Multi-level Production Planning Considering Conflicting Multiple Objectives: A Case Study of the Arak Machine Manufacturing Plant

نوع: Type: Thesis

مقطع: Segment: masters

عنوان: Title: Multi-level Production Planning Considering Conflicting Multiple Objectives: A Case Study of the Arak Machine Manufacturing Plant

ارائه دهنده: Provider: Hossein Kavousi

اساتید راهنما: Supervisors: Dr. Amir-Saman Kheirkhah

اساتید مشاور: Advisory Professors: Dr. Saeed Rezaei

اساتید ممتحن یا داور: Examining professors or referees: Dr. Soleimani, Dr. Alamdar

زمان و تاریخ ارائه: Time and date of presentation: 2026

مکان ارائه: Place of presentation: 62

چکیده: Abstract: Production planning and scheduling in heavy industries present significant managerial challenges due to complex structures, resource constraints, and conflicting objectives. This study proposes an integrated optimization model to simultaneously manage four key criteria—time, cost, quality, and energy consumption—in the boiler manufacturing process at the Arak Machine Manufacturing Company. The proposed mathematical model is formulated as a multi-objective, multi-mode Resource-Constrained Project Scheduling Problem (RCPSP), where three distinct levels of workforce skill are considered as different execution modes for project activities. To solve the model and achieve a compromise solution among the conflicting objectives, the interactive STEM method was employed. Results obtained from implementing the model using real-world factory data demonstrate that the proposed approach successfully establishes an appropriate balance among the four conflicting goals. Compared to the current operational status of the factory, it achieves significant improvements in project completion time, execution costs, and energy consumption. The findings of this research indicate that employing multi-objective optimization models based on real industrial data can serve as an effective decision-support tool for managers in production project planning. Furthermore, it paves the way for enhancing productivity, reducing costs, and advancing towards sustainable production practices within heavy industries.