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3832. A Generalized MILP Model based Co-Clustering Approach: An Application in Cellular Manufacturing
Invited abstract in session MD-49: Lot-sizing with industrial applications II, stream Lot Sizing, Lot Scheduling and Production Planning.
Monday, 14:30-16:00Room: M1 (building: 101)
Authors (first author is the speaker)
| 1. | Rahul Prasad
|
| Management Studies, IIT Madras | |
| 2. | Rajendran C
|
| IITMadras |
Abstract
Efficient machine-part cell formation is crucial for modern manufacturing systems, ensuring high throughput, reduced setup times, and increased flexibility. This paper introduces a novel 2-phase MILP-based Co-clustering model tailored for optimal cell formation in manufacturing systems. Addressing both binary and ratio (workload) input datasets, the model introduces the noble similarity coefficient yielding better results. The objective is to provide an efficient and versatile solution, with a particular emphasis on minimizing inter-cell workloads and reducing idle time within the cells. Unlike probabilistic methods, our model guarantees a deterministic, globally optimal grouping of machines and parts using a mathematical model. The model is successfully applied to over 50 benchmark datasets of binary and workload drawn from Cellular Manufacturing Systems. The model demonstrates superior grouping efficacy and lower time complexity compared to existing methods. Furthermore, a streamlined heuristic follows the mathematical model, enabling efficient machine or part reallocation, further improving the model's efficiency.
Keywords
- Programming, Mixed-Integer
- Mathematical Programming
- Manufacturing
Status: accepted
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