Development of an advanced application process of Lean Manufacturing approach based on a new integrated MCDM method under Pythagorean fuzzy environment

Soltani, Mohyiddine; Aouag, Hichem; Cherrafi, Anass; Mouss, Mohamed Djamel

doi:10.1016/j.jclepro.2022.135731

Cited by 12 publications

(7 citation statements)

References 67 publications

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“…Analytically, the proposed mechanism can be applied to a plethora of manufacturing environments and research problems with minimal modifications, due to the nature of implemented amalgamation of reinforcement learning and parametric control policies. Furthermore, due to the employment of machine learning and lean green manufacturing strategies, it is clear that the presented approach facilitates the creation of smart lean and sustainable production environments in contrast to other methodologies, e.g., VSM [47]. It can dynamically assess the manufacturing environment and tackle in realtime emerging issues, e.g., manufacturing waste, with joint policies related to operations, e.g., raw material procurement.…”

Section: Discussionmentioning

confidence: 99%

“…For this effort, it incorporates a variety of decision-making agents, specializing on discrete aspects of manufacturing environments, such as coordination of operations or resource management. Following a sophisticated approach, Soltani et al [47] presented a lean manufacturing approach that assess the manufacturing sustainability of plants with VSM and suggests solutions for minimizing generated waste through multi-criteria decision analysis methods, e.g., TOPSIS.…”

Section: Sustainable Productionmentioning

confidence: 99%

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Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

Paraschos,

Koulinas,

Koulouriotis

2024

Algorithms

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The manufacturing industry often faces challenges related to customer satisfaction, system degradation, product sustainability, inventory, and operation management. If not addressed, these challenges can be substantially harmful and costly for the sustainability of manufacturing plants. Paradigms, e.g., Industry 4.0 and smart manufacturing, provide effective and innovative solutions, aiming at managing manufacturing operations, and controlling the quality of completed goods offered to the customers. Aiming at that end, this paper endeavors to mitigate the described challenges in a multi-stage degrading manufacturing/remanufacturing system through the implementation of an intelligent machine learning-based decision-making mechanism. To carry out decision-making, reinforcement learning is coupled with lean green manufacturing. The scope of this implementation is the creation of a smart lean and sustainable production environment that has a minimal environmental impact. Considering the latter, this effort is made to reduce material consumption and extend the lifecycle of manufactured products using pull production, predictive maintenance, and circular economy strategies. To validate this, a well-defined experimental analysis meticulously investigates the behavior and performance of the proposed mechanism. Results obtained by this analysis support the presented reinforcement learning/ad hoc control mechanism’s capability and competence achieving both high system sustainability and enhanced material reuse.

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Section: Discussionmentioning

confidence: 99%

Section: Sustainable Productionmentioning

confidence: 99%

Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

Paraschos,

Koulinas,

Koulouriotis

2024

Algorithms

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“…Due to several stricter protocols, this approach is crucial to dealing with environmental concerns such as managing the generated wastewater and controlling the emission of pollutants and chemical dust. Soltani et al (2023) proposed the integration of sustainable manufacturing in an iron industry to analyze the interrelationship among the causes of waste from a sustainability viewpoint and prioritize a set of solutions. There is a need to plan to adopt sustainable manufacturing approaches while balancing the lack of various resources, market competitiveness and the increase of global apprehension.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sustainable manufacturing has emerged as a way to integrate sustainable development into industrial processes, and organizations need to prioritize their development to enhance their sustainability performances (Soltani et al, 2023). Sustainable manufacturing practice is one of SLM for the long term…”

Section: Introductionmentioning

confidence: 99%

Sustainable lean manufacturing as long-term strategy: performance framework development and prioritization

Gama,

Bonamigo

2023

JSMA

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PurposeIn response to mounting global concerns about climate change and scarcity of natural resources, manufacturers have been pressured to develop strategies and enhance their sustainability performance. The integration of sustainable lean manufacturing (SLM) during value chain processes could balance environmental, social and economic concerns into their decision-making, which not only ensures responsible practices but also drives efficiency and success. This paper aims to identify, measure and prioritize metrics to develop a performance measurement system that assesses the multi-dimensional performance of SLM.Design/methodology/approachStrategic decision-making has some conflicting criteria and objectives to be considered simultaneously. The Multi-Criteria Decision Making provides a foundation for selecting, sorting and prioritizing these strategies with the determination of drivers and indicator weight.FindingsThe performance model enables the decision-makers to consistently evaluate the level of sustainability through a multidimensional framework, which could support the assessment of the existing sustainability of a manufacturing process and analyze opportunities for improvement. This study divided the performance into five drivers: Quality, Operational, Finance, Environment, Safety and People and selected 17 KPIs for assessing the multi-dimensional performance of SLM organizations. The research results revealed an organization's perspective transition from strategies focused on operational and economic performance to a more sustainable ideal with greater importance for social and environmental directions.Originality/valueThis framework will be facilitated by the selection of the most significant drivers and the development of strategic plans for the successful adoption of sustainable manufacturing. The practices support implementation, pursue competitive advantages and sustain manufacturing, meeting strategic requirements of suitable and lean performance. With the limited resources of the organizations, the framework proposed will guide the priorities and actions to be taken toward the SLM.

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“…In [ 45 ], Sharma et al introduced a multi-objective bi-level chance-constrained optimization to solve a company’s production planning problem in intuitionistic fuzzy environment. In Pythagorean fuzzy environment, Soltani et al [ 46 ] developed a Pythagorean fuzzy MADM model to enhance the application process of traditional lean manufacturing. Besides, in [ 47 ], Fei and Feng introduced a dynamic multi-attribute decision framework based on PFSs that fuses decision information from different periods for addressing the effect of the time factor on MADM results.…”

Section: Introductionmentioning

confidence: 99%

A new multi-attribute decision making approach based on new score function and hybrid weighted score measure in interval-valued Fermatean fuzzy environment

Qin

Peng

et al. 2023

Complex Intell. Syst.

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Interval-valued Fermatean fuzzy sets (IVFFSs) were introduced as a more effective mathematical tool for handling uncertain information in 2021. In this paper, firstly, a novel score function (SCF) is proposed based on IVFFNs that can distinguish between any two IVFFNs. And then, the novel SCF and hybrid weighted score measure were used to construct a new multi-attribute decision-making (MADM) method. Besides, three cases are used to demonstrate that our proposed method can overcome the disadvantages that the existing approaches cannot obtain the preference orderings of alternatives in some circumstances and involves the existence of division by zero error in the decision procedure. Compared with the two existing MADM methods, our proposed approach has the highest recognition index and the lowest error rate of division by zero. Our proposed method provides a better approach to dealing with the MADM problem in the interval-valued Fermatean fuzzy environment.

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Development of an advanced application process of Lean Manufacturing approach based on a new integrated MCDM method under Pythagorean fuzzy environment

Cited by 12 publications

References 67 publications

Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

Sustainable lean manufacturing as long-term strategy: performance framework development and prioritization

A new multi-attribute decision making approach based on new score function and hybrid weighted score measure in interval-valued Fermatean fuzzy environment

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