Production-Energy Mode Control and Decision Support System Development for Assemble-to-Order Systems

Articles in Press

Document Type : ODSIE 2024

Authors

1 İzmir Democracy University

2 Izmir Democracy University

Abstract

Cost management is vital in today's competitive business environment. Achieving a balance between energy and inventory management is essential for sustainable production and economic growth. This study examines an assemble-to-order system with multiple products, components, and customer types. Assemble-to-order is a strategy where components are pre-stocked and assembled into the final product once an order is received. In this system, machines producing semi-finished products operate in different energy modes with varying energy consumption. For this system, we optimize the parameters of production and order control policies that are both user-friendly and efficient. The goal is to determine which machines should produce the semi-finished products, in which energy modes, and when production should begin as inventory levels drop. Similarly, we establish when and how much to reorder when inventory levels of semi-finished products fall. Additionally, because there are multiple customer types for each final product, different allocation policies are analyzed to optimize the allocation of finished products. A general simulation model has been developed to support these analyses, designed to accommodate the system's complexity and facilitate experimental study of its structure. Finally, a decision support system is developed by adapting the simulation model to meet user requirements, allowing for practical application and further system optimization. The numerical results reveal that as customers' tolerance limits increase, average total costs rise due to higher waiting costs. In contrast, lost sales costs decrease as more time is available to fulfill demand. Additionally, On-Idle modes are more cost-effective when customer arrivals are frequent, whereas On-Off modes perform better when arrivals are less frequent. These insights highlight the importance of dynamically selecting energy modes based on demand patterns to achieve cost efficiency and improved service levels.

Keywords

References

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Journal of Optimization and Supply Chain Management

Articles in Press, Accepted Manuscript
Available Online from 24 January 2025

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