An improved electromagnetism-like mechanism algorithm for energy-aware many-objective flexible job shop scheduling

2022 ◽  
Author(s):  
Minghao Qu ◽  
Ying Zuo ◽  
Feng Xiang ◽  
Fei Tao
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Energy Aware ◽  
Shop Scheduling

scholarly journals A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hua Zhang ◽  
Ziwei Dai ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yan Wang ◽  
...  
Keyword(s):  
Job Shop ◽  
Planning Process ◽  
Job Shop Scheduling ◽  
Harmony Search ◽  
Electricity Consumption ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Energy Aware ◽  
Shop Scheduling

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).

scholarly journals Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 210 ◽  
Author(s):  
Hongliang Zhang ◽  
Haijiang Ge ◽  
Ruilin Pan ◽  
Yujuan Wu
Keyword(s):  
Material Handling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Sustainable Manufacturing ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Energy Aware ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Level Model

The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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