A Heuristics-based Advanced Planning and Scheduling System with Bottleneck Scheduling Algorithm

This paper presents a heuristics-based advanced planning and scheduling (APS) system with bottleneck scheduling algorithm. It has been designed to solve production scheduling problems in discrete manufacturing industry. The proposed APS system can be configured to be deployed in different production environments, including make-to-stock, make-to-order, bottleneck-driven shop floor, through its forward, backward and bottleneck scheduling algorithms. It allows users to specify heuristic rules at each operation based on the scheduling policy of the operation. The embedded scheduling techniques facilitates the generation of feasible and practical schedule to achieve a fine balance among the conflicting production goals of maximizing resource utilization, minimizing work-in-process (WIP), and reduction of cycle time. In addition, the system can be easily reconfigured to address them various requirements imposed by the physical and operational constraints of the production environment. The APS system deploys two layers of heuristic algorithms intertwined within the scheduling engine. The two layers of heuristic algorithms are job prioritization (JP) rules and machine selection (MS) rules. JP heuristics rules are designed to prioritize orders at each operation, while machine selection (MS) algorithm selects the best-fit machines and other optional resources to generate the dispatching list. The modular and configurable approach adopted in the design and development of the scheduling engine allows the reconfiguration of basic core JP and MS modules for different industry-specific requirements. The proposed APS system has been successfully implemented to fulfil the daily production scheduling needs of a few semiconductor backend assembly companies.