Integrated resource leveling and material procurement with variable execution intensities

Kazemi, Sajad; Davari-Ardakani, Hamed

doi:10.1016/j.cie.2020.106673

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…They propose a genetic algorithm (GA) for the solution of the problem. Kazemi and Davari-Ardakani [24] proposed a multi-objective model that integrates project scheduling and raw material purchasing simultaneously and solved it with NSGA-II and Taguchi. Kannan et al [14] proposed a multi-criteria decision-making (MCDM) approach called Fuzzy Axiomatic Design (FAD) to select the best green supplier for a Singaporebased plastic manufacturing company for purchase and supply cycle to ensure the supplier of goods and meeting of green criteria.…”

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

show abstract

“…They propose a genetic algorithm (GA) for the solution of the problem. Kazemi and Davari-Ardakani [23] proposed a multi-objective model that integrates project scheduling and raw material purchasing simultaneously and solved it with NSGA-II and Taguchi. Kannan et al [13] proposed a multi-criteria decision-making (MCDM) approach called fuzzy axiomatic design (FAD) to select the best green supplier for a Singapore-based plastic manufacturing company for purchase and supply cycle to ensure the supplier of goods and meeting of green criteria.…”

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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“…Applying the new technique to resource management is a prerequisite for construction contractors in today's complex construction environment [4]. An appropriate method for resource management could determine the high level of project success to avoid project delay and cost overrun [5,6].…”

Section: Introductionmentioning

confidence: 99%

Robust forensic-based investigation algorithm for resource leveling in multiple projects

Tran¹,

Le²,

Nguyen³

et al. 2022

Scientia Iranica

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The project managers often face challenging due to a scarcity of resources in construction management. Levelling the used resources in multiple projects is a frequently encountered problem in construction areas and manufacturing sectors. This study proposes a robust forensic-based investigation (FBI) algorithm for resource leveling in multiple projects with considerations of different objective functions of resource graphs. The fuzzy c-means clustering approach is fused into the main operation of the FBI to enhance the rate of convergence by utilizing population information. The scheduling examines different objective functions for optimizing resource profile selection. Two application case studies are used to demonstrate the performance of the improved optimization algorithm in dealing with the resource-leveling problem in multiple projects. Experimental findings and statistical comparisons demonstrated that the developed FFBI could acquire high quality solutions and surpass those of compared optimization algorithms.

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Integrated resource leveling and material procurement with variable execution intensities

Cited by 12 publications

References 33 publications

Raw Material Purchasing Optimization Using Column Generation

Raw Material Purchasing Optimization Using Column Generation

Raw Material Purchasing Optimization Using Column Generation

Robust forensic-based investigation algorithm for resource leveling in multiple projects

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