Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

2017 ◽  
Author(s):  
Qiong Liu ◽  
Youquan Tian ◽  
Chao Wang ◽  
Freddy O. Chekem ◽  
John W. Sutherland
Keyword(s):  
Carbon Footprint ◽  
Manufacturing System ◽  
Mixed Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop Scheduling ◽  
Carbon Footprints ◽  
Shop Scheduling ◽  
Product Carbon Footprint ◽  
Proposed Model

In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.

Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Qiong Liu ◽  
Youquan Tian ◽  
Chao Wang ◽  
Freddy O. Chekem ◽  
John W. Sutherland
Keyword(s):  
Carbon Footprint ◽  
Manufacturing System ◽  
Mixed Model ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop Scheduling ◽  
Carbon Footprints ◽  
Shop Scheduling ◽  
Product Carbon Footprint ◽  
Proposed Model

In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint-oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Nondominated sorting genetic algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.

A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint

2017 ◽  
Vol 168 ◽  
pp. 668-678 ◽  
Author(s):  
Qiong Liu ◽  
Mengmeng Zhan ◽  
Freddy O. Chekem ◽  
Xinyu Shao ◽  
Baosheng Ying ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Fruit Fly ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling

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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