A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

et al. 2019

Self Cite

Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, nonrandom initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule. Index Terms-production scheduling; genetic algorithm; continuous manufacturing; multi-objective optimization; industrial internet of things

Section: A Continuous Production Schedulingmentioning

confidence: 99%

An efficient genetic method for multi-objective continuous production scheduling in Industrial Internet of Things

David

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

et al. 2019

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“…The power profile of each state is the aggregated power profiles of these three major sub-consumers. Readers are referred to (Gong et al, 2016c) for this empirical power data and state-based energy model.…”

Section: Power Consumption Datamentioning

confidence: 99%

“…Four idle modes remain to be scheduled in-between jobs: ProheatIdle, PreheatIdle, Idle, and Off (Gong et al, 2016c). An average power profile (2.54 kW during 11272 sec) was used to characterize the power consumption behavior of a bottle color changeover (Gong et al, 2016c).…”

Section: Empirical Scheduling Modelmentioning

confidence: 99%

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Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Gong

Wee

Journal of Cleaner Production

et al. 2017

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With the penetration of smart grid into factories, energyefficient production scheduling has been emerged. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, medium, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. 1Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: an empirical study AbstractWith the penetration of smart grid into factories, energy-efficient production scheduling has been emerged. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and laboraware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instan...

“…Besides powering off, three machine idle modes were observed by distinct power profiles, i.e., power level and breakeven duration (necessary time period for switching to and recovering from an idle mode). The collected power data and the empirical state-based energy model can be found in (Gong et al, 2016b). Three daily shifts are included: early shift (6 AM -2 PM), late shift (2 PM -10 PM), and night shift (10 PM -6 AM).…”

Section: Data and Experiments Settingmentioning

confidence: 99%

Energy-Efficient and Labor-Aware Production Scheduling based on Multi-Objective Optimization

Gong

Computer Aided Chemical Engineering

Martens

et al. 2017

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Manufacturing industry is a major energy consumer and greenhouse gas producer for the society. As recent literature points out, energy awareness can be integrated to production scheduling to enable industrial demand side management. Consequently, production loads can be shifted to periods with a lower electricity price for energy cost minimization. However, this may increase the overall production cost, because the labor compensation usually follows the opposite trend with the electricity price. To investigate this trade-off, this paper introduces a scheduling approach that considers both energy consumption and labor shifts. An efficient memetic algorithm is proposed for multi-objective optimization. Numerical experiments on a blow molding process demonstrated two general trade-offs: one between energy cost and labor cost, as well as one between overall production cost and makespan. Therefore, these trade-offs must be considered when performing energy-efficient production scheduling.