Deliveries optimization by exploiting production traceability information

Tamayo, Simòn; Monteiro, Thibaud; Sauer, Nathalie

doi:10.1016/j.engappai.2009.02.007

Cited by 29 publications

(18 citation statements)

References 17 publications

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“…As several principles of the evolution are based on random, the progress in results may vary from one population to another even for the same set of parameters. Once a type of production is categorised by the two main factors (quantity and number of batches) its optimal dispersion can be obtained using the genetic algorithm with the corresponding heuristic configuration, afterwards this value of optimal dispersion compared to the real dispersion value embodies an important indicator of the production's risk level, and becomes capital in future optimisations (Tamayo et al, 2009). Figure 6 shows the algorithm's behaviour for two different problems of type 1 and 4, for an optimal parameters configuration (obtained after the simulations).…”

Section: Discussion and Industrial Statementmentioning

confidence: 99%

Dispersion optimisation using a genetic algorithm

Tamayo

Monteiro

Sauer

2009

IJAOM

Self Cite

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The presented works concern the traceability phenomenon implementation within the production organisation, particularly in the field of raw materials management in the food industry. This project aims to minimise the raw material's dispersion in the manufactured products. We seek to solve the problem of raw materials allocation into finished products, in order to minimise its dispersion and moreover, the products recall if needed. The dispersion criteria are afterwards used to determine production's criticality in terms of sanitary risk, from which it is possible to optimise the processes of picking and dispatching. The final objective is to reduce the number of recalls in case of a crisis using the traceability information and the decision making tools. To solve the first part of the problem, a genetic algorithm is proposed.

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Section: Discussion and Industrial Statementmentioning

confidence: 99%

Dispersion optimisation using a genetic algorithm

Tamayo

Monteiro

Sauer

2009

IJAOM

Self Cite

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“…To reduce the size of the recalled lots, other authors have proposed to reduce the batch dispersion by reducing the size and the mixing of batches using linear programming (Dupuy et al, 2005) or genetic algorithms and neural networks (Tamayo et al, 2009). Dupuy et al define the notions of downward dispersion, upward dispersion and batch dispersion.…”

Section: Minimizing Of the Size Of The Recall Through The Traceabilitmentioning

confidence: 99%

Effective Use of Food Traceability in Product Recall

Diallo

Henry

Ouzrout

2016

Advances in Food Traceability Techniques and Technologies

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This chapter provides an overview of product recall process and highlights the need for traceability to achieve an effective product recall. The key tasks of a recall procedure are described and we explain how food traceability can be effectively use in product recall. We also explain how the recall process can be optimized by reducing the batch dispersion and root causes analysis through the traceability system. The link between the recall process and the different types of traceability according to the visibility and the management policy (Internal and external) and according to the level of detail of the traceability process (Unitary and batch traceability) is described.

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“…A better visibility and the verification of a product or raw materials within the supply chain contribute to the fight against counterfeiting [2]. The ability to track and trace enables the stakeholders to have authentic information about product distribution [3] and the origin of the identified products for recalling, which improves the efficiency of recall handling [4]. Moreover, with the traceability information related to the production process, such as the energy use, water use, the company can quantify the environmental footprint during the whole product life cycle, and further optimize the production process to reduce environmental impacts [5].…”

Section: Introductionmentioning

confidence: 99%

Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning

Wang¹,

Kumar²,

Zeng³

et al. 2019

IJCIS

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The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today's global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement. The traceability coding tag consists of unique features for identification, which links the product with traceability information, plays an important part in the traceability system. In this paper, we describe an innovative technique of product component-based traceability which demonstrates that product's inherent features-extracted using deep learning-can be used as a traceability signature. This has been demonstrated on textile fabrics, where Faster region-based convolutional neural network (Faster R-CNN) has been introduced with transfer learning to provide a robust end-to-end solution for coded yarn recognition. The experimental results show that the deep learning-based algorithm is promising in coded yarn recognition, which indicates the feasibility for industrial application.

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Deliveries optimization by exploiting production traceability information

Cited by 29 publications

References 17 publications

Dispersion optimisation using a genetic algorithm

Dispersion optimisation using a genetic algorithm

Effective Use of Food Traceability in Product Recall

Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning

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