Search Results (44)

This research investigates the strategic interplay between pricing, delivery promises, and handling strategies for service-level returns—products returned by consumers due to operational issues like late delivery rather than product defects. In a vertical decentralized supply chain with a manufacturer and an e-tailer, a shorter promised delivery lead time (PDL) attracts more customers but also increases the risk of late delivery, making products more return-prone. Modeling the return rate as an endogenous variable dependent on the e-tailer’s PDL decision, we develop a Manufacturer-Stackelberg (MS) game-theoretic model to examine whether service-level returns should be handled by the manufacturer (Buy-Back strategy) or the e-tailer (No-Returns strategy). The results suggest that the optimal handling strategy depends on the e-tailer’s reselling ratio—a measure of its efficiency in extracting value from returns. A win-win situation is achieved when the reselling ratio is smaller than a threshold, as the manufacturer’s decision to buy back these returns also benefits the e-tailer. Surprisingly, when the manufacturer leaves the e-tailer to handle FFRs, a higher reselling ratio is not necessarily profitable for the e-tailer. Extending the analysis to a retailer-Stackelberg (RS) scenario reveals that the supply chain’s power structure is a fundamental determinant of the optimal returns handling strategy, shifting the equilibrium from a counterintuitive, power-distorted outcome in a MS system to an intuitive, profit-driven one in a RS system. Full article

Against the backdrop of escalating atmospheric carbon dioxide concentrations, carbon emission trading systems (ETS) have emerged as pivotal policy instruments, with China’s ETS playing a prominent role globally. The carbon price, central to ETS functionality, guides resource allocation and corporate strategies. Due to unexpected events, political conflicts, limited access to data information, and insufficient cognitive levels of market participants, there are epistemic uncertainties in the fluctuations of carbon and energy prices. Existing studies often lack effective handling of these epistemic uncertainties in energy prices and carbon prices. Therefore, the core objective of this study is to reveal the dynamic linkage patterns between energy prices and carbon prices, and to quantify the impact mechanism of epistemic uncertainties on their relationship with the help of uncertain differential equations. Methodologically, a dynamic model of carbon and energy prices was constructed, and analytical solutions were derived and their mathematical properties were analyzed to characterize the linkage between carbon and energy prices. Furthermore, based on the observation data of coal prices in Qinhuangdao Port and national carbon prices, the unknown parameters of the proposed model were estimated, and uncertain hypothesis tests were conducted to verify the rationality of the proposed model. Results showed that the mean squared error of the established model for fitting the linkage relationship between carbon and energy prices was 0.76, with the fitting error controlled within $3.72\%$. Moreover, the prediction error was 1.$88\%$. Meanwhile, the $5\%$ value at risk (VaR) of the logarithmic return rate of carbon prices was predicted to be $-0.0369$. The research indicates that this methodology provides a feasible framework for capturing the uncertain interactions in the carbon-energy market. The price linkage mechanism revealed by it helps market participants optimize their risk management strategies and provides more accurate decision-making references for policymakers. Full article

The strategic allocation of advertising billboards has become a critical aspect of urban planning and resource management. While previous studies have explored site selection based on road network and population data, they have often overlooked the diminishing marginal returns of overlapping coverage and neglected to efficiently process large-scale urban datasets. To address these challenges, this study proposes two complementary optimization methods: an enhanced greedy algorithm based on geometric modeling and spatial acceleration techniques, and a reinforcement learning approach using Proximal Policy Optimization (PPO). The enhanced greedy algorithm incorporates population-weighted road coverage modeling, employs a geometric series to capture diminishing returns from overlapping coverage, and integrates spatial indexing and parallel computing to significantly improve scalability and solution quality in large urban networks. Meanwhile, the PPO-based method models billboard site selection as a sequential decision-making process in a dynamic environment, where agents adaptively learn optimal deployment strategies through reward signals, balancing coverage gains and redundancy penalties and effectively handling complex multi-step optimization tasks. Experiments conducted on Wuhan’s road network demonstrate that both methods effectively optimize population-weighted billboard coverage under budget constraints while enhancing spatial distribution balance. Quantitatively, the enhanced greedy algorithm improves coverage effectiveness by 18.6% compared to the baseline, while the PPO-based method further improves it by 4.3% with enhanced spatial equity. The proposed framework provides a robust and scalable decision-support tool for urban advertising infrastructure planning and resource allocation. Full article

This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual dynamic programming (SDDP) algorithms. Our method optimizes residential solar power generation and battery storage systems, reducing costs through strategic charging and discharging patterns. Using stylized test data, we evaluate battery storage optimization strategies by comparing various SDDP model configurations against a linear programming (LP) benchmark model. The SDDP optimization framework demonstrates robust performance in battery operation management, efficiently handling diverse pricing scenarios while maintaining computational efficiency. Our analysis reveals that the SDDP model achieves positive financial returns with small-scale battery installations, even in scenarios with limited photovoltaic generation capacity. The results confirm both the economic viability and environmental benefits of residential solar–battery systems through two key strategies: aligning battery charging with renewable energy availability and shifting energy consumption away from peak periods. The SDDP framework proves effective in managing battery operations across dynamic pricing scenarios, achieving performance comparable to LP methods while handling uncertainties in PV generation, consumption, and pricing. Full article

Organizational performance defines how well an organization achieves its goals and objectives. To fulfill these, the organization should improve its logistical competencies including delivery speed, order accuracy, and returns handling. At the same time, social media plays an important role. Therefore, the main objective of this study is to examine and research the influence of logistical competence on the performance of small and medium enterprises (SMEs) with the moderating effect of the competitive advantages of social media. We used a quantitative, descriptive, cause–effect, and cross-sectional approach to actualize this research. A non-probability convenience sampling method was used as it is cost-effective, practical, easy to access, and time-efficient. The main variables, such as delivery speed, order accuracy, and returns handling, were analyzed to determine their influence on organizational performance. A total of 163 respondents participated, ranging from middle to top management employees in SMEs, specifically in Jordan, who completed a structured Google form. Simple, multiple, and hierarchical regression were used to check the hypotheses in this research. The conclusion shows that logistical competence positively affects organizational performance, with competitive advantages in social media campaigns enhancing this effect significantly; this was evident as social media campaigns emerged as an essential platform for marketing logistical strengths and boosting customer engagement. This study and research give recommendations for SMEs to integrate logistics and E-marketing strategies properly. Regarding the study limitations, we see that the regional focus and the small sample size are acknowledged. In the future, research is highly encouraged which looks into industry-specific dynamics, advancing technologies, and cross-cultural contexts. This research bridges the gap between logistics and marketing, thus showcasing a framework promoting logistical competence to gain a competitive advantage in the SME market. Full article

A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. By integrating perturbation-based augmentation, backward return estimation, and policy-stabilization mechanisms, the framework facilitates robust modeling and optimization of complex, dynamic behavior sequences. Experimental evaluations on four distinct behavior data subsets indicate that the proposed method achieved consistent performance improvements over representative baseline models across key metrics, including total profit gain, average reward, policy stability, and profit–price correlation. On the sales feedback dataset, the framework achieved a total profit gain of 0.37, an average reward of 4.85, a low-action standard deviation of 0.37, and a correlation score of $R^2=0.91$. In the overall benchmark comparison, the model attained a precision of 0.92 and a recall of 0.89, reflecting reliable strategy response and predictive consistency. These results suggest that the proposed method is capable of effectively handling decision-making scenarios involving sparse feedback, heterogeneous behavior, and temporal volatility, with demonstrable generalization potential and practical relevance. Full article

Portfolio management, a critical application of financial market analysis, involves optimising asset allocation to maximise returns while minimising risk. This paper addresses the notable research gap in analysing historical financial data for portfolio optimisation purposes. Particularly, this research examines different approaches for handling missing values and volatility, while examining their effects on optimal portfolios. For this portfolio optimisation task, this study employs a metaheuristic approach through the Swarm Intelligence algorithm, particularly Particle Swarm Optimisation and its variants. Additionally, it aims to enhance portfolio diversity for risk minimisation by dynamically clustering and selecting appropriate assets using the proposed strategies. This entire investigation focuses on improving risk-adjusted return metrics, like Sharpe, Adjusted Sharpe, and Sortino ratios, for single-asset-class portfolios over two distinct classes of assets, cryptocurrencies and stocks. Considering relatively high market activity during pre, during and post-pandemic conditions, experiments utilise historical data spanning from 2015 to 2023. The results indicate that Sharpe ratios of portfolios across both asset classes are maximised by employing linear interpolation for missing value imputation and exponential moving average smoothing with a lower smoothing factor ($\alpha$). Furthermore, incorporating assets from different clusters significantly improves risk-adjusted returns of portfolios compared to when portfolios are restricted to high market capitalisation assets. Full article

Advancements in transport engineering and technology play a crucial role in improving multimodal transport systems and optimizing logistics operations. This study focuses on efficient material flow management in an industrial enterprise, directly supporting the goals of sustainable transport and innovative logistics strategies. The manufacturing plant in Veselí nad Lužnicí was selected as a case study because of the identified inefficiencies in its logistics processes and the availability of detailed operational data, allowing for an accurate analysis of material flows. The research identifies weaknesses in the current material flow and proposes the following two optimization solutions: replacing an external operator for semi-finished goods transport with in-house logistics and substituting external transport providers for finished goods transportation with an internally managed fleet. The proposed methodology introduces a novel integration of analytical tools, including checkerboard table analysis, cost modeling, and return-on-investment (ROI) assessment, to evaluate logistics efficiency and minimize material handling costs. This study demonstrates how optimized material flows, particularly using railway logistics, can contribute to cost-effective and sustainable supply chains. The research reflects current trends in transport system planning, emphasizing transport modeling, digital twin simulations, and smart railway technologies to enhance operational efficiency and resilience. The results provide practical recommendations for companies seeking to integrate rail transport into their logistics processes, contributing to broader objectives of environmental sustainability and digital transformation in the transport sector. Full article

With the rapid advancement of edge-computing technology, more computing tasks are moving from traditional cloud platforms to edge nodes. This shift imposes challenges on efficiently handling the substantial data generated at the edge, especially in extreme scenarios, where conventional data collection methods face limitations. UAVs have emerged as a promising solution for overcoming these challenges by facilitating data collection and transmission in various environments. However, existing UAV trajectory optimization algorithms often overlook the critical factor of the battery capacity, leading to potential mission failures or safety risks. In this paper, we propose a trajectory planning approach Hyperion that incorporates charging considerations and employs a greedy strategy for decision-making to optimize the trajectory length and energy consumption. By ensuring the UAV’s ability to return to the charging station after data collection, our method enhances task reliability and UAV adaptability in complex environments. Full article

The successful separation of a single sheet from a stack of paper is considered a paper-handling goal when using a robot hand. Under the condition of uncertain friction coefficients, a stochastic algorithm introducing randomness is formulated, which converges a paper stack to a state of single-sheet separation through the repetition of simple robot operations. This formulation is based on the proposed motion strategy for a robotic hand, which introduces a state of a partially separated paper bundle to temporarily allow the simultaneous separation of multiple sheets and a return operation to return the paper to the original paper bundle. The experimental results indicate that a single sheet can be completely separated from a vertically standing stack of business-card-sized papers by shifting the paper in a high-speed translational movement using two fingers of the robot hand that grasp the paper from both sides. Full article

This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the test of availability of a core solution that satisfies all associated parties/players; (ii) the measurement of players’/parties’ rational minimal and maximal return expectations from the grand coalition regarding their all individual and sub-group strategies and associated return rationalities; (iii) the determination of the critical players/parties in the grand coalition. The study’s main contributions are the provision of a methodology that identifies spatially/geographically critical players/parties and the design of an algorithm for handling exponentially growing strategies alongside increasing numbers of players/parties. In sum, a comprehensive cooperative game theory framework is introduced to measure the significance of location and neighborhood relations in conjunction with the magnitude of the players/parties. The case of the EU has revealed the union’s geographically critical countries, with Germany being found to be the most influential. Full article

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions. Full article

Buildings account for over 40% of global carbon dioxide (CO₂) emissions, with supply and return fans in air handling units consuming a significant portion of energy. To address this, researchers have explored innovative economizer damper control methods and identified the “split-signal” strategy, which optimizes supply airflow using a single damper as a promising approach. In this study, split-signal was further refined for practical application and energy simulation, aiming to demonstrate its effectiveness and encourage adoption in real-world building mechanical systems. Laboratory testing on chilled water variable air volume (VAV) system showed fan energy savings of 0.2–5% compared to traditional “three-coupled” control, depending on ventilation air proportions, and prevented reverse airflow. A statistical regression model, based on experimental data, was developed to predict energy savings and streamline comparisons. Energy simulations were conducted across various U.S. climate zones and revealed potential savings of 15–20% in energy use, operational costs, and CO₂ emissions. With minimal financial investment, split-signal control offers a cost-effective solution to improve energy efficiency and reduce environmental impact, promoting its adoption in real-world building applications. Full article

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time. Full article

Taiwan’s encouragement of installing solar photovoltaic power plants (SPPPs) on warehouse rooftops is a step towards sustainability and profitable investment. This study, analyzing the installations of STY Company, found that rooftop SPPPs significantly boost revenue, with rates increasing from 2.0088% to 6.8681% over 20 years. The break-even point is in the 7th year, with a return rate ranging from 2.0088 to 2.1748%. This shows that SPPP investments are a benefit for investors, shortening construction times and allowing warehouses to sell solar energy at an earlier date. This research utilized a fuzzy nonlinear multi-objective programming model to examine trade-offs between construction time, cost, quality, and revenue (TCQR) to optimize SPPP construction. The findings suggest that reducing construction time is an effective strategy to lower carbon emissions despite potential cost increases. However, time and quality costs are inversely proportional, highlighting the importance of efficient project management in minimizing the impacts of this trade-off. Adjusting funding can maintain quality while speeding up construction. Completing projects early also heightens revenue from green energy sales, offsetting higher initial investments. The TCQR focuses on investment revenue, managing time efficiently, and making data-driven decisions to expedite SPPP development. This model improves project profitability and promotes sustainable growth by reducing construction time and optimizing financial strategies. This study’s contribution includes: 1. Optimizing the installation process of warehouse rooftop SPPPs, which provide significant long-term revenue and environmental benefits. 2. Combining the different research methods of scholars into fuzzy methods that can solve complex systems with high uncertainty. The nonlinear model put forth by this study is closer to the actual situation and can handle balancing complex problems in multi-objective programming. 3. Improving the efficiency of time management to make it feasible to reduce construction time to lower carbon emissions. 4. Concocting a comprehensive approach integrating financial, environmental, and operational factors for successful SPPP development. This study addresses an academic gap. Previously, scholars conducted research independently, focusing solely on financial investment or time, cost, and quality (TCQ) issues without considering the two together. By combining financial investment with TCQ, this study fills a significant gap in academic research. According to this study, better investment returns could improve the promotion of solar energy. Unlike previous research, this study integrates the analysis of TCQ with that of revenue by assessing costs and revenues together. This approach allows decision-makers to derive judgments from the TCQR model quickly. Full article