Integration of lot sizing and safety strategy placement using interactive multiobjective optimization

Kania, Adhe; Sipilä, Juha; Misitano, Giovanni; Miettinen, Kaisa; Lehtmäki, Jussi

doi:10.1016/j.cie.2022.108731

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Experimental results show the performance of the optimal approach in computational time measurement. Kania et al [22] propose an integration model for lotsizing and safety strategy placement for handling demand uncertainty. The goal is to store a proper quantity of items to satisfy demand but concurrently avoid shortages and excess inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

Raw Material Purchasing Optimization Using Column Generation

Gao,

Li,

Wang

et al. 2024

Preprint

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The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

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Section: Literature Reviewmentioning

confidence: 99%

Raw Material Purchasing Optimization Using Column Generation

Gao,

Li,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Experimental results show the performance of the optimal approach in computational time measurement. Kania et al [21] propose an integration model for lot-sizing and safety strategy placement for handling demand uncertainty. The goal is to store a proper quantity of items to satisfy demand but concurrently avoid shortages and excess inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

Raw Material Purchasing Optimization Using Column Generation

Gao,

Li,

Wang

et al. 2024

Applied Sciences

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The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost, consisting of the price of raw materials, purchasing set-up costs, and inventory costs, and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch and bound algorithm while solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

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“…To deal with demand uncertainty, safety stock has been employed extensively. A level of item, also known as a stock keeping unit (SKU), known as safety stock is kept in a warehouse to manage unforeseen demand [15].…”

Section: Safety Stockmentioning

confidence: 99%

Inventory planning of raw material using Silver Meal and Wagner Whitin Algorithm

Asmal,

Kurniawan,

Indah

et al. 2023

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Material inventory planning is needed in the industrial world to advance a business because it will affect the costs incurred, the production process, and the profits generated by the company. The company has a problem that occurs when the control of raw material inventory could be more optimal. It recorded that throughout 2021, 33.647,18 meters of fabric pants were stored in warehouses. This is influenced by several factors, including the calculation of safety stock, lead time due to uncertain logistics and the selection of methods that have not been maximized. The novelty of this research is that it discusses inventory planning, the methods used are Silver Meal Algorithm and the Wagner Whitin Algorithm. Data processed is on demand for clothes and jeans throughout 2021. The results show that the Silver Meal is suitable for five materials, and the Wagner Whitin Algorithm is on one material. Companies can use both methods for each raw material. Nevertheless, it is more optimal if the company utilizes only one method for procurement of raw materials, the Silver Meal because when it is used in the long term, the cost of raw materials is less than the Wagner Whitin Algorithm. The Siler Meal’s total inventory cost is Rp. 138.281.497, and the Wagner Whitin Algorithm is Rp. 170.010.097

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Integration of lot sizing and safety strategy placement using interactive multiobjective optimization

Cited by 6 publications

References 36 publications

Raw Material Purchasing Optimization Using Column Generation

Raw Material Purchasing Optimization Using Column Generation

Raw Material Purchasing Optimization Using Column Generation

Inventory planning of raw material using Silver Meal and Wagner Whitin Algorithm

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