A Fuzzy AHP-TOPSIS Approach for Dynamic Due Date Assignment in Machine Scheduling

Abstract

Dynamic machine scheduling involves integrating new jobs into existing plans with minimal disruption. Quoting due dates for new jobs presents a trade-off between customer satisfaction and tardiness risk. The complexity increases in family setups, where batch processing requires additional setups. This study focuses on due date assignment for new arrivals and scheduling of both new and incomplete jobs. Four objectives are considered: minimizing total setup time, tardiness of incomplete jobs, due date of the new job, and energy consumption. To address uncertainties in processing and setup times, a hybrid fuzzy multicriteria decision-making (MCDM) approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is proposed. FAHP captures expert judgments to weigh objectives, while FTOPSIS ranks insertion positions under uncertainty. The framework offers a computationally efficient, expert-driven alternative to optimization models, suitable for real-time dynamic scheduling.