A Hybrid Nested Partitions Method for Bi-objective Job Shop Scheduling Problem Considering Energy Consumption and Makespan

2018 ◽  
pp. 563-571
Author(s):  
Mei Dai ◽  
Xin Li ◽  
Limin Mao
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Nested Partitions ◽  
Shop Scheduling

An improved SFLA for flexible job shop scheduling problem considering energy consumption

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840112 ◽  
Author(s):  
Xiaoxing Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

scholarly journals Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Mathematics ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. 220 ◽  
Author(s):  
Tianhua Jiang ◽  
Chao Zhang ◽  
Huiqi Zhu ◽  
Jiuchun Gu ◽  
Guanlong Deng
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Whale Optimization ◽  
Energy Efficient Scheduling

Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

scholarly journals Adaptive Population NSGA-III with Dual Control Strategy for Flexible Job Shop Scheduling Problem with the Consideration of Energy Consumption and Weight

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 344
Author(s):  
Mingliang Wu ◽  
Dongsheng Yang ◽  
Bowen Zhou ◽  
Zhile Yang ◽  
Tianyi Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Problem Model ◽  
Shop Scheduling ◽  
Set Up

The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model. The weight property of jobs is also considered in our model. The heavier the job, the more energy it consumes during the transport, set up, and unload stage. Meanwhile, this paper invents an adaptive population non-dominated sorting genetic algorithm III (APNSGA-III) that combines the dual control strategy with the non-dominated sorting genetic algorithm III (NSGA-III) to solve our flexible job shop scheduling problem model. Four flexible job shop scheduling problem instances are formulated to examine the performance of our algorithm. The results achieved by the APNSGA-III method are compared with five classic multi-objective optimization algorithms. The results show that our proposed algorithm is efficient and powerful when dealing with the multi-objective flexible job shop scheduling problem model that includes energy consumption.

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