Publication: Plywood product mix optimization using metaheuristics
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Date
2024-07-01
Authors
Chong Wen Cong
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Abstract
Plywood, a composite material made up of several layers of wood with different thicknesses, serves a vital function in the fields of construction and furniture production. Nevertheless, the sector encounters challenges associated with component variability and manufacturing complexity. The objective of this study is to tackle these problems by optimizing the ordering and manufacturing operations for plywood in order to minimize production costs and maximize net profit. The research employs a methodical approach, commencing with a thorough analysis of the manufacturing complexity, encompassing fourteen essential manufacturing processes. Afterwards, a Microsoft Excel spreadsheet was created to carry out the calculation and construct a cost evaluation fitness function that includes adjusting the production orders and manufacturing process operating hours. The raw data was obtained from an industry case study. A cross-platform Python program was created to establish a connection between the spreadsheet and execute optimization tasks utilizing 25 different pre-programmed metaheuristics chosen from the Mealpy (Metaheuristic algorithms library in Python) library. Three stages of performance analysis were conducted to identify the best metaheuristics. The results indicate a substantial increase in net profit after optimization. During the performance analysis, SHADE (Success-History based Adaptive Differential Evolution) has shown superior performance compared to other selected metaheuristics. The method demonstrated improved resilience and flexibility in the optimization process by utilizing the past performance of effective parameter choices. The sensitivity analysis demonstrated the effects of changing profit and cost elements on the net profit. The study's findings offer insights into the plywood industry, presenting a realistic and effective optimization strategy to improve net profit through iterative improvement in metaheuristic. The versatility and adaptability of this cross-platform optimization framework make it suitable for other industries that encounter comparable issues, highlighting the wider range of applications and the significant influence of the research.