The role of reverse logistics in the transition to a circular economy: Case study of automotive spare parts logistics

Makarova, Irina; Shubenkova, Ksenia; Buyvol, Polina; Shepelev, Vladimir; Гриценко, А. В.

doi:10.5937/fme2101173m

Cited by 29 publications

(7 citation statements)

References 34 publications

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“…Common methods for analyzing system reliability are failure tree analysis [1], logicalprobabilistic method [2][3], failure mode and effect analysis [4], multivariate failure analysis [5][6], fuzzy logic [7], finding of failure distribution functions [8], instrumental diagnostics methods [9][10], for example, based on vibration level measurement [11][12]. However, limitations of the first three methods are the lack of deep systemic knowledge about the design and ongoing processes of the system under consideration.…”

Section: Theory: Methods Used In the Analysis Of Vehicle Reliabilitymentioning

confidence: 99%

Application of intelligent analysis to identify defective vehicle components

Voroshilov

Buyvol

2021

MATEC Web Conf.

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The article shows the possibility of using intelligent analysis in a vehicle service when assessing the vehicle reliability. It was hypothesized that the use of association rules in diagnostics can increase the speed of repairs and the quality of customer service, allowing to identify the nodes that are highly likely to be faulty at the same time. For this, a knowledge base was built from the patterns obtained by applying association rules to the vehicle failure statistics. An application was implemented, which, on its basis, issues recommendations to the repair worker to check certain nodes based on the already identified defective nodes entered into the program. The proposed technique, together with the developed software tool, will optimize the diagnostic processes.

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Section: Theory: Methods Used In the Analysis Of Vehicle Reliabilitymentioning

confidence: 99%

Application of intelligent analysis to identify defective vehicle components

Voroshilov

Buyvol

2021

MATEC Web Conf.

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“…A case study was conducted to validate the classification and policy assignment methodology. This study was conducted in a manufacturing company that produces electronic components for the automotive indus-try, a sector where the logistics of spare parts may be particularly complex [30].…”

Section: Case Studymentioning

confidence: 99%

Spare parts stock management: Classification and policy assignment

Teixeira,

Lopes,

Figueiredo

2024

FME Transactions

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Spare parts management is a maintenance management function that aims to support maintenance activities. It intends to provide real-time information about the available quantities for each spare part and to adopt stock management policies that assure its availability at the lowest possible cost. This paper proposes a methodology for the stock management of spare parts that aims to assign stock management policies to each spare part in an industrial plant. Five groups and five associated stock management policies were defined. Considering that the policy to be allocated depends strongly on the characteristics of demand, two of the groups are intended for generic spare parts, and the other three for specific spare parts. The definition of the groups and the assignment method took into account the associated stock management policy, which in turn took into account the characteristics of the demand for the associated parts. A case study carried out in a manufacturing company that produces electronic systems for the automotive sector showed that the developed methodology allows, simply and intuitively, the assignment of policies that balance the cost of a part being out of stock and the cost of keeping it in stock, thus minimizing total cost.

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“…The supply chain is responsible for transforming primary materials into the final products or services we consume. Transportation is a crucial phase in supply chains, accounting for approximately 49% of logistics activities [1]. Optimizing transport routes can save up to 30% of freight costs [2].…”

Section: Introductionmentioning

confidence: 99%

An end-to-end Approach to a Reinforcement Learning in Transport Logistics

Ramón Gómez,

El-Hajj

2023

2023 16th International Conference on Signal Processing and Communication System (ICSPCS)

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The use of machine learning and reinforcement learning techniques has become increasingly important in enhancing the performance of transportation in supply chains. These techniques allow for real-time adaptation to changing conditions and optimization of decision-making, resulting in more efficient and cost-effective transportation routes. By incorporating machine learning and reinforcement learning, companies can improve their overall supply chain management and competitiveness in today's fast-paced business environment. In this paper, we proposed a multi-mode transportation and route planning using Reinforcement Learning (RL) algorithm. The algorithm showed good performance in multi-modal routing and transport selection based on cost functions through the evaluation of three trained agents in 100 different environments. However, a comparison with the Dijkstra algorithm revealed sub-optimal decisions with higher costs. Further training is needed to fully define the optimal policy, with the dynamic environment being a challenge

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The role of reverse logistics in the transition to a circular economy: Case study of automotive spare parts logistics

Cited by 29 publications

References 34 publications

Application of intelligent analysis to identify defective vehicle components

Application of intelligent analysis to identify defective vehicle components

Spare parts stock management: Classification and policy assignment

An end-to-end Approach to a Reinforcement Learning in Transport Logistics

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