A New Decision Model for Reducing Trim Loss and Inventory in the Paper Industry

In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss. 1. Introduction Numerous industries with bulk production modes, such as the industrial-use paper industry, have gradually changed their production environments to high-mix low-volume production. Customer requirements for high-mix low-volume production and instant supply also increase the difficulty of optimizing production scheduling. An issue with even greater significance is how to properly employ production scheduling flexibility and coordinated supplementary measures. In this paper, we consider two main issues in relation to the production planning of industrial paper. (1) If production scheduling is fixed, how may the trim loss ratio and production inventory be reduced, whether evenly distributed in different widths of inventory or not. (2) Does increasing or decreasing the production of scheduling produce better results for inventory distribution and trim loss ratio better than the original scheduling? Since the 1960s [1], numerous studies have examined how to configure orders most effectively to optimize production scheduling. However, these optimized results have not been able to satisfy the requirements of numerous managers because, in situations of raw order production, if the structure remains poor after the permutation and combination of orders, significant trim loss can occur. Thus, managers must abandon optimized scheduling and use their experience to identify the best solution. Most cutting stock problems (CSPs) are classified as NP-complete, meaning that it is difficult to obtain optimal solutions. Gilmore and Gomory [1] presented a delayed pattern generation technique for solving a one-dimensional cutting problem using linear programing. Other methods can also be found in the literature [2–13]. Morabito and Arenales [14] considered