Bottleneck-based heuristic dispatching rule for optimizing mixed TDD/IDD performance in various factories

Abstract

Most research on scheduling problems focuses on increasing production efficiency. For instance, the shortest processing time (SPT) and earliest due date (EDD) dispatching rules perform well in minimizing mean flow time and reducing maximum tardiness, respectively. However, those indices ignore the financial impact (material cost and order price) on the factory. Previous studies focused mainly on cycle time and due date. However, the theory of constraint (TOC) considers not only the effect of time, but also financial factors. Therefore, TOC addresses the concepts of throughput-dollar-day (TDD) and inventory-dollar-day (IDD). The former index (TDD) represents penalties for tardy deliveries, while the latter index (IDD) refers to the material holding cost. Based on these two indices, this investigation creates a novel mixed TDD/IDD weighted value (Zvalue) to replace the other traditional indices for taking measurements in various factories. This study also designs a heuristic dynamic scheduling algorithm (mixed TDD/IDD dispatching rule) for reducing the system Zvalue. Some traditional dispatching rules are compared with the proposed rule in terms of TDD, IDD, and Zvalue. Analytical results indicate that the mixed TDD/IDD dispatching rule is feasible and generally outperforms other conventional dispatching rules in terms of Zvalue under various factories.