Performance analysis of a decoupling stock in a Make-to-Order system

Cuypere, Eline De; Turck, Koen De; Fiems, Dieter

doi:10.3182/20120523-3-ro-2023.00177

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Pairing means that transmission is only possible if both queues are nonempty. These systems have been studied in various contexts including leaky-bucket access control [49,50], kitting processes [51,52] and decoupling buffers in production systems [53]. The present paper extends our preliminary findings presented in [54] which also studies an energy harvesting sensor node, but adopts a simplified energy expenditure model.…”

Section: Contributionsupporting

confidence: 57%

A queueing model of an energy harvesting sensor node with data buffering

2017

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Battery lifetime is a key impediment to long-lasting low power sensor nodes and networks thereof. Energy harvesting -conversion of ambient energy into electrical energy -has emerged as a viable alternative to battery power. Indeed, the harvested energy mitigates the dependency on battery power and can be used to transmit data. However, unfair data delivery delay and energy expenditure among sensors remain important issues for such networks. We study performance of sensor networks with mobile sinks: a mobile sink moves towards the transmission range of the different static sensor nodes to collect their data. We propose and analyse a Markovian queueing system to study the impact of uncertainty in energy harvesting, energy expenditure, data acquisition and data transmission. In particular, the energy harvesting sensor node is described by a system with two queues, one queue corresponding to the battery and the other to the data buffer. We illustrate our approach by numerical examples which show that energy harvesting correlation considerably affects performance measures like the mean data delay and the effective data collection rate.

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Section: Contributionsupporting

confidence: 57%

A queueing model of an energy harvesting sensor node with data buffering

2017

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“…Most of the literature has considered the impact of multiple factors on delayed production patterns. Cuypere et al [ 12 ] considered the delivery time and total cost, and used the Markov decision model to study the "decoupled inventory" problem. Kuthambalayan and Bera [ 34 ] considered the impact of delivery time and total profit and used a two-stage model to study the inventory of semi-finished products with delayed production.…”

Section: Data Mining To Analyze the Influencing Factors Of Delayed Pr...mentioning

confidence: 99%

“…Different products have different production processes [ 9 ] that are multi-layered [ 10 ], and the product series includes different types [ 11 ], which all need to consider multiple CODP or PDP location issues. In addition, semi-finished product inventory corresponding to CODP or PDP is important for delayed production control, including the impact of inventory volume on delayed production control [ 12 , 13 ] and the impact of inventory capacity constraints [ 14 , 15 ] on delayed production control. According to the literature on delayed production mode and actual situation of delayed production mode in iron and steel enterprises, optimization of delayed production mode in the process of production enterprises should simultaneously consider the content of delayed production control and inventory management.…”

Section: Introductionmentioning

confidence: 99%

A study on optimization of delayed production mode of iron and steel enterprises based on data mining

Shi

2023

PLoS ONE

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Delayed production mode has been adopted by an increasing number of process production enterprises as a method to realize mass customization of multi-products. This paper used the convolutional neural network-long short-term memory artificial neural network algorithm (C-LSTM) in data mining technology to analyze and determine factors that have an impact on delayed production mode in the internal and external production and operation of enterprises. Combined with the actual production situation of iron and steel enterprises, a quantitative model of the delayed production was constructed. Lastly, data from a large iron and steel enterprise with good operation was used to verify the validity of the proposed model and analyze key influencing factors. According to the research, in scenarios of considering PDP alone, considering CODP alone, considering both PDP and CODP, considering PDP and CODP and using data mining technology to model, the matching degree of these methods with the actual situation of the enterprise is 31.8%, 61.4%, 71.6% and 86.6%, respectively. The numerical analysis results of the model based on data mining technology show that in delayed production, when customer service level improves or the delay penalty coefficient increases, the optimal locations of the product differentiation point (PDP) and customer order decoupling point (CODP) move toward the end of production, and the total cost increases gradually. When the difference in production cost or benefit of early delivery between the candidate locations of PDP and CODP is small, optimal locations of PDP and CODP are close to the beginning of the general and dedicated production processes. With an increase of cost difference or early delivery benefit, the optimal locations of PDP and CODP jumped to the end stage of the general and dedicated production processes, and the total cost begins to decrease.

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Performance analysis of a decoupling stock in a Make-to-Order system

Cited by 2 publications

References 15 publications

A queueing model of an energy harvesting sensor node with data buffering

A queueing model of an energy harvesting sensor node with data buffering

A study on optimization of delayed production mode of iron and steel enterprises based on data mining

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