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dc.contributor.advisorDuong, Vo Nhi Anh
dc.contributor.authorNguyen, Thi Minh Anh
dc.date.accessioned2024-03-21T09:49:05Z
dc.date.available2024-03-21T09:49:05Z
dc.date.issued2022
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/5198
dc.description.abstractMaterial Requirements Planning (MRP) plays a vital role in managing manufacturing facilities. The problem is determining the production loading plan consisting of the quantity of production and inventory level - fulfilling future demand. This paper investigates two-stage stochastic optimization methods for Material Requirements Planning (MRP) systems under demand uncertainty in the automotive industry. First, this study addresses the demand forecast by applying the decomposition model and compared with other forecasting methods. Although forecast models could be used to improve forecast accuracy, error and uncertainty still exist. To deal with this uncertainty, a two-stage stochastic scenario-based production planning model is developed to maximize the net profit received at the end of the planning horizon. A parametric analysis is used to derive managerial insights related to three factors: total cost, unexpected loss, and revenue. Specifically, the first stage determines the production quantity needed to assemble cars while demand is unknown at period 0. When a possible realization of demand is revealed, the second stage variables represent the over-time production quantity, inventory level, backorder level, defective rate, and planned order receipts. In addition, the model also considers Order-up-to level method to tighten inventory control and re-balance stock on hand. The model is solvediv with data from a local manufacturing facility, and the results are compared with deterministic production models to show the effectiveness of the developed stochastic model. As a result, implementing the two-stage stochastic optimization model should be considered in MRP systems since this method would allow manufacturers to determine a plan that yields an optimal trade-off between the profit and production costs in this complex environmenten_US
dc.language.isoenen_US
dc.subjectStochastic optimizationen_US
dc.titleA Stochastic Model For Material Requirement Planning: A Case Of Assembly Productionen_US
dc.typeThesisen_US


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