Optimizing Production Planning in the Banbury Mixing Process via Modified FCMRP: A Tire Manufacturing Case Study
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Creator Anan Butrat
Title Optimizing Production Planning in the Banbury Mixing Process via Modified FCMRP: A Tire Manufacturing Case Study
Contributor Rissaphop Treesuwan, Thanitsak Pudtipatkosit
Publisher Phetchaburi Rajabhat University
Publication Year 2569
Journal Title Interdisciplinary Research Review (IRR)
Journal Vol. 21
Journal No. 5
Page no. 23-35
Keyword Banbury mixing process, FCMRP, quencing rules, orward and backward scheduling, application in industry
URL Website https://ph02.tci-thaijo.org/index.php/jtir
Website title Interdisciplinary Research Review (IRR)
ISSN 2697-536X
Abstract Production planning in the Banbury mixing process presents unique scheduling challenges, primarily due to complex alternative Bill of Materials (BOM) routing and mandatory natural aging constraints. This study addresses the severe operational inefficiencies—specifically, excessive lead times and high machine idle time ratios—currently faced by a real-world tire manufacturing company. To resolve these issues, we propose a modified Finite Capacity Material Requirements Planning (FCMRP) framework. This approach dynamically calculates job release times based on predecessor due times and previous order finish times to effectively accommodate mandatory resting periods. To determine the most effective scheduling strategy, we systematically evaluate the company's baseline Highest Order Quantity (HOQ) rule against standard Earliest Release Time (ERT) and Shortest Processing Time (SPT) heuristics. Furthermore, we introduce a novel Non-Alternative Machine (NAM) sub-rule designed to intelligently isolate and reallocate movable excess jobs to alleviate machine bottlenecks. The experimental results demonstrate that combining the ERT rule with the NAM sub-rule successfully reduces bottleneck workloads and yields the optimal overall lead time. Conversely, applying ERT without the NAM logic provides the best machine idle time ratio. Ultimately, this research delivers a practical, data-driven framework for selecting priority rules to optimize complex mixing processes in the manufacturing industry.
Nakhon Pathom Rajabhat University

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