Search Results (88)

The determination of freight rates in liner shipping is influenced by the market dynamics and the strategic decisions of shipping alliances. This study investigates the effect of non-alliance tonnage on freight rates along the Asia–North America West Coast route, employing a quantile regression method. A dataset covering July 2021 to June 2023 was used, with bunker prices and the Dow Jones Index serving as control variables. The results reveal that the non-alliance share has a significant and negative impact on lower quantiles, suggesting that enhanced competition reduces freight rates when the prices are low. In contrast, this effect disappears at higher freight levels. Bunker prices and the stock market index also exhibit varying effects, depending on the quantile, with demand-side variables being more influential during low-freight conditions. These findings suggest that market concentration affects price-setting power, and quantile-based approaches offer deeper insights into these complex relationships than linear models. These insights contribute to the sustainable development of maritime transport by promoting fair competition, improving pricing transparency, and supporting efficient policy interventions in global liner shipping. Full article

This model extends the classical economic production quantity (EPQ) model to address the complexities within a two-echelon supply chain system. The model integrates the cost of raw materials necessary for production and takes into account the presence of imperfect quality items within the acquired raw materials. Upon receipt of the raw material, a thorough screening process is conducted to identify imperfect quality items. Combining imperfect raw material and the concept of shifting production rate, two different inventory models for deteriorating products are formulated under imperfect production with demand dependent on the stock level. In the first model, the imperfect raw materials are sold at a discounted price at the end of the screening period, whereas in the second one, imperfect items are kept in stock until the end of the inventory cycle and then returned to the supplier. Numerical analysis reveals that selling imperfect raw materials yields a favourable financial outcome, with an optimal inventory level $I_1$ = 11,774 units, optimal cycle time $T=2140$ h, and a total profit per hour of USD 183, while keeping the imperfect raw materials to return them to the supplier results in a negative profit of USD $-4.44\times {10}^3$ per hour, indicating an unfavourable financial outcome with the optimal inventory level $I_1$ and optimal cycle time T of 26,349 units and 4702.6 h, respectively. The findings show the importance of selling imperfect raw materials rather than returning them and provide valuable insights for inventory management in systems with deteriorating products and imperfect production processes. Sensitivity analysis further demonstrates the robustness of the model. This study contributes to satisfying the need for inventory models that consider both the procurement of imperfect raw materials, stock-dependent demand, and deteriorating products, along with shifts in production rates in a multi-echelon supply chain. Full article

Effective investment decision-making in today’s volatile financial market demands the integration of advanced predictive analytics, alternative data sources, and behavioral insights. This paper introduces an innovative Internet of Behaviors (IoB) ecosystem that integrates real-time data acquisition, advanced feature engineering, predictive modeling, explainability, automated portfolio management, and an intelligent decision support engine to enhance financial decision-making. Our framework effectively captures complex temporal dependencies in financial data by combining robust technical indicators and sentiment-driven metrics—derived from BERT-based sentiment analysis—with a multi-layer LSTM forecasting model. To enhance the model’s performance and transparency and foster user trust, we apply XAI methods, namely, TimeSHAP and TIME. The IoB ecosystem also proposes a portfolio management engine that translates the predictions into actionable strategies and a continuous feedback loop, enabling the system to adapt and refine its strategy in real time. Empirical evaluations demonstrate the effectiveness of our approach: the LSTM forecasting model achieved an RMSE of 0.0312, an MAE of 0.0250, an MSE of 0.0010, and a directional accuracy of 95.24% on TSLA stock returns. Furthermore, the portfolio management algorithm successfully transformed an initial balance of USD 15,000 into a final portfolio value of USD 21,824.12, yielding a net profit of USD 6824.12. These results highlight the potential of IoB-driven methodologies to revolutionize financial services by enabling more personalized, transparent, and adaptive investment solutions. Full article

Reducing the energy consumption of the existing building stock is of paramount importance in the race to reach national and international climate goals. While multiple initiatives are in place and provide guidance, heritage-protected buildings are often not part of the equation. Protected buildings make up a large share of the existing building stock and therefore offer large savings potential. In Trondheim, Norway, alone, that share is close to 10%, which demands the establishment of representative retrofitting cases. A case study of the central buildings on the NTNU campus was established to specifically test passive retrofitting measures, which are greatly affected by heritage protection. The application of measures selected in collaboration with heritage authorities led to overall energy savings of 16% to 18%, while the energy for heating alone was reduced by 34% to 40%. The reductions were especially prominent during cold winter months, where overall consumption peaks were reduced by up to 37%, greatly decreasing the dependence on cold outdoor temperatures. The results make a case for the application of passive retrofitting measures to heritage-protected buildings despite them not reaching deep energy retrofitting goals, especially in cold climates and alongside other energy-saving or -producing measures. Full article

Given the increasing demand for higher construction productivity and better thermal resistance, adopting innovative building envelope systems is crucial. Dry-stack masonry blocks have emerged as a viable solution, due to their rapid assembly, mortar-free construction, and reduced dependence on skilled labor. However, there is a lack of scientific evaluation on the thermal performance of dry-stack blocks for cold climate zones and corresponding proper designs. This study addresses this gap by investigating market-available blocks and proposing two innovative block designs—a composite block and a simple block—highlighting their thermal performance and associated challenges. Using finite element modelling, the thermal resistance of these blocks was carefully assessed and compared. The results show that thermal bridging, induced by masonry ties penetrating the insulation, significantly impacts the thermal resistance of the wall made with simple blocks, resulting in an 11% decrease in the effective thermal resistance (R-value) as compared to the composite block walls. Furthermore, compared to a conventional masonry wall with the same insulation thickness, the composite-block wall exhibits a 24% higher R-value. The composite block outperforms existing market options in terms of thermal resistance, making it a superior choice for cold climate regions. Conversely, the simple block is preferred when sophisticated manufacturing facilities are unavailable. Overall, the composite block wall’s improved thermal resistance can make a meaningful contribution to lowering operational energy demand (i.e., operational carbon), contributing to the shift to a sustainable building stock. Full article

Sustainability is essential in addressing the environmental impacts of supply chains, a significant source of global emissions. This study develops an inventory model to optimize retailer profit by integrating joint pricing, environmental investment, ordering costs, preservation technology, and replenishment timing for non-instantaneously decaying items. Demand depends on stock and selling price, while an algorithm optimizes variables such as selling price, preservation investment, emission costs, ordering costs, and replenishment cycles. The dragonfly algorithm (DA) is employed to find optimal solutions, with numerical analysis demonstrating the model’s application. To justify the results, we have used an updated version of the dragonfly algorithm. Managerial insights highlight the practical relevance of the proposed framework. Full article

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing. Full article

This paper formulates six inventory models for products with instantaneous deterioration, focusing on the impacts of full and partial advance payment structures. The demand function depends on both price and stock levels and accounts for shortages through full backlogging. The primary objective is to determine the optimal payment policy under varying trade facilities, analyzing six distinct payment scenarios commonly employed in business practice. Each model is presented with closed-form solutions and supported by mathematical formulations. For each case, algorithms and mathematical proofs are developed to determine the optimal cycle duration and corresponding unit cost. Numerical examples and 2D graphical representations generated using MATLAB are included to validate the proposed models. Additionally, a sensitivity analysis is conducted to examine the effects of each payment policy and parameter variation, providing key managerial insights into payment planning in inventory management. Full article

This study examines the soil organic carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture, and different drainage and slope categories, this research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC’s global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: (1) the SOC stock baseline map (2010) by JRC, (2) and the SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization—DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. This study unveils the influence of soil order, weaving a tapestry of SOC variability. Factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021, the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights. More specifically, understanding SOC dynamics depends on multiple factors, including soil texture, pH, and landscape characteristics like slope. These variables collectively influence SOC retention, stabilization, and loss rates, highlighting the need for an integrated approach to studying SOC behavior across different environments. These findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges. Full article

Reducing the energy demand in the housing sector is one of the current topics in the EU. Slovakia, as an EU member, is also trying to lower the dependence on the import of energy raw materials used for heating. While new buildings reflect the technical requirements of applicable standards, buildings built in the past usually do not meet any technical requirements. The basis of efficient operation is not only satisfactory building structures, but also technological equipment of the buildings. The heating system is often in an unsatisfactory state, and an outdated heat source disproportionately reduces the overall efficiency of energy conversion. Complex restoration is, therefore, in most cases, necessary and often financially costly. The presented article analyzes the current state of housing stock in the example of a selected city district. In the next step, the current state and energy consumption are identified. Subsequently, needed retrofit measures are identified and the possibilities of renewal are analyzed. The use of RES in buildings is proposed, while selected city districts could create an independent energy community. The main goal of this article is to show the necessary steps to achieve efficient energy use and, using the example of a Zilina City district, show the possible benefits of such community creation in Slovakia. The article also discusses the correlation between the number of sunny days and possible energy generation in winter months. Full article

Crustacean fisheries are gaining prominence globally amid a decline in finfish stocks. Some decapod crustacean species have experienced increased landings in response to shifting market demands and environmental dynamics. Notably, the caramote prawn (Penaeus kerathurus—Forskål, 1775) in the northern Adriatic Sea, Geographical Sub Area (GSA) 17, has risen in both landings and economic importance in recent years. However, despite its significance, comprehensive information on fishery-dependent data, age, and growth in this region remains lacking. To address this gap, this study employs modal progression analysis and the ELEFAN approach, utilizing the “TropFishR” R package and newly developed functions, including bootstrapping procedures. These advancements aim to overcome issues identified in previous versions and enhance the accuracy and reliability of age and growth estimations. The study leverages one year of monthly length-frequency distributions (LFDs) collected from commercial bottom trawls in the northern Adriatic Sea. The results of the analysis confirm the presence of sexual dimorphism in the caramote prawn species, with females exhibiting faster growth rates compared to males. Additionally, the growth performance index supports this observation, further underscoring the importance of accounting for sexual dimorphism in growth modeling and fisheries management strategies. By contributing to a growing body of knowledge on the growth dynamics of the caramote prawn, this study provides valuable insights for sustainable fisheries management in the northern Adriatic Sea. Understanding the age and growth patterns of key crustacean species is essential for developing effective conservation measures and ensuring the long-term health and productivity of marine ecosystems. The findings of this study serve as a foundation for informed decision-making and proactive management practices aimed at preserving the ecological integrity and economic viability of crustacean fisheries in the region. Full article

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control. Full article

Environmental responsibility and economic benefits have promoted the development of closed-loop supply chains (CLSCs), and shortages and channels are considered to be two important issues in a CLSC. This paper explores the ordering and channel decisions in a CLSC with new and differentiated remanufactured products; considers the price and sales-effort-dependent demands, as well as the proportion of emergency orders determined by emergency order costs and backorder losses; and establishes integrated and decentralized CLSC game models. We introduce a stochastic sales effort, which affects two types of products. The numerical results show that sales effort and the order quantity of new and remanufactured products exhibit concave and convex functions, respectively. The upper limit of sales effort has a greater impact on supply chain decisions. High sales efforts can serve as a means of coordinating dispersed supply chains. Moreover, in different cases, the decisions of an integrated channel are better than those of a decentralized channel. Finally, whether the supply chain adopts an emergency order strategy depends on the relative cost of emergency orders and out-of-stock costs. According to this research, some management insights are also provided. Full article

This study systematically investigates the pivotal role of inventory management within the framework of “cloud supply chain” operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a “cloud supply chain” through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain. Full article

Background: Neutrosophic logic explicitly quantifies indeterminacy while also maintaining the independence of truth, indeterminacy, and falsity membership functions. This characteristic assumes an imperative part in circumstances, where dealing with contradictory or insufficient data is a necessity. The exploration of differential equations within the context of uncertainty has emerged as an evolving area of research. Methods: the solvability conditions for the first-order linear neutrosophic differential equation are proposed in this study. This study also demonstrates both the existence and uniqueness of a solution to the neutrosophic differential equation, followed by a concise expression of the solution using generalized neutrosophic derivative. As an application of the first-order neutrosophic differential equation, we discussed an economic lot sizing model in a neutrosophic environment. Results: This study finds the conditions for the existing solution of a first-order neutrosophic differential equation. Through the numerical simulation, this study also finds that the neutrosophic differential equation approach is much better for handling uncertainty involved in inventory control problems. Conclusions: This article serves as an introductory exploration of differential equation principles and their application within a neutrosophic environment. This approach can be used in any operation research or decision-making scenarios to remove uncertainty and attain better outcomes. Full article