Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involved. This study investigates the use of cost-sharing contracts by manufacturers to promote marketing in both online and offline channels, with the goal of achieving Pareto improvements in supply chain profitability. The model also accounts for consumers’ reference quality perceptions in online channels, offering a comprehensive evaluation of how cost-sharing contracts influence the operational strategies and performance of both online and offline enterprises. An empirical analysis is conducted using the “US Stores Sales” dataset from Kaggle, comprising 4,249 samples with 20 recorded characteristics per sample. The findings indicate that: (1) Cost-sharing in marketing efforts facilitates a Pareto improvement in profits for manufacturers, online enterprises, and offline retailers, with manufacturers experiencing the most significant benefit. (2) When the manufacturer assumes a larger share of marketing costs for one channel (e.g., online or offline) and a smaller share for the other, the party receiving the higher cost-sharing proportion typically sees increased profitability, while the other party’s profitability may diminish. (3) Empirical analysis suggests that manufacturers should prioritise supporting online businesses’ marketing activities, as this strategy is more likely to result in higher overall profits for the manufacturer. (4) Interestingly, when equal cost-sharing proportions are offered to both online and offline enterprises for the sake of fairness, the manufacturer’s profitability is enhanced. Moreover, the profitability of online enterprises tends to increase when the equal cost-sharing proportion is smaller. These findings validate the proposed model and underscore the critical role of strategic cost-sharing contracts in optimising Online to Offline (O2O) supply chain performance. Further research could explore the implications of varying consumer preferences and digitalisation trends on the effectiveness of such strategies.
A detailed investigation into the axial bearing load of the revolving platform in a hydraulic excavator equipped with a shovel attachment was presented in this study. A mathematical model was formulated to assess the forces acting on the bearing under various operational conditions. The analysis focuses on a 100,000 kg excavator with a 6.5 m³ bucket, examining the contributions of kinematic chains and drive mechanisms to axial loads. Simulations of multiple positions within the working range were carried out, calculating the load spectrum, including boundary resistance, to ensure machine stability. An optimization program was developed to refine the bearing selection process by identifying equivalent loads and moments. These calculations were benchmarked against manufacturer capacity diagrams, allowing for precise selection of appropriate bearing sizes. The findings underscore the critical role of accurate load calculations in enhancing the performance, reliability, and design optimization of hydraulic excavators. This approach provides engineers with a framework for selecting bearings that can withstand complex operational stresses, thereby improving the efficiency and longevity of hydraulic machinery.
The current research is to profit from the science of enterprise architecture (Enterprise Architecture) and its application in building the structure of government sector institutions in the Kingdom of Saudi Arabia, in accordance with the Kingdom of Saudi Arabia 2030 vision. while emphasizing the value of enterprise architecture (EA) and the need for knowledge to apply its models and procedures while creating its structures. The research study's scope is determined by how well the descriptive and analytical approaches function together, and this is achieved by choosing a few government sector organizations to focus on. Throughout exploring the possibility of applying the Enterprise Architecture model, as an application case based on the extent of knowledge of the cadres of those entities with the organizations' enterprise architecture, and the presence of supervisory expertise. By relying on the quantitative method of studying and analyzing the situation by conducting a questionnaire on some workers in those bodies under consideration (Research Sample), studying the possibility and feasibility of applying enterprise architecture for organizations and generalizing this in the restructuring of government sector’s institutions in general.
This study evaluates the safety management system at Xuefu Gas Station in Xiangtan City of China through a combination of Preliminary Hazard Analysis (PHA) and Fault Tree Analysis (FTA). Initially, PHA was employed to identify potential hazards and assess the probability of associated accidents. This analysis led to the formulation of preventive measures aimed at mitigating identified risks. Subsequently, FTA was utilized to construct a logical framework for analyzing the various causes of system failures and their interdependencies. The analysis revealed deficiencies in the management system, equipment, ignition sources, and human factors. An approximate calculation method was applied to rank the structural importance of these factors, thereby highlighting key areas of impact. Based on these findings, targeted recommendations were proposed to enhance the safety management practices at the gas station, thereby reducing accident likelihood and safeguarding personnel and property. The results underscore the necessity of improving management practices, upgrading equipment, controlling ignition sources, and bolstering human factors to achieve a comprehensive safety management system.
The selection of appropriate anti-drone systems is critical for enhancing a military's defensive capabilities. With a range of non-kinetic anti-drone guns available, it is essential to identify the optimal system that meets specific military requirements. This study presents a comprehensive approach, combining Multiple Criteria Decision Making (MCDM) techniques to facilitate this selection process. The Defining Interrelationships Between Ranked Criteria II (DIBR II) method has been employed to determine and calculate the criteria weighting coefficients, while the Grey Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified to utilize interval grey numbers, has been applied to rank the alternatives. The criteria weighting coefficients, defined by expert input, are aggregated using the Bonferroni mean. The proposed DIBR II-Grey MARCOS model is then subjected to a sensitivity analysis, which further validates the robustness of the selection process. A comparative analysis of results, based on the applied MCDM methods, underscores the efficacy of the proposed model. The findings demonstrate that this integrated model not only provides a reliable framework for selecting anti-drone guns but also offers a versatile tool for resolving other MCDM challenges across various domains. The study highlights the potential of this model for broader application in diverse operational environments, where complex decision-making is required. The combination of MCDM techniques and sensitivity analysis offers valuable insights into optimizing resource allocation, thereby enhancing strategic decision-making processes. The proposed model's adaptability and effectiveness suggest its significant potential for adoption beyond the military sector.
To address the challenges in traditional Failure Mode and Effects Analysis (FMEA) related to determining factor weights, identifying risk priority of failure modes, and managing uncertainties in the risk assessment process, this paper proposes an enhanced FMEA risk factor evaluation method. This method integrates incomplete and imprecise expert assessments using a fuzzy multi-criteria compromise ranking technique called the “V1seKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR). By employing Fuzzy Evidence Reasoning (FER), the risk factor ratings are represented using fuzzy belief structures to capture their diversity and uncertainty. Objective weights are adjusted using Shannon entropy to correct subjective weights, and the VIKOR technique is applied to prioritize failure modes based on the principles of minimizing individual regret and maximizing group utility. The improved model is applied to identify key equipment associated with oil and gas leakage risk in the Floating Production Storage and Offloading (FPSO) system. Validity and sensitivity analysis confirm the robustness and reliability of the method, enhancing the accuracy and credibility of the evaluation results.
New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk.
Computer-Aided Design (CAD) is employed extensively to facilitate design processes through software tools, serving as an indispensable component in Reverse Engineering (RE) across various sectors. This study elucidates the integration of RE and CAD in constructing generic product models for the manufacturing industry, particularly through the enhancement of the Feature-Based Design (FBD) approach. The Characteristic Product Features (CPF) methodology, pivotal in this research, enhances FBD by enabling the creation of parametrically defined generic features. Such features encapsulate a range of parameters including geometrical dimensions, topological constraints, and requirements for material properties and functionality, all dictated by the parametric model established. The methodology affords mechanical engineers enhanced capabilities to devise specific or customized manufacturing processes, applicable in domains spanning CAD, Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). The practical application of CPF within CAD is exemplified through the development of a three-dimensional geometrical model of an extruder screw utilized in polymer extrusion, illustrating the potential for tailored process innovation in manufacturing.
In this study, an economical prototype of a uniaxial shake table, named the Eastern Mediterranean University (EMU) shake table, was developed using an Arduino platform for the simulation of sinusoidal waves and scaled earthquake data. The table incorporates a ball-screw mechanism actuated by a stepper motor. Simulations were conducted using sinusoidal signals and earthquake data for three distinct seismic events, recorded at discrete timestamps. The performance of the shake table was assessed by analyzing the discrepancies between the input signals and the table's outputs.In sinusoidal mode, a feedforward gain was computed to achieve the desired output amplitude values. Furthermore, a decreasing trend in the error between input and output acceleration values was observed. The table, without any payload, achieved an acceleration of 0.8 g at a frequency of 14.5 Hz and an amplitude of 1 mm. However, the effectiveness of earthquake simulations was constrained by the storage capacity of the Arduino Uno and the motor's performance capacity. Iterative methods were necessary for each earthquake simulation to determine the minimal timestep size that the motor could optimally handle. The methodology for simulating earthquakes was elaborated, identifying limitations and suggesting areas for future enhancement. The major constraints of the project were cost, time, and resource availability.
In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings.
Effective management of supply chains, pivotal for sustaining business operations, is increasingly challenged by rising costs and complexity in logistics processes. Performance-Based Logistics (PBL) emerges as a critical strategy to enhance logistical effectiveness and competitiveness by focusing on performance targets rather than merely procuring products or services for maintenance and repair. This study examines the implementation of PBL in manufacturing enterprises and explores the factors influencing its benefits. By employing the polytopic fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method, a sophisticated Multi-criteria Decision Analysis (MCDA) technique, criteria were weighted to determine their impact on PBL effectiveness. It was found that the paramount criterion affecting PBL advantages is the capability to manage operations more effectively, whereas the reduction in system lifecycle costs through savings in labor and training was identified as the least impactful. This analysis not only underscores the necessity of designing reliable systems that align with customer expectations but also highlights the added value PBL provides by integrating reduced support elements essential for logistics and sustainability. The findings advocate for meticulous emphasis on PBL practices within business models to optimize operational efficiency and strategic advantage.
This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings.
The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications.
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