Optical film cutting is a critical upstream process in LCD and electronics manufacturing, characterized by high material costs, diverse order specifications, and frequent demand fluctuations that complicate production planning. In practice, manufacturers must coordinate cutting methods, manage semi-finished inventory across multiple periods, and handle recyclable waste simultaneously. However, these decisions are often made independently, resulting in excessive material usage, unstable inventory levels, and avoidable waste. This study develops a multi-period optimization framework for LCD optical film cutting that integrates two production routes, namely slitting and miter cutting, with inventory control and waste recycling. The model captures key operational trade-offs, including process selection, timing of intermediate production, and recycling decisions. It first minimizes total costs, incorporating material, processing, inventory, and waste-related costs, and is further extended into a bi-objective formulation that jointly considers operational cost and waste generation. The primary contribution of this research is to provide an integrated decision framework that reflects real production conditions in electronics manufacturing. By linking cutting, inventory, and recycling decisions across multiple periods, the model enables more coordinated planning and improved resource utilization. A case study with large-scale order data shows that the proposed approach can reduce waste, stabilize inventory, and improve the balance between cost efficiency and environmental performance. The analysis also reveals that cutting assignments tend to concentrate around several preferred angle configurations under realistic production conditions, suggesting the existence of practical process preferences and recurring operational patterns in optical film manufacturing. Overall, this study offers a practical planning tool for optical film converting operations and is applicable to other high-value, roll-based materials in precision manufacturing environments where inventory linkage, process flexibility, and recyclable waste are critical considerations.

The rapid advancement of the Internet has significantly reshaped traditional business models, enabling firms to leverage both online and offline channels to enhance competitiveness and profitability. This study explores the interplay between bundling and pricing strategies within a dual-channel retail system that integrates a physical store and an online shop, utilizing a game-theoretic approach while accounting for network externalities. A two-stage model is proposed: in stage 1, a manufacturer supplies two products with differing network externalities to a retailer, who must decide whether to sell them individually or as a bundle. In stage 2, the manufacturer considers launching its online channel to complement the existing physical channel. Four distinct scenarios are analyzed, examining the bundling and pricing strategies employed by both the manufacturer and the retailer across both channels to maximize their profits. The findings indicate that the integration of physical and online channels is mutually beneficial for both parties, resulting in increased profits that grow alongside stronger network externalities. Moreover, the optimal bundling strategy is contingent upon the nature of the products and their respective externalities. Specifically, when both products demonstrate high network externalities, the manufacturer should implement a mixed strategy—offering the products as a bundle online while selling them individually in physical stores. Numerical analysis emphasizes the importance of network externalities in shaping bundling decisions and profit outcomes, providing actionable insights for firms operating in multi-channel retail environments.

The study examines the influence of different carbon policies and the incorporation of green technologies in a two-echelon supply chain, with a focus on carbon emissions generated during transportation, production, and storage phases. The study evaluates three strategies for controlling carbon emissions: setting a maximum limit on total emissions, implementing carbon-taxation, and adopting a cap-and-trade framework. The proposed model assists businesses determine the optimal production and delivery volumes, as well as calculate the most effective investment in green technologies to reduce costs in the context of different carbon emission regulations. Furthermore, this study offers practical guidance for policymakers, highlighting the importance of balancing environmental sustainability with economic growth. Results indicate that companies are more inclined to pursue advanced green technology solutions under a carbon tax policy. The analysis highlights that carbon emissions per unit of production and transportation distance significantly impact overall emissions. The imposed emission cap has a stronger influence than the emission reduction potential of green technologies. The study recommends that governments establish realistic emission limits in cap-and-trade schemes to prevent excessive trading of emission allowances by suppliers.

This research delves into the software testing process and its environmental factors to uncover the core elements influencing software reliability. Specifically, it focuses on the learning and negligent aspects of the software reliability growth model. The learning factor accelerates reliability growth, leading to an S-shaped curve in the mean value function, while the negligent factor highlights the occurrence of imperfect debugging. The study also uses Brownian motion and stochastic differential equations to establish statistical confidence intervals for reliability and costs. These intervals aid software managers in assessing potential release risks at various confidence levels, allowing them to make informed decisions considering resource constraints and desired system reliability levels across different scenarios.