A Heuristic Factory Planning Algorithm for Supply Chain Management

• Main Authors: Ling-Chieh Kung, 孔令傑
• Other Authors: Ching-Chin Chern
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/17910600402932857519

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 95 === In resent years, supply chain management (SCM) has become an emerging issue discussed widely. Each member involved in supply chain operations makes efforts to reduce its costs and maximize its profits. This study belongs to the Factory Planning (FP) level in Advanced Planning and Scheduling (APS), a supply chain planning model. After the Master Planning (MP) level completes a mid-term plan for members involved in supply chain operations, each manufacturer is assigned a production quantity for each product in each mid-term period. According to the product structure and routing of each product, the FP level models the production by designing jobs. Each job consists of several stages while each stage occupies several short-term periods at a specific work center and can start to work only after its preceding stages produce enough WIP. An FP problem is thus a generalized sequence-dependent job scheduling problem with its sequence-dependent nature make it an NP-hard problem and increases the difficulty of optimization. According to the importance and difficulty, this study focuses on optimizing scheduling problems inside factories. FP problems discussed in this study have three objectives: minimizing delay time, minimizing cycle time, and minimizing advance time. Although FP problems can be formulated by an MIP model, the number of binary variables is too large for the MIP model to solve FP problems in an acceptable time. Therefore, this study develops a heuristic FP algorithm, Heuristic Factory Planning Algorithm (HFPA), to find a near-optimal solution. HFPA contains three phases: work center sorting, job sorting, and job scheduling. HFPA first sorts work centers according to their work loads and finds the bottleneck work center. It then sorts jobs and gives higher priority to those jobs depending on the bottleneck work center more. After determining the order of jobs, the Bottleneck-Oriented Scheduling Algorithm (BOSA) is performed to schedule jobs one by one in three iterations. In the first iteration, it schedules jobs which can be scheduled in their preferred interval without splitting; in the second iteration, it schedules jobs which can be scheduled in their preferred interval with splitting; in the third iteration, it schedules all remaining jobs by advancing or delaying them. BOSA also minimize cycle time of this job when scheduling stages of a job. HFPA can produce near-optimal or optimal solutions when the capacity is loose, the production type is flow shop, the bottleneck is significant, and the stage structure is complex. Each of the first three factors dominates the fourth factor and amplifies each other. However, comparing to the exponentially-growing solving time of exact algorithms such as MIP, HFPA is a polynomial time algorithm and requires only very less time to solve an FP problem. Therefore, HFPA can also perform well when the size of problem increases such that the solving time and the objective values do not grow exponentially. In conclusion, HFPA is suitable for solving general FP problems effectively and efficiently.