Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

Dai, Min; Tang, Dunbing; Giret, Adriana; Salido, Miguel Á.; Li, W.D.

doi:10.1016/j.rcim.2013.04.001

Cited by 419 publications

(207 citation statements)

References 24 publications

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“…This study utilized two genetic algorithms for solving problems: the CGA for minimizing the makespan and calculating the electricity cost and EGA for determining low energy consumption scheduling and calculating the electricity cost concurrently [6]. The procedures of the proposed method are demonstrated as follows:…”

Section: The First Stage Of Esgamentioning

confidence: 99%

Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Huang¹,

Yu²,

Chen³

2017

JEP

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Many researches discussing reduced energy consumption for environmental protection focus on machine efficiency or process redesign. To optimize the machine operation time can also save the energy, and these researches have received great interests in recent years. This study considers three different states of machines, among processing there are two different speeds, to solve the problem of minimizing energy costs under time-of-use tariff with no tardy jobs in flexible flow shop. This problem is basically NP-hard, we proposed a hybrid genetic algorithm (GA) to solve problems in reasonable timeliness. The result shows that to optimize different states of machines under time-of use tariff can reduce energy costs significantly in on-time delivery.

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Section: The First Stage Of Esgamentioning

confidence: 99%

Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Huang¹,

Yu²,

Chen³

2017

JEP

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“…Equation (16) controls that, at most, one job is processed in an event point. Equation (17) controls that one machine is available at an event point only after its previous jobs are completed. Equation (18) ensures the completion time of an event point is equal to the sum of the start time and processing time.…”

Section: Indices Imentioning

confidence: 99%

“…Then Dai et al [17] applied this framework to the flexible flow shop scheduling problem, and Tang et al [18] adopted it to solve an energy-efficient dynamic scheduling. Since some machines and appliances cannot be switched off during the manufacturing process in some workshops [19], a new method of speed scaling framework has been developed by Fang et al [20].…”

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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“…To solve the issue, they proposed a turn-on/turn-off scheduling framework (which determines whether and when to turn off a machine or to keep it idle) to reduce the overall energy consumption of the machines. This framework has been utilized and extended in later research such as [5][6][7][8] for single machine scheduling or flow shop scheduling settings. Another research framework is based on machine speed scaling [9].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Zhang

2017

Sustainability

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Abstract:The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG). Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

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Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

Cited by 419 publications

References 24 publications

Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

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

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

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