An Iterative Heuristic Optimization Model for Multi-Echelon (R, Q) Inventory Systems
نویسندگان
چکیده
Large multi-echelon inventory systems usually consist of hundreds of thousands of stock keep units (SKU). Calculating inventory policies for each product is a computational burden that necessitates the need for more efficient policy setting techniques that reduce computational time and increases managerial convenience. The main objective of our research is to investigate the effect of segmentation (grouping) methodologies on multi-echelon inventory systems. A backorder multi-echelon constrained optimization model is developed that minimizes the expected total system costs. An overview of the research methodology is presented as well as initial results. We also discuss future work intended to examine segmentation policies within this context
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