Dynamic Optimization Model for Remanufacturing Operations
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Solution Overview
Problem
The complexity of high-tech configurable multi-generation products complicates supply chains and remanufacturing operations due to variability in configuration combinations, impacting inventory management and decision-making processes, which rely heavily on heuristic rules and expert opinions, often overlooking available return inventory and resource adequacy.
Innovation Solution
A computer-implemented method and system that generates real-time operational decisions by developing dynamic optimization models from return product data, configuring return products to a commodity level, and creating plans for remanufacturing-oriented processes to recommend configurations, thereby optimizing production and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic optimization models are implemented for real-time operational decisions, then productivity and resource utilization improve, but device complexity increases
Solution Approach 1:
The system segments the remanufacturing decision-making process into distinct functional modules: return product data collection, quality analysis, dynamic optimization model development, remanufacturing plan creation, and configuration recommendation. Each module handles a specific aspect of the complex decision process, making the overall system more manageable and implementable while maintaining high productivity through coordinated operation of these segments.
2Ease of operation
If expert opinion and heuristic rules are used for decision-making, then ease of operation is maintained, but manufacturing precision and optimization deteriorate
Solution Approach 1:
The system introduces a computer-implemented intermediary layer that acts as a mediator between the complexity of dynamic optimization models and the simplicity required for operational decision-making. The intermediary automatically collects return product data, performs quality analysis, develops optimization models, and generates configuration recommendations, thereby maintaining ease of operation while achieving high manufacturing precision through algorithmic optimization rather than manual expert judgment.
3Manufacturing precision
If return product data is thoroughly analyzed and configured to commodity level, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by automatically collecting return product data and conducting quality analysis as soon as products are returned, before remanufacturing decisions are required. This preliminary processing prepares the data in advance, enabling rapid development of dynamic optimization models and quick generation of configuration recommendations when needed, thereby reducing the time loss associated with thorough analysis while maintaining high manufacturing precision.
Data Source
AI summary
Making real-time operational decisions for remanufacturing operations by receiving data on return product including analysis of return product quality, and configuring the return product meeting quality threshold to commodity level. At least one dynamic optimization models is developed from data on the return product. A plan is created for a remanufacturing-oriented process from the at least one dynamic optimization models. The dynamic plan declares all activists required to build final product including all alteration and other activities such as disassembly, assembly, quantity needed from each return and replenishment quantity of new commodity. A final product is matched to an inventory of return product based on the plan for the remanufacturing-oriented process to provide a configuration recommendation.