Energy-aware manufacturing operations

Prabhu, Vittaldas V.; Trentesaux, Damien; Taisch, Marco

doi:10.1080/00207543.2015.1100766

Cited by 31 publications

(16 citation statements)

References 47 publications

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“…Labor and material productivities in manufacturing have been studied in-depth during recent decades, while energy productivity has been largely neglected. 10 Energy productivity has been studied intensely at the grid level 11 and in the residential sector 12,13 by considering contributions of DR programs and renewable sources. This has changed lately because of climate change and global warming concerns, which have attracted the attention of scholars to energy productivity in sustainable manufacturing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…10 Adopting electricity DR programs leads to an ECA manufacturing system because manufacturers are provided with the opportunity to take advantage of lower prices during off-peak hours. 10 Time-of-use (TOU) pricing, critical peak pricing (CPP), and real-time pricing (RTP) are 3 time-based DR programs commonly available to industrial customers. Day-ahead purchase commitment is also offered alongside RTP by some utilities.…”

Section: Introductionmentioning

confidence: 99%

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Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Khalaf

Wang

2018

Int J Energy Res

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Summary The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Khalaf

Wang

2018

Int J Energy Res

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“…If ik z V represents the total removed volume from the semi-product of job i on lathe k, the cutting time can be calculated by Equation (9), and combined with Equation (8), it conducts Equation (10).…”

Section: Klmentioning

confidence: 99%

“…After long-term endeavors, the first method has maintained a good momentum on the single equipment with manufacturing process improvement, material savings, and waste reduction [7][8][9]. As for the second method, energy-saving through optimization the manufacturing system faces great challenges due to changeable market demand, diversified product structure, and flexible processing routes [10][11][12], as well as insufficient accurate data of energy consumption [13,14], so researchers still struggle to break down these technical and theoretical barriers.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Wang

Rao

Zhang

et al. 2016

Preprint

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With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

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“…Since the last decade, researchers have included sustainable concepts at lower manufacturing levels (Garetti and Taisch 2012;Herrmann et al 2014;Prabhu et al 2015). Nevertheless, from a preliminary review, very few studies have focused on the inclusion of sustainable concepts in the manufacturing control domain.…”

Section: Introductionmentioning

confidence: 99%

A dynamic hybrid control architecture for sustainable manufacturing control

et al. 2016

Self Cite

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Manufacturing systems face the challenge of accomplishing the productive effectiveness and sustainable efficiency goals at operational level. For this, manufacturing control systems must incorporate a mechanism that balances the trade-off between effectiveness and efficiency in perturbed scenarios. This paper proposes a framework of a dynamic hybrid control that manages and balances the trade-off between effectiveness and efficiency objectives. Our proposal integrates this trade-off in three different locations: the predictive-offline scheduling component, the reactive-online control component, and the switching mechanism that changes dynamic architecture. To show the contribution of our approach and the progress of our research, a case study dealing with energy-aware manufacturing control is presented.

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Energy-aware manufacturing operations

Cited by 31 publications

References 47 publications

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

A dynamic hybrid control architecture for sustainable manufacturing control

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