Parameterisation of demand-driven material requirements planning: a multi-objective genetic algorithm

Damand, David; Lahrichi, Youssef; Barth, Marc

doi:10.1080/00207543.2022.2098074

Cited by 16 publications

(3 citation statements)

References 34 publications

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“…The second topic is the study and improvement of the method itself. For example, Martin, Lauras, Baptiste, Lamothe, Fouqu and Miclo (2019) develop a process control and a decision-making tool to adjust buffer parameters, Lee and Rim (2019) propose an alternative model for the safety stock calculation, while Achergui, Allaoui and Hsu (2020) develop an algorithm to solve the optimization problem of minimizing storing costs for uncapacitated buffer positioning and (Damand, Lahrichi & Barth, 2023) propose a multi-objective genetic algorithm to determine a set of parameters related to DDMRP. Recently, Dessevre et al (2021) propose a visual tool to correlate service rate, resource utilization and DDMRP parameters, that helps to choose a capacity solution among others and Azzamouri, Baptiste, Pellerin and Dessevre (2022) analyze the impact of a periodic review of DDMRP stock buffers, while Cuartas and Aguilar (2023) develop a hybrid algorithm based on reinforcement learning to determine the optimal time and quantity to buy a product and Martin, Lauras and Baptiste ( 2023) propose an experimental design to compare different multi-parameter sizing policies.…”

Section: Publications About Ddmrpmentioning

confidence: 99%

Analysis of a new dynamic capacity management approach in DDMRP: Application on a real industrial case

Dessevre,

Benali

2024

JIEM

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Purpose: Although the authors of the Demand Driven Material Requirements Planning (DDMRP) argue that the method DDMRP is the solution to the limitations of traditional production management methods, its capacity management system remains unclear. Since DDMRP operates at infinite capacity, it is important to consider a capacity management approach to avoid under- or overloading production workshops.Design/methodology/approach: We propose a new dynamic capacity management approach for the DDMRP method. Our approach is based on the calculation of the anticipated workload, using DDMRP stock buffers and considering customer order spikes. Considering a real industrial case, we compare the proposed approach to a static one and a dynamic approach from the literature.Findings: The analysis of the results, supported by a two-way ANOVA, indicates that the proposed capacity management approach outperforms the performance of the other two approaches by maximizing the resource loading rate while ensuring a high customer service level.Originality/value: The originality of the article comes on the one hand from the capacity adjustment module by calculating the anticipated workload, and on the other hand from the comparison of this approach with two others, one of which comes from the literature.

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Section: Publications About Ddmrpmentioning

confidence: 99%

Analysis of a new dynamic capacity management approach in DDMRP: Application on a real industrial case

Dessevre,

Benali

2024

JIEM

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“…To achieve the above management objectives optimization, multi-objective optimization is necessary. The MOP method mainly includes traditional optimization methods such as linear weighting and intelligent optimization algorithms such as multi-objective GA [18]. Among them, GA has higher global optimization capability, and this algorithm can solve MOP.…”

Section: Research On Engineering Project Mop Model Based On Nsga -ⅱ A...mentioning

confidence: 99%

Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction

Zhang

2023

Decis. Mak. Appl. Manag. Eng.

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In the context of Sustainability Development (SD), Green Construction (GC) has become a key direction for optimizing engineering project objectives. In order to improve the management ability of project engineering in GC, an improved NSGA - II algorithm was used in this study to establish a multi-optimization model for engineering projects. In this process, the hill climbing is introduced to improve the search ability of NSGA - Ⅱ algorithm. Finally, a Multi-Objective Optimization (MOP) model with strong convergence and distribution was obtained. In subsequent validation experiments, the total construction period of the engineering project MOP model based on the improved NSGA - II algorithm was between 190 and 234days. The total cost ranges from 171,473 to 20,461,800 yuan. Its total mass ranges from 90.41% to 92.19%. Its total safety is between 91.30% and 99.32%. The total environment is between 144.54 and 193.58. Its total resources range from 86.21% to 99.91%. The cost of improving the NSGA-II algorithm is 500300 yuan lower than that of the NSGA-II algorithm, with a resource target increase of 0.4% and an environmental target increase of 4.33%. The iteration curves of the improved NSGA - II algorithm in terms of duration, cost, and environmental objective function are lower than those of the NSGA - II algorithm. Overall, the improved NSGA - II algorithm has better MOP performance, can obtain better Pareto solutions, and has better performance.

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“…Lahrichi et al [28] suggested the use of the MILP model in determining strategic reservoir parameters. In their publications [29,30], authors Damand et al presented a procedure for applying genetic algorithms to determine DDMRP system parameters. In their publication, Martin et al [31] compared several approaches to sizing DDMRP system parameters from different authors and verified the results using dynamic simulation.…”

Section: Introductionmentioning

confidence: 99%

Parameter Setting for Strategic Buffers in Demand-Driven Material Resource Planning through Statistical Analysis and Optimisation of Buffer Levels

Krajčovič,

Gabajová,

Gašo

et al. 2024

Applied Sciences

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The Demand-Driven Material Resource Planning (DDMRP) method is one of the newer methods of inventory management in an enterprise. Its creation was initiated by a change in the business environment and the characteristics of today’s supply chains. DDMRP brings a combined pull/push approach to inventory management based on creating strategic stacks in the supply chain and managing inventory at these strategic points based on customer orders. The DDMRP system provides a simple methodology that is easy to apply, even in smaller businesses, without the need for advanced information systems. However, a simple methodology also has its limitations because, in many cases, intuitive and subjective approaches are used to set inventory management parameters (variability factor, running time factor, seasonality factor, thresholds, etc.). Simplified parameter determination may, under certain conditions, lead to some storage tanks being too high or too low for certain periods of time. We know from classical inventory management, in the conditions of setting stack parameters in DDMRP, that the deficiency can be eliminated by the use of statistical–analytical approaches and optimisation techniques. This article deals with the issue of setting optimal values of storage tanks in DDMRP, while the correctness of the methodology is verified through simulation of the demand-driven planning process. The correctness and usability of the proposed approaches in sizing strategic reservoirs in DDMRP was confirmed through the results of stimulation experiments.

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Parameterisation of demand-driven material requirements planning: a multi-objective genetic algorithm

Cited by 16 publications

References 34 publications

Analysis of a new dynamic capacity management approach in DDMRP: Application on a real industrial case

Analysis of a new dynamic capacity management approach in DDMRP: Application on a real industrial case

Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction

Parameter Setting for Strategic Buffers in Demand-Driven Material Resource Planning through Statistical Analysis and Optimisation of Buffer Levels

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