Optimization of Inventory Replenishment under Asymmetric Stock-Out and Inventory Holding Costs

Perishable products are an essential part of commerce. Shelf-life characteristics are usually not modeled in traditional inventory models. This study proposes an inventory replenishment model for perishable products with an asymmetric cost structure for holding and stock-out costs. The modeling phase involves the shelf-life characteristics of products. Shelf life is essential due to sustainability concerns, costs, and service levels due to perished products. In contrast to classical safety stock models, where stock-out costs increase linearly, the proposed model utilizes incrementally increased fixed costs for holding costs in a conflicting cost structure. It incorporates the shelf-life of the products, calculates the probability of perishing, and formulates accurate waste and total costs using an asymmetrical cost structure. The model is applied to a real dataset to assess the performance and compare it with the traditional approach. The performance of the proposed model is better, with a total cost reduction of 45.33%. Additionally, the model demonstrated a 17.21% increase in service level. The sensitivity analysis further underlined the robustness of the proposed model across various demand scenarios and shelf-life conditions. The main research gap addressed by this study is the lack of consideration for shelf-life characteristics and asymmetric cost structures in traditional inventory models. By integrating these factors, this research provides a more accurate and cost-effective approach to inventory management for perishable products, enhancing sustainability and service levels. This study's findings can help businesses optimize inventory strategies, reduce waste, and improve operational efficiency

Design and Implementation of Lean Six Sigma in The Garment Industry

Lean Six Sigma (LSS) is widely adopted in manufacturing industries to optimize production efficiency and minimize defects. However, its application in the garment industry, particularly in sewing line balancing, remains underexplored. The study integrates LSS methodologies with the Ranked Positional Weight (RPW) method to enhance line balancing efficiency in a sewing line. The Define, Measure, Analyze, Improve, and Control (DMAIC) framework was applied to identify the root causes of defects, optimize workstation balancing, and evaluate process improvements. Data was gathered from a single sewing line to evaluate the existing sigma level and identify critical inefficiencies. Statistical tools such as Process Capability Indices (Cp, Cpk) and short-term vs. long-term sigma level analysis were used to assess process variations. The findings indicate that implementing RPW resulted in a reduction of workstations from 27 to 22, an increase in line balancing efficiency from 53% to 70.3%, and a decrease in the defect rate from 8.34% to 4.6%. The process sigma level improved from 2.9 to 3.19, demonstrating a significant enhancement in quality performance. This research demonstrates practical value by integrating Lean Six Sigma (LSS) with the Ranked Positional Weight (RPW) method for line balancing in the garment industry. It offers valuable insights into critical challenges, identifies root causes, and presents a scalable approach for defect reduction in textile manufacturing

Optimal Control Strategies for Adaptive Pricing in Ride-Sharing Services

Rideshare platforms are an example of economies of sharing where ride requests initiated by riders are fulfilled by car owners through the platform that connects both of them. When demand for a ride is initiated by the customer, the platform checks service providers' (car owners) availability and assigns a fare (ride price) that both the ride requester and provider should agree on to complete the transaction, and the ride service is fulfilled. In this research, optimal pricing strategies for ride-share platforms are considered. The optimal control approach is used to first develop differential equations to model the dynamics of the number of ride requests and for the price rate. Second, we model the total profit as a function of a linear revenue and a nonlinear cost. The optimal rate of change in the ride price is then obtained. Finally, a numerical example and extensive sensitivity analyses not only provide insights into the effect of the system parameter on the model but also lead to managerial implications to help companies determine the best price for each ride

Strategic Investment in BIST100: A Machine Learning Approach Using Symbolic Aggregate Approximation Clustering

This study employs the Symbolic Aggregate Approximation (SAX) clustering method to enhance investor decision-making on the Borsa Istanbul (BIST100) by identifying companies exhibiting analogous stock movements. The data from 81 BIST100 companies over a three-year period has been analyzed, with a focus on risk minimization and strategic investment. The SAX method, integrated with a dendrogram, categorizes stocks into sector-based and non-sector-based clusters, providing insights for portfolio optimization. The results demonstrate the effectiveness of the method in identifying relevant stock patterns across sectors, aiding in more informed investment decisions. This approach highlights the need for considering multiple factors in investment strategies, offering a new perspective on stock market analysis with advanced clustering techniques

A Deep Learning-based Data-driven Approach for Modeling and Optimization of Laser Transmission Welding of Polypropylene

In this study, a novel multi-stage framework is explored for laser transmission welding of polypropylene by integrating the design of experiments (DoE), artificial neural networks (ANN), non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective optimization by ratio analysis (MOORA). The framework enables comprehensive experimental investigation, process modeling, and multi-objective optimization. The response surface method (RSM) based DoE is used to develop correlations between welding parameters and responses, which form the foundation for experimental investigations. ANN models, incorporating additional fractional factorial DoE data, are employed for precise non-linear mapping of process parameters and responses, with predictive accuracy surpassing that of RSM models. The 3-6-1 ANN architecture is demonstrated to predict weld strength with high precision, while the 3-7-2-1 model is found to predict weld width accurately. These ANN models are used as objective functions for simultaneous optimization via NSGA-II, generating Pareto-optimal sets. These sets are further prioritized by MOORA, with an optimal parameter set of 220 W laser power, 81.29 mm/s scanning speed, and 63.97 mm defocus distance, yielding a weld strength of 63.86 N/mm and a weld width of 3.24 mm. The proposed synergistic DoE-ANN-NSGA-II-MOORA framework not only confirms its efficacy in this particular case but is also adaptable for other materials and processing applications

A Novel Hybrid Method for Intelligent Machining Feature Recognition in Manufacturing Systems

A matter-element and graph-based machining feature recognition method is proposed to address the recognition of complex machining features and provide corresponding tool adaptation interfaces. First, since complex features are formed through Boolean operations of basic features, an inclusion relationship necessarily exists between complex and basic features. Therefore, the matter-element model, which excels at representing inter-object relationships, is employed to describe complex features, basic features, and their relationships. Next, an Attributed Adjacency Graph (AAG)-based algorithm is introduced to decompose the entire part and derive AAGs of machining features. The corresponding Attribute Adjacency Matrix (AAM) for each machining feature is constructed based on geometric element coding rules to enable basic feature recognition. Furthermore, using shared surfaces and edges extracted from the STEP neutral file, the recognized basic features are systematically organized as matter-element structures to represent complex features. The critical dimensions of complex features are determined by comparing the geometric dimensions of the constituent basic features. Finally, a platform developed in Java 1.8 demonstrates the method’s practicality through a case study. Results indicate that the proposed method is not only straightforward to implement but also readily integrable with cutting processes

Comparative Effectiveness of Data Normalization Methods in ARAS for Multi-Criteria Decision-Making Across Industrial Applications

Multi-criteria decision-making (MCDM) utilizes various tools and methods to enhance decision-making across fields such as engineering, materials, manufacturing, and management. Data normalization is a crucial step in MCDM for converting criteria values to a standard scale, enabling accurate rating and ranking of alternatives. Selecting the appropriate data normalization method from various available options remains challenging yet crucial for effective decision-making across industrial applications. In this study, ten data normalization methods (DNMs) are evaluated with the ARAS (Additive Ratio Assessment) method, and their selection for enhancing robustness in MCDM is investigated. The suitability of each DNM is assessed through various test cases and sensitivity analyses, examining the impact of normalization on decision-making results. A comparative analysis of DNMs is performed using Spearman’s rank correlation, criteria weight variations, dynamic matrices, and plurality voting to identify the most effective data normalization methods. The findings from this study offer comprehensive insights into how different DNMs influence the performance of the ARAS method, providing practical guidance for improving decision-making accuracy across industrial applications. Through this process, four additional DNMs are identified as suitable for integration with the ARAS method, expanding its application scope beyond the traditional sum-based linear normalization method

Impermanent Loss Mitigation for Decentralized Exchanges through Optimization

Decentralized exchanges are one of the most remarkable revolutionary inventions in cryptocurrency trading. Decentralized exchanges present a way to set prices mathematically without maintaining the order book-based trading system. The order book-based trading system has been a widely-known method to determine prices in the stock markets and even in decentralized cryptocurrency exchanges for over 400 years. Recently, a new type of decentralized exchange has devised a constant function market maker to determine prices mathematically. Among them, the method of constant product market makers is the most widely used one. In addition, several constant function market makers have been proposed. However, there was little discussion about the desirable properties of a constant-function market maker regarding price and impermanent loss. In this paper, we discuss the desirable price and its effects on impermanent loss. This study considers two types of prices: the reference price and the actual price. We show that impermanent gain can be achievable under a certain condition and prove it mathematically. Two examples are provided to show that impermanent gain is achievable.

Digital Nudge and UX Psychology: Improving Human Visual Perception on Mobile Platforms

This study aims to enhance mobile usability by applying nudge and Gestalt theories in UX psychology. As usability on mobile platforms significantly influences user experiences, the ergonomic design of visual elements has gained more importance. The recent UX trend emphasizes accessibility, efficiency, and personalization for optimal user experiences. Using Figma as an interface design tool, 10 mobile prototypes, such as food delivery and scheduling platforms, were tested by 18 participants. Usability was evaluated through performance time, number of clicks, and heatmaps. Applying digital nudges (defaults, reminders) and Gestalt principles (simplicity, proximity) improved human visual perception. Results showed a 63% reduction in performance time and 66% fewer clicks. Heatmaps confirmed better intuitiveness, and post-interviews highlighted increased satisfaction with the revised design. The findings emphasize the importance of tailored designs while validating the universal benefits of UX psychology

A Novel Multimodal Transport Model for School Commuting

During peak hours, the roads surrounding schools often become a source of concern due to severe traffic congestion. This not only leads to a substantial increase in travel time but also poses heightened safety risks. This study proposes a multimodal transport model that integrates private cars, shared parking spaces, and school buses (CPB) to address these challenges. The model aims to improve traffic efficiency and reduce safety hazards. The approach involves two key phases: identifying optimal locations for private car parking and optimizing school bus routes. Results show an 18.53% reduction in private car costs and an 8.13% decrease in traffic delays within the road network. The advantages of this model become particularly significant when student commuting demand exceeds 70% of peak transportation demand. This study provides a robust scientific foundation for developing traffic management strategies around schools