A fast fashion system combines quick response production capabilities with enhanced product design capabilities to both design “hot” products that capture the latest consumer trends and exploit minimal production lead times to match supply with uncertain demand. We develop a model of such a system and compare its performance to three alternative systems: quick-response-only systems, enhanced-design-only systems, and traditional systems (which lack both enhanced design and quick response capabilities). In particular, we focus on the impact of each of the four systems on “strategic” or forward-looking consumer purchasing behavior, i.e., the intentional delay in purchasing an item at the full price to obtain it during an end-of-season clearance. We find that enhanced design helps to mitigate strategic behavior by offering consumers a product they value more, making them less willing to risk waiting for a clearance sale and possibly experiencing a stockout. Quick response mitigates strategic behavior through a different mechanism: by better matching supply to demand, it reduces the chance of a clearance sale. Most importantly, we find that although it is possible for quick response and enhanced design to be either complements or substitutes, the complementarity effect tends to dominate. Hence, when both quick response and enhanced design are combined in a fast fashion system, the firm typically enjoys a greater incremental increase in profit than the sum of the increases resulting from employing either system in isolation. Furthermore, complementarity is strongest when customers are very strategic. We conclude that fast fashion systems can be of significant value, particularly when consumers exhibit strategic behavior. This paper was accepted by Yossi Aviv, operations management.
ManMohan S. Sodhi, Byung‐Gak Son, Christopher S. Tang
Supply chain risk management (SCRM) is a nascent area emerging from a growing appreciation for supply chain risk by practitioners and by researchers. However, there is diverse perception of research in supply chain risk because these researchers have approached this area from different domains. This paper presents our study of this diversity from the perspectives of operations and supply chain management scholars: First, we reviewed the researchers' output, i.e., the recent research literature. Next, we surveyed two focus groups (members of Supply Chain Thought Leaders and International SCRM groups) with open‐ended questions. Finally, we surveyed operations and supply chain management researchers during the 2009 INFORMS meeting in San Diego. Our findings characterize the diversity in terms of three “gaps”: a definition gap in how researchers define SCRM, a process gap in terms of inadequate coverage of response to risk incidents, and a methodology gap in terms of inadequate use of empirical methods. We also list ways to close these gaps as suggested by the researchers.
This study introduces a joint inventory framework that links the vendor and the buyer, while addressing the production of both good-quality and defective units. During inspection, the defective items are separated to ensure that only flawless products are delivered to the buyer in several shipments. Instead of being discarded, the rejected units are directed to a secondary market where they are accepted at a lower price, which helps in minimizing waste. The model also integrates environmental aspects by considering carbon emissions generated in manufacturing, transportation, and storage activities. To promote greener practices, a carbon tax is incorporated, aligned with real-world pricing mechanisms and environmental regulations. This tax motivates firms to control emissions and adopt sustainable operations. The paper further develops a mathematical model to identify the optimal delivery schedule and cycle length under the effect of carbon taxation. A numerical example is presented to demonstrate the applicability of the model. The sensitivity analysis shows that while rising demand boosts profits, higher production speeds may decrease them, and increased carbon costs lower vendor earnings. These findings highlight the importance of strategic demand management, balanced production planning, and the adoption of eco-efficient technologies.
Abstract Multi energy demand response is an important measure to achieve the economic and efficient operation of the integrated energy system (IES), which is of great significance to promote the sustainable development of the IES. The concept of demand response (DR) is extended to IES, and a double objective operation optimization model of IES considering integrated demand response (IDR) mechanism is proposed. Firstly, based on the electro-thermal IES, this paper proposes a demand response mechanism considering electric load and thermal load. According to the load composition (transferable load, reducible load, adjustable thermal load), the load management strategy is established. Then, based on the DR mechanism of electro-thermal IES, a multi-objective operation optimization model is established for the first time, which takes the economic benefits and comprehensive energy efficiency as the objective function. The model involves the equipment constraint in the process of energy production and energy transmission. The relationship between total operation cost and comprehensive energy efficiency of electro-thermal IES is analyzed in the part of Data, Simulation Results and Analysis. Finally, the results show that the model effectively improves the economic benefits and comprehensive energy efficiency of IES, and reduces the pollutants emissions to a certain extent.
Abstract The purpose of this paper is to examine and discuss the current patterns, gaps and trends in Digital Twin research in the architectural, engineering, construction, operation, and facility management (AECO-FM) industry and to propose future directions for industry stakeholders . A bibliometric search was performed on 197 papers that were obtained from the Scopus database. 151 of those papers were used in the subsequent scientometric analysis and mapping to narrate the evolution of the research subject. The studies discussed in the paper cover a wide range of research from model-based information management to building information management, all the way to the interaction between buildings and smart cities. The ‘virtual-physical building integration’, ‘building lifecycle management’, and ‘information integrated production’ subjects are highly in the focus of the research field. While ‘information-based predictive management’ and ‘virtual-based information utilization’ studies need to be addressed for a holistic Digital Twin adoption in the AECO-FM industry. Future studies should focus on the full integration of digital twin and its physical counterpart for better performance throughout the building life cycle. This study highlights the patterns, gaps and trends in Digital Twin research in the AECO-FM industry and contributes to the state-of-the-art digitalization and automation approaches in the construction project management body of knowledge. The sample size is a limitation for this research since it is relatively small due to the newness of the subject. Future studies may include data related to practice-oriented innovations and industry-initiated improvements to achieve broader and more informed results.
Currently, major online retail platforms, such as Amazon and JD, provide both conventional reselling channels and online marketplaces (i.e., agency channels), enabling their suppliers to encroach more efficiently than the traditional self-established direct channel. We construct a parsimonious model of a bilateral monopoly supply chain where the supplier can encroach through online marketplaces as both reselling and agency channels are available. Surprisingly, the encroachment option does not necessarily benefit the supplier. Unlike the traditional supplier encroachment literature, the supplier now always prefers simultaneous ordering, suggesting that more information can hurt the supplier. We further characterize the retailer’s channel offering strategy and identify a win-win region where both firms prefer encroachment (i.e., the hybrid mode). Our work provides a guideline for supplier encroachment in the era of dominant platforms and sheds light on its impact on vertical channel relationships.
Anil Palepu, Valentin Liévin, Wei-Hung Weng
et al.
While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic response, and safe medication prescription - remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini's long-context capabilities, combining in-context retrieval with structured reasoning to align its output with relevant and up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialist physicians and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding of management plans in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies (US, UK) and validated by board-certified pharmacists. While AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management.
Zuzanna Osika, Roxana Rădulescu, Jazmin Zatarain Salazar
et al.
Many real-world problems (e.g., resource management, autonomous driving, drug discovery) require optimizing multiple, conflicting objectives. Multi-objective reinforcement learning (MORL) extends classic reinforcement learning to handle multiple objectives simultaneously, yielding a set of policies that capture various trade-offs. However, the MORL field lacks complex, realistic environments and benchmarks. We introduce a water resource (Nile river basin) management case study and model it as a MORL environment. We then benchmark existing MORL algorithms on this task. Our results show that specialized water management methods outperform state-of-the-art MORL approaches, underscoring the scalability challenges MORL algorithms face in real-world scenarios.
Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating decisions, highlighting the potential of LLM-based multi-agent systems to automate and enhance pest management processes.
LU Zaiwang, ZHANG Yucheng, MA Yike, DAI Feng, DONG Jie, WANG Peng, LU Huixian, LI Tongbin, ZHAO Kaibin
[Significance] With the deepening of intelligent agriculture and precision agriculture, the agricultural production mode is gradually transforming from traditional manual experience based operations to a modern model driven by data, intelligent decision-making, and autonomous execution. In this context, improving agricultural operation efficiency and achieving large-scale continuous and seamless operation coverage have become key requirements for promoting the modernization of agriculture. The multi-robot full coverage operation technology, with its significant advantages in operation efficiency, system robustness, scalability, and resource utilization efficiency, provides practical and feasible intelligent solutions for key links such as sowing, plant protection, and harvesting in large-scale farmland. This technology, through the collaborative work of multi-robot systems, can not only effectively reduce the repetition rate of tasks and avoid omissions, but also achieve efficient and accurate continuous operations in complex and dynamic agricultural environments, greatly improving the automation and intelligence level of agricultural production. [Progress] Starting from the global perspective of systems engineering, an integrated closed-loop technology framework of "perception-decision-execution" is constructed. It systematically sorts out and deeply analyzes the technological development status and research methods of each key link in the full-coverage operations of agricultural multi robot. At the level of perception and recognition, it focus on exploring the application of multi-source information fusion and collaborative perception technology. By integrating multi-source sensor data, multi-level fusion of data level, feature level, and decision level is achieved, and a refined global environment model is constructed to provide accurate crop status, obstacle distribution, and terrain information for the robot system. Especially in the field of multi-robot collaborative perception, research has covered advanced models such as distributed simultaneous localization and mapping (SLAM) and ground to ground collaboration. Through information sharing and complementary perspectives, the system's perception ability and modeling accuracy in wide area, unstructured agricultural environments have been improved. At the decision-making and planning level, three key aspects are analyzed: task allocation, global path planning, and local path adjustment. Task allocation has evolved from traditional deterministic methods to market mechanisms, heuristic algorithms, and intelligent methods that integrate reinforcement learning and graph neural networks to address the challenges of dynamic and complex resource constraints in agricultural scenarios. The global path planning system analyzes the characteristics of geometric decomposition, grid method, global planning, and learning methods in terms of path redundancy, computational efficiency, and terrain adaptability. Local path planning emphasizes the combination of real-time perception in dynamic environments, using methods such as graph search, sampling optimization, model predictive control, and end-to-end reinforcement learning to achieve real-time obstacle avoidance and trajectory smoothing. At the control execution level, the focus is on model-based trajectory tracking and control technology, aiming to accurately convert planned paths into robot motion. Traditional control methods such as PID, LQR, sliding mode control, etc. are continuously optimized to cope with terrain undulations and system disturbances. In recent years, intelligent methods such as fuzzy control, neural network control, reinforcement learning, and multi machine collaborative strategies have been gradually applied, further improving the control accuracy and collaborative operation capability of the system in dynamic environments. [Conclusions and Prospects] The closed-loop technical framework is systematically constructed for agricultural multi-robot full coverage operations, and in-depth analysis of key modules is conducted, providing some understanding and suggestions, and providing theoretical references and technical paths for related research. However, the technology still faces many challenges, including perceptual uncertainty, dynamic changes in tasks, vast and irregular work areas, unpredictable dynamic obstacles, communication and collaboration barriers, and energy endurance issues. In the future, this field will further strengthen the integration with artificial intelligence, the Internet of Things, edge computing and other technologies, focusing on promoting the following directions, including the development of intelligent dynamic task allocation mechanism; optimize global and local path planning algorithms to enhance their efficiency and adaptability in large-scale complex scenarios; enhance the real-time perception and response capability of the system to dynamic environments; promote software hardware collaboration and intelligent system integration to achieve efficient communication and integrated task management; develop high-efficiency power systems and intelligent energy consumption strategies to ensure long-term continuous operation capability. Through these efforts, agricultural multi-robot systems will gradually achieve higher levels of precision, automation, and intelligence, providing key technological support for the transformation of modern agriculture.
Alexander M. Gaffke, Daijiang Li, Veronica Manrique
et al.
Classical weed biological control is a major management tool deployed worldwide for the control of invasive plants. Classical weed biological control has a long-standing history of safe and effective weed management that has resulted in the protection of many ecosystems. Despite this history of safe and effective control, significant public opposition can occur. Plans to release biological control agents developed for the invasive tree Chinese tallow, Triadica sebifera (L.) Small received widespread and enthusiastic support from land managers and the environmental community. However, agent release was opposed by beekeeping organizations. Chinese tallow is purported to be an important nectar plant for beekeeping operations in the southeastern U.S.A. In this article, we discuss the primary concerns raised by commercial beekeepers opposed to Chinese tallow management with biological control and present data on the flowering phenology of tallow. Review of the scientific literature identified multiple studies reporting the importance of native plants to honey production, highlighting the need to protect important pollen and nectar sources from displacement by Chinese tallow. Additionally, results indicate shorter bloom periods for tallow than previously reported. These results highlight the importance of reducing the tallow invasion and protecting native ecosystems to enhance floral diversity. The implementation of a biological control program for Chinese tallow may be the best option for land managers and beekeepers at conserving native ecosystems of the southeastern U.S.A. while maintaining the services they provide.
Abstract Sustainable supply chain management (SSCM) plays a great part in enterprise production operation management due to strict government regulations and increased public awareness. Selecting the best sustainable supplier is essential for companies to promote SSCM, which, as indicated in many studies, is a multi-criteria decision making (MCDM) problem. Besides, decision makers tend to utilize linguistic terms for expressing their evaluations owing to their fuzzy knowledge. This paper reports an innovative MCDM model for sustainable supplier selection by integrating best-worst method (BWM) and alternative queuing method (AQM) within the interval-valued intuitionistic uncertain linguistic setting. The novel approach allows to capture the uncertainty and vagueness of decision makers’ judgements with the aid of interval-valued intuitionistic uncertain linguistic sets. Furthermore, the BWM method can obtain the optimal weights of criteria by constructing a nonlinear programing model. The AQM is reliable and intuitive to generate the ranking of candidate suppliers. Finally, a watch manufacturer is used as an example for illustrating the practicability and effectiveness of the proposed sustainable supplier selection model.
The microbial fuel cell has been considered a promising alternative to traditional fossil energy. It has great potential in energy production, waste management, and biomass valorization. However, it has several technical issues, such as low power generation efficiency and operational stability. These issues limit the scale-up and commercialization of MFC systems. This review presents the latest progress in microbial community selection and genetic engineering techniques for enhancing microbial electricity production. The summary of substrate selection covers defined substrates and some inexpensive complex substrates, such as wastewater and lignocellulosic biomass materials. In addition, it also includes electrode modification, electron transfer mediator selection, and optimization of operating conditions. The applications of MFC systems introduced in this review involve wastewater treatment, production of value-added products, and biosensors. This review focuses on the crucial process of microbial fuel cells from preparation to application and provides an outlook for their future development.
Sarah K Schaumann, Bublu Thakur-Weigold, Luk N Van Wassenhove
This position paper reframes the ongoing relevance versus rigor debate in operations research (OR) as a Kuhnian epistemological crisis, in which the dominant paradigm of quantitative modeling shows signs of exhaustion. Humanitarian fleet management is presented as an empirical case of extensive operations theory, which has not been implemented by the stakeholders who paid for its production. We propose a possible way out of the crisis by combining “hard” and “soft” OR, illustrating the potential with a selected problem structuring method. Optimization solutions can become more productive by first surfacing the organizational context of decision-making. The illustration emphasizes that hard and soft OR are not binary opposites but interlocking, mutually empowering components which expand the evidence base. Shifting the current paradigm toward more engaged scholarship could counteract the ongoing theoretical drift, for more strategic impact on the pressing problems of today.
Mazen Mohamad, Jan-Philipp Steghöfer, Eric Knauss
et al.
With the increasing prevalence of open and connected products, cybersecurity has become a serious issue in safety-critical domains such as the automotive industry. As a result, regulatory bodies have become more stringent in their requirements for cybersecurity, necessitating security assurance for products developed in these domains. In response, companies have implemented new or modified processes to incorporate security into their product development lifecycle, resulting in a large amount of evidence being created to support claims about the achievement of a certain level of security. However, managing evidence is not a trivial task, particularly for complex products and systems. This paper presents a qualitative interview study conducted in six companies on the maturity of managing security evidence in safety-critical organizations. We find that the current maturity of managing security evidence is insufficient for the increasing requirements set by certification authorities and standardization bodies. Organisations currently fail to identify relevant artifacts as security evidence and manage this evidence on an organizational level. One part of the reason are educational gaps, the other a lack of processes. The impact of AI on the management of security evidence is still an open question