International Journal of Industrial Engineering: Theory, Applications and Practice
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The Effect of Incentive Policy on Manufacturer's Disruption Recovery In Building A Resilient Supply Chain
Get PDFThe outbreak of the epidemic and the complex international situation have caused supply disruptions to varying degrees. To survive the disruption, it is critical to think about how to build a resilient supply chain. We examine the manufacturer's recovery capacity under the fiscal interest-discount rate and tax discount rate. Motivated by the current incentive policies, such as the fiscal interest-discount policy and tax preference policy, we develop a game-theoretical game model focusing on a manufacturer aiming to maximize profits, a government aiming to maximize utility, and consumers. We have derived optimal decisions for products with varying degrees of scarcity after disruption by analyzing the perspectives of profit maximization and social welfare optimization for each entity in the supply chain. We find that for the recovery of moderately scarce products, the fiscal interest-discount policy will be the optimal choice for both the interrupted manufacturer and the government. However, as production scarcity increases, the manufacturer will prefer the tax preference policy
Dynamic Computational Approach for Routing and Scheduling Last-Mile Distribution
Get PDFThis paper proposes an efficient dynamic mathematical programming approach to route and schedule downstream last-mile distributions of goods and assign a set of delivery points to delivery persons for minimizing the number of delivery persons required over multiple time periods while satisfying predetermined time restrictions. This approach aims to develop a decomposition MIP-based procedure that dynamically changes the members of the delivery point sets. This procedure allows for easily solvable optimization models, reducing computational efforts and minimizing memory usage and execution times. To demonstrate the effectiveness of the proposed approaches, the paper presents two datasets of 25 last-mile case-problems. The key findings include significant computational improvements in terms of reduced numbers of constraints and variables, which result in reduced computational effort, time, and memory usage required for optimizing the routes, schedules, and assignments of last-mile distributions
Analyzing Key Challenges for Implementing Circular Supply Chains in The Indian Electronics Sector: A Study Using AHP-Entropy Methodology
Get PDFThe Circular Economy (CE) prioritizes sustainable practices over conventional ones that deplete resources and generate waste, which is crucial for the electronics industry due to valuable metals and harmful compounds in E-waste. This study aims to identify core obstacles to establishing Circular Supply Chains (CSC) in India's electronic sector. This article delineates twenty-seven challenges encountered by the Indian electronic manufacturing Supply Chain (SC) through a comprehensive literature review and insights from industry experts from prominent electronic multinational companies. These challenges were subsequently categorized into five distinct groups. To examine the prioritization of critical obstacles, we used the Analytical Hierarchy Process (AHP) technique in conjunction with Shannon's Entropy approach. The model robustness was investigated by performing sensitivity analysis. The investigation uncovers the cost of sustainable materials, restricted financial capacity for implementing CSCM initiatives, absence of fiscal incentives for advancing CSC, focus on immediate economic gains, and lack of matured technology resources as core obstacles. These study outcomes will help decision-makers understand the main challenges in migrating to a CSC, and accordingly, they may pick out a successful navigation path. The scientific procedure of modeling obstacles' criticality prioritization will assist them in enhanced decision-making and focused efforts for incorporating circularity in SC
Joint Optimization of Pricing and Inventory for Cross-Border E-Commerce Retail Export Supply Chains, Considering Export Tax Rebates and Stochastic Demand
No full textThis paper investigates a cross-border dual-channel supply chain involving domestic manufacturers and foreign retailers. The supply chain integrates wholesale and online sales channels managed by domestic manufacturers with traditional retail channels operated by foreign retailers. The Chinese government provides export tax rebates to manufacturers to stimulate international sales, while foreign import tariffs influence pricing and demand dynamics. Consumer purchasing behavior in traditional retail channels is influenced by inventory levels. Excess inventory often triggers intensified promotional efforts by sales staff. The interaction between domestic manufacturers and foreign retailers is modeled as a Stackelberg game, with random demand following a Poisson distribution. The findings reveal that, under certain conditions, an increase in the export tax rebate rate reduces wholesale, direct sales, and distribution prices, which in turn boosts order volumes from retailers. Similarly, higher retail import tariffs can lower wholesale and direct sales prices, although the distribution price varies based on specific factors, ultimately increasing retailer order volumes. In contrast, an increase in wholesale import tariffs results in lower prices across all channels and reduced retailer order volumes. Additionally, when consumer preference for online shopping increases, wholesale prices decrease, direct sales prices rise, distribution prices drop, and retailer order volumes decline. Numerical simulations provide strategic insights for optimizing profitability in cross-border dual-channel supply chains. These insights contribute to greater supply chain stability, alleviation of domestic overcapacity issues, and promotion of international cooperation for mutual benefits. This research offers both theoretical and practical guidance for multinational firms aiming to refine wholesale and inventory strategies in tariff-sensitive environments. It highlights pathways to enhance resilience and expand operations in global markets
Digital Nudge and UX Psychology: Improving Human Visual Perception on Mobile Platforms
No full textThis 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
Reliability Optimization of Linear and Linear Consecutive K-Out-Of-N Systems Using Teaching-Learning-Based Optimization and Genetic Algorithm
Get PDFNumerous engineering applications involve ensuring the proper functioning of systems, minimizing errors, and optimizing the system and its subcomponents. Achieving desirable outcomes often requires enhancing positive factors through optimization methods while mitigating negative factors. In this context, metaheuristic algorithms are favored to find solutions aligned with the intended objectives. Among such algorithms, Teaching-Learning Based Optimization (TLBO) and Genetic Algorithm (GA) stand out, drawing inspiration from real-life processes. This study focuses on applying the TLBO algorithm to optimize the reliability of linear k-out-of-n: F and G (lin/k/n: F and lin/k/n:G) and linear consecutive k-out-of-n: F and G (lin/con/k/n:F and lin/con/k/n:G) systems. Additionally, the system was analyzed using GA, and the results from both approaches were compared. By employing these powerful metaheuristic algorithms, we aim to attain effective and robust solutions for enhancing system reliability and performance. Also, this study can be a guide in terms of contributing to the reduction of costs by ensuring more efficient use of resources, especially in complex systems. It can also increase productivity by reducing labor by ensuring the efficient operation of machines and processes
Electroencephalography (EEG) Signal-Based Mental Workload Detection for Intelligent Workstation Machine Cognitive Task
Get PDFThe intelligent workstation serves as a cornerstone in the advancement of intelligent and digital manufacturing systems, with operators' roles increasingly shifting from physical labor to cognitive effort. To examine the effect of cognitive task difficulty on mental workload (MWL), operational tasks of low, medium, and high difficulty were designed as experimental conditions. The National Aeronautics and Space Administration Task Load Index (NASA-TLX), task performance metrics, and electroencephalographic (EEG) data were utilized to compare differences across the three levels. The results showed that, under high task difficulty, the average whole-brain power spectral density (PSD) in the Theta, Alpha, and High Beta frequency bands was significantly elevated among the 20 participants. Further electrode-level analysis revealed that the sensitivity and discriminative capacity of EEG signals varied across frequency bands and electrode sites. These EEG-based indicators demonstrate strong potential as neurophysiological biomarkers for differentiating levels of MWL and detecting operator overload in intelligent workstation contexts
An Inventory System with Time-Dependent Holding Cost and Demand Dynamics under a Generalized Trapezoidal Neutrosophic Framework
Get PDFThis study presents an inventory model that incorporates realistic demand factors such as price, time, reliability, and the influence of advertisements. The model allows shortages with full backlogging and considers permissible delays in payment. Deterioration is represented by a two-parameter Weibull distribution, while the holding cost is modeled as a function of both holding time and item reliability. Since inventory cost parameters are often imprecise, this uncertainty is addressed using Generalized Trapezoidal Neutrosophic Numbers (GTrNNs). To handle such imprecision, a novel de-neutrosophication technique is proposed. The payment structure considers two cases: when the credit period is less than or equal to the replenishment cycle time, and when it is greater. Optimal solutions are derived by taking the time at which the inventory level reaches zero as the decision variable. Numerical examples and sensitivity analyses are conducted to examine the impact of variations in key parameters on the optimal inventory policy
Optimizing Replenishment System in Refrigerated Beverage Supply Chain Using Control Charts and TOPSIS: A Case Study of Convenience Stores in Taiwan
No full textAs consumers become more health-conscious, the demand for fresh, short-shelf-life beverages has increased, making effective inventory management essential. This study optimizes a replenishment system for refrigerated beverages in convenience stores to ensure product freshness, reduce waste, and improve inventory efficiency. To achieve this goal, we used XΜ
control charts and modified Western Electric rules to monitor demand and inventory dynamics, enabling accurate forecasting and responsive supply chain management. System performance, evaluated based on inventory stability, bullwhip effect, and service level, was integrated using the technique for order preference by similarity to ideal solution (TOPSIS) to determine the optimal replenishment parameter combination from historical data. This study proposes an SPC-based replenishment framework that not only applies control charts to monitor demand and inventory data but also leverages TOPSIS to optimize key performance indicators. This integrated approach balances methodological rigor with practical applicability, enabling timely, data-driven decisions and enhancing supply chain performance for refrigerated beverages
Who Should Undertake Corporate Social Responsibility in Freshness Preservation for Dual-Channel Fresh Food Products Supply Chain?
Get PDFThis paper examines the impact of corporate social responsibility (CSR) on the fresh food supply chain. Using a Stackelberg game framework, three scenarios are developed: scenario M1(No CSR), scenario M2 (CSR by the retailer), and scenario M3 (CSR by the supplier), to make efforts on the freshness preservation. The analysis reveals that increased CSR commitment consistently leads to lower retail prices, more significant preservation efforts, and larger consumer surplus. While CSR enhances social utility, it often comes at the expense of supply chain members' profits. In the retailer-led CSR scenario, the supplier's dominant position increases overall supply chain profitability despite reduced retailer profits. Counter-intuitively, in the supplier-led CSR scenario, overall profitability decreases, but consumers benefit from fresher products at lower prices due to higher preservation efforts and lower online channel sales prices. The findings suggest that strategic CSR adoption can improve social welfare and market competitiveness, providing a theoretical basis for real-world business decisions. Future research could explore optimal CSR allocation within the supply chain to balance profitability and social welfare