Integrating Multi-Index Materials Classification and Inventory Control in Discrete Manufacturing Industry: Using a Hybrid ABC-Chaos Algorithm

In order to efficiently reduce the high inventory cost of discrete manufacturing firms, which is caused by the large production scale, complex processes, and varieties of materials, this article presents a two-stage decision framework integrating multi-index material classification and inventory control policy. In the first stage, a multi-index material classification method based on analytic hierarchy process-information entropy is implemented to categorize materials in production process into three classes: class A (i.e., strategic materials), class B (i.e., bottleneck materials), and class C (i.e., general materials). In the following stage, based on the results of material classification, an order quantity forecasting model for strategic materials based on ( Q , s ) is constructed with the objective to minimize the total inventory cost over a planning horizon. Then, a hybrid Artificial Bee Colony-Chaos (ABC-Chaos) algorithm is employed to solve the proposed model. To be specific, the chaotic local search strategy is introduced to help local extreme point escape from bondage in the random search phase of scout bee. Finally, a real-world case study from a typical foundry enterprise is illustrated to demonstrate the applicability and feasibility of the proposed decision framework. The experimental results show that the proposed hybrid ABC-chaos algorithm performs better than the other algorithms.