Investigation of Non-Value-Added Activities to Reduce Lead Time in The Mass-Customized Glass Process Industry
DOI:
https://doi.org/10.23055/ijietap.2024.31.5.9997Keywords:
mass customization, enhanced reinforcement learning, GMCDMAbstract
Increasing levels of customization in customer orders leads to numerous new challenges in the industry. One significant aspect is achieving the optimal lead time to meet customer demands. The reconfigurable hybrid permutation mass customization problem (RHPMCP), a subset of flow shop problems with significant application in the mass-customized tuff glass process industry, is the primary focus of this study. We categorize this study into two phases; the first phase investigates the non-value-added activities to identify which output parameter (i.e., Makespan, flow-time, idle-time, and efficiency) significantly affects the schedule. We employ a new decision-making method for investigation by integrating a Z-number-based Consistent Fuzzy Analytic Hierarchy Process (Z-CFAHP) and a Z-number-based fuzzy Combined Compromise Solution (Z-FCoCoSo). The second phase, using Analytical Batch Enhanced State-Action-Reward-State-Action Optimization (ABESO), reduces the identified high-impact output parameter makespan to obtain the optimal lead time. The proposed approaches are validated by sensitivity analysis for decision-making, and scheduling problems are compared to the existing scheduling rule in the real-time tuff glass process industry. This research provides a practical solution for optimizing lead time in the tuff glass process industry, demonstrating the effectiveness of the proposed method.
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