Modern software-intensive engineering systems require release planning strategies that simultaneously balance software reliability, development cost, and time-to-market under uncertain debugging conditions. Existing software reliability growth models (SRGMs) generally assume perfect debugging or instantaneous fault removal, whereas practical software development often involves imperfect fault correction, delayed debugging processes, and multiple sequential software releases. Moreover, most existing studies primarily focus on reliability estimation rather than optimization-oriented release decision support. To address these limitations, this study proposes an integrated optimization framework for multi-version software release planning that jointly considers imperfect debugging, time-delayed fault removal, and reliability–cost trade-offs. The proposed framework is developed based on a non-homogeneous Poisson process (NHPP) and extends conventional SRGMs by explicitly modeling the interactions among fault detection, delayed correction, and debugging efficiency across successive software versions. A joint optimization model is subsequently formulated to determine economically efficient release policies while satisfying predefined reliability requirements. Numerical experiments demonstrate that the proposed approach consistently achieves lower total expected costs and improved release decisions compared with conventional sequential planning methods. Sensitivity analyses further reveal the significant influence of debugging efficiency, delay effects, and fault-removal dynamics on optimal release policies and overall software reliability. From an industrial engineering perspective, the proposed framework provides an effective computational decision-support methodology for software release management by integrating reliability modeling with optimization techniques. The results offer practical managerial insights for balancing software quality, development resources, and release scheduling in complex multi-version software development environments.

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.