personnel or to develop internal expertise. Against this background, a comprehensive approach to implementing AMRs in brownfield environments is required. This paper aims to provide a widespread overview of the scientific literature concerning holistic planning approaches for AMR deployment through a systematic literature review. These results provide an entry point for the development and consolidation of theoretical models, describing deficits regarding brownfield implementations as well as AMR specifications and identifying additional topics that should be included in the planning processes like change management, employee acceptance, and sustainability.
Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon
et al.
The necessity of blockchain systems to remain decentralised limits current solutions to blockchain governance and dynamic management, forcing a trade-off between control and decentralisation. In light of the above, this work proposes a dynamic and decentralised blockchain management mechanism based on digital twins. To ensure decentralisation, the proposed mechanism utilises multiple digital twins that the system's stakeholders control. To facilitate decentralised decision-making, the twins are organised in a secondary blockchain system that orchestrates agreement on, and propagation of decisions to the managed blockchain. This enables the management of blockchain systems without centralised control. A preliminary evaluation of the performance and impact of the overheads introduced by the proposed mechanism is conducted through simulation. The results demonstrate the proposed mechanism's ability to reach consensus on decisions quickly and reconfigure the primary blockchain with minimal overhead.
LLMs are emerging tools for simulating human behavior in business, economics, and social science, offering a lower-cost complement to laboratory experiments, field studies, and surveys. This paper evaluates how well LLMs replicate human behavior in operations management. Using nine published experiments in behavioral operations, we assess two criteria: replication of hypothesis-test outcomes and distributional alignment via Wasserstein distance. LLMs reproduce most hypothesis-level effects, capturing key decision biases, but their response distributions diverge from human data, including for strong commercial models. We also test two lightweight interventions -- chain-of-thought prompting and hyperparameter tuning -- which reduce misalignment and can sometimes let smaller or open-source models match or surpass larger systems.
We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a provably optimal fair hiring policy that depends on the protected attribute functionally, but not statistically. The policy does not set rigid quotas, and does not withhold information from decision-makers. Both synthetic and real data indicate that the policy can greatly improve equity for underrepresented and historically marginalized groups, often with negligible loss in objective value.
Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.
Markus Voggenreiter, Florian Angermeir, Fabiola Moyón
et al.
In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles as they are a fundamental part of secure software development in the industry. A common practice arising from this trend is the automation of security tests that analyze a software product from several perspectives. To effectively improve the security of the analyzed product, the identified security findings must be managed and looped back to the project team for stakeholders to take action. This management must cope with several challenges ranging from low data quality to a consistent prioritization of findings while following DevOps aims. To manage security findings with the same efficiency as other activities in DevOps projects, a methodology for the management of industrial security findings minding DevOps principles is essential. In this paper, we propose a methodology for the management of security findings in industrial DevOps projects, summarizing our research in this domain and presenting the resulting artifact. As an instance of the methodology, we developed the Security Flama, a semantic knowledge base for the automated management of security findings. To analyze the impact of our methodology on industrial practice, we performed a case study on two DevOps projects of a multinational industrial enterprise. The results emphasize the importance of using such an automated methodology in industrial DevOps projects, confirm our approach's usefulness and positive impact on the studied projects, and identify the communication strategy as a crucial factor for usability in practice.
While the existing literature has studied the impact of demand uncertainty extensively within various monopoly settings, there is little research on its impact among competitive retailers. In this paper, we study the effects of demand uncertainty reduction in a setting with two newsvendor retailers competing on product availability. We parameterize the uncertainty level via a mean‐preserving spread and investigate the behavior of both retailers' equilibrium stocks and their expected profits in response to demand uncertainty reduction. We find that as one retailer's demand uncertainty decreases, it might move its order quantity away from the demand mean, which contrasts with the “pull‐to‐center” effect in the monopoly setting. Moreover, we show that as a retailer's demand uncertainty decreases, its own equilibrium profit increases while that of its competitor decreases.
Many individuals have strong preferences regarding COVID‐19 vaccines and would like to choose the vaccine they get. This practice, known as “vaccine shopping,” presents unique challenges to timely vaccine rollout: On the one hand, people may not get vaccinated if they are unable to receive their preferred vaccine, and on the other hand, a policy maker is inclined to distribute vaccines in a brand agnostic fashion to avoid wastage. We study whether a policy maker should allow individuals to choose their vaccine, and the optimal mix of single‐ and two‐dose vaccines to procure. We develop a stylized queueing game‐theoretic model that captures the main trade‐offs in the interaction between individuals and a policy maker to examine the impact of vaccine choice on the number of vaccinations. Individuals obtain a reward from the vaccine administered at a server and decide whether to get a vaccine based on the wait time, their inclination toward vaccinating, and the level of choice provided. We find that restricting choice results in a greater number of vaccinations when vaccine supply is low by administering doses as and when they become available. Contrary to popular belief that restricting choice wastes fewer vaccines, we find that fewer vaccines are wasted when patients who are moderately hesitant about vaccinating are allowed to choose their vaccine. In this case the possibility of being assigned a nonpreferred vaccine leads patients to forego vaccination, and allowing a choice alleviates this concern. Using a mathematical model for COVID‐19 transmission, we find that providing choice results in fewer infections in the United States than limiting choice, and the number of infections is lowest when a lower efficacy, single‐dose vaccine forms 5%–8% of the total vaccine dose supply. Our findings provide guidance to policy makers, especially as they plan to vaccinate effectively using a diverse vaccine supply.
Auto‐delivery is a subscription model widely employed in supply chains, whereby a supplier delivers products to a buyer (or multiple buyers) according to the buyer's choice of a constant shipping quantity to be delivered at prescheduled dates. The buyer enjoys a discount for the auto‐delivery orders and other benefits, including free subscription and cancellation. Because these benefits seem to all accrue to the buyer at the supplier's expense, the rationale for the supplier's decision to offer auto‐delivery and its impact on the profitability of both parties is an intriguing concern. We first develop a model that consists of a supplier and a single buyer, whereby the supplier offers a discount for the auto‐delivery orders and the buyer chooses the auto‐delivery quantity with the flexibility of cancelling the subscription. We derive the two parties' operating characteristics of their inventory systems and examine their optimal decisions. Our analysis shows that buyers benefit from the auto‐delivery discount; the supplier benefits from the demand‐expansion effect and the inventory‐reduction effect, a potential discount on the cost of the auto‐delivery units; and the supply chain benefits from reducing the bullwhip effect. We also find that channel coordination requires the supplier to pass the inventory‐related savings to the buyer through the auto‐delivery discount, which depends on the ratio of the two parties' holding cost rates. Moreover, we examine a model extension whereby the supplier announces a discount that is available for multiple buyers, we show that the supplier's optimal auto‐delivery discount under exponential demand can be determined based on the aggregate‐level demand information from all buyers. Finally, we discuss another model extension whereby the lead time of the supplier's recurring orders for auto‐delivery is longer than that of the regular orders and present a full analysis of the case when the lead time differential is one time period.
In the practical business of asset management by investment trusts and the like, the general practice is to manage over the medium to long term owing to the burden of operations and increase in transaction costs with the increase in turnover ratio. However, when machine learning is used to construct a management model, the number of learning data decreases with the increase in the long-term time scale; this causes a decline in the learning precision. Accordingly, in this study, data augmentation was applied by the combined use of not only the time scales of the target tasks but also the learning data of shorter term time scales, demonstrating that degradation of the generalization performance can be inhibited even if the target tasks of machine learning have long-term time scales. Moreover, as an illustration of how this data augmentation can be applied, we conducted portfolio management in which machine learning of a multifactor model was done by an autoencoder and mispricing was used from the estimated theoretical values. The effectiveness could be confirmed in not only the stock market but also the FX market, and a general-purpose management model could be constructed in various financial markets.
It is conventional wisdom that a manufacturer's encroachment into retail space will likely hurt an existing retailer. In contrast to this conventional belief, current research indicates that a retailer may welcome a manufacturer's encroachment despite the new competition in the final market. The encroachment may help the manufacturer have some “skin in the game” at the retail level, which will cause the manufacturer to make a selfish cost‐reducing investment that spills over to the retailer as a lower wholesale price. Such a spillover effect enhances the retailer's profit as long as the encroachment does not result in extreme retail competition by a certain degree of product differentiation, and ultimately generates Pareto gains in the supply chain. The spillover effect is so robust that the retailer's benefit from the encroachment remains even after considering potential mitigating factors such as selling costs, a nonlinear form of cost reduction, decentralized encroachment, additional retail competition, price competition, and a negotiation between the manufacturer and the retailer.
A challenge for “Big Data” in the chemical production industry is not only to evaluate file storage but also to use online information to improve process performance. It should be spectral, vibration, thermal, and other sensors are more and more widely available. In today's harsh industrial conditions, accurate and reliable reviews or product quality assessments are critical. To predict important attribute factors utilizing quantifiable signals, information soft sensors dependent on Projection to Latent Structure (PLS) techniques are frequently used. However, due to changes in equipment, raw material, sensors, or management, most operations are carried out under real and stable conditions. The structure of the flexible sensors must be maintained at regular intervals. Reconstruction of the method using more recent sensor primary data focus of current design maintenance techniques, such as mobile window updates and recursive updates within the enterprise. In situations where data were collected with extremes, downtimes, and other transients in the non-stationary phase, this strategy was not sufficiently resilient. An alternative model update strategy was reviewed as part of this study. To assess the effectiveness of the current soft sensor approach, they modified two Key Performance Indicators (KPIs). The residue-dependent forecast KPI identifies long-term forecast damping models using a filtered estimation error. The KPI dependent on T2 would be a forecast KPI that checks the system's speculations to the expected original data. This updated strategy is effective in improving predictive accuracy without completely reconstructing the PLS model using research papers using industrial operations information. Finally, the KPI attributes and model upgrade mechanism could be used together. The researchers demonstrated that this update technique significantly improved the accuracy of the PLS soft detector predictions through the emulation of live behavior using industrial data. The configuration technique also made it possible to quickly identify underlying issues in situations where the original sample was ideal as well as informed engineers that a new method needed to be built.
Electric apparatus and materials. Electric circuits. Electric networks
Aiming to ensure current market needs, manufacturing companies search for tools and methodologies that would help them deliver their products efficiently and cost-effectively and enable them to become a part of Industry 4.0. Digital twins are a technology created based on the idea of the Fourth Industrial Revolution. The solution helps recreate physical devices in virtual space based on gathered data. It supports performance tests, configuration changes, and predictive maintenance without engaging existing machines. The paper aims to gain knowledge about the awareness level of the digital twin technology among industry representatives and identify the most important problems that stand in the way of implementing the technology in enterprises. The research focused on market awareness of the described technology. It also examined how companies use employee suggestions to improve their organisations and the factors that influence process efficiency. The methods used for the research were a literature review and cross-sectional survey conducted with 50 employees of manufacturing and IT companies. The research showed the need to implement digital twins in enterprises. Half of the survey respondents replied that the technology would help improve the efficiency of the company’s processes. The main benefit of the conducted research is identified awareness of the technology among industry representatives. In the future, the research will be extended to include the analysis of specific cases affecting the implementation of digital twins in enterprises.
Supply chain is definable as “the activities covering the procurement of raw materials, storage, production and assembly, stock control, distribution, order management, and delivery of the product to the end user, as well as the information systems necessary for monitoring and controlling these activities” (Author, Year, p. ??). Information technologies occupy a very important place in supply chains Due to the very close relationship that exists between them. Among the information technologies used in supply chains, barcode technology has been preferred as it provides traceability and accuracy in all operations of the supply chain and has currently reached a very important position. Meanwhile, stocking and warehouse operations are also important in supply chains as they occur at all links of the supply chain and have become the primary area of improvement for companies that want to increase supply chain efficiency. Stocking and warehouse operations are currently carried out using barcode technology, but changing and developing conditions have affected customer needs, accordingly also affecting all supply chain links and ultimately stocking and storage operations. The increases in the quality and quantity of the products to be stored have made warehouse management difficult. However, the fact that this process is still being carried out with barcode technology is now slowing down the processes. For this reason, radio frequency identification (RFID) technology has come to the agenda of businesses as a fast technology free from human error. RFID technologies promise an environment of fast, human-independent operation wherever possible. As a result, businesses have become aware of the need for effective stocking and storage operations for an effective supply chain and have begun investigating how RFID technologies can be used in these operations. This article includes an example that discusses how RFID technologies can improve storage and warehouse operations.
A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events. Traditional congestion management modeling involves AC or DC power flow equations, while the former equation always accompanies great amount of computation, and the latter cannot consider voltage amplitude and reactive power. Therefore, this paper proposes a congestion management approach incorporating a specially-designed generalized generator shift distribution factor (GSDF) to derive a computationally-efficient and accurate management strategies. This congestion management strategy involves multiple balancing generators for generation shift operation. The proposed model is superior in a low computational complexity (linear equation) and versatile modeling representation with full consideration of voltage amplitude and reactive power.