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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

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Front. Mech. Eng.    2019, Vol. 14 Issue (4) : 474-488    https://doi.org/10.1007/s11465-019-0560-z
RESEARCH ARTICLE
Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level
Jiali ZHAO1, Shitong PENG2(), Tao LI2, Shengping LV3, Mengyun LI2, Hongchao ZHANG2,4
1. School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. Institute of Sustainable Design and Manufacturing, Dalian University of Technology, Dalian 116024, China
3. College of Engineering, South China Agricultural University, Guangzhou 510642, China
4. Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
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Abstract

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.
Keywords remanufacturing scheduling      adaptive genetic algorithm      energy efficiency      sustainable remanufacturing      hormone modulation mechanism     
Corresponding Author(s): Shitong PENG   
Just Accepted Date: 11 September 2019   Online First Date: 15 November 2019    Issue Date: 02 December 2019
 Cite this article:   
Jiali ZHAO,Shitong PENG,Tao LI, et al. Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level[J]. Front. Mech. Eng., 2019, 14(4): 474-488.
 URL:  
https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0560-z
https://academic.hep.com.cn/fme/EN/Y2019/V14/I4/474
Fig.1  Comparison of three approximate maximum methods.
Fig.2  Configuration of the crankshaft remanufacturing process.
Equipment Processing capability Operation power/kW Idle power/kW Time duration/min
m1 Surface coarsening 1.50 0.9 (1.4, 2, 2.9)
m2 Spray coating 20.00 12.0 (0.8, 1, 1.6)
m3 Grinding 1.50 0.9 (8, 10, 12)
m4 Grinding 2.00 1.2 (7, 8, 11)
m5 Cleaning 42.76 / (2, 2.5, 3.1); (2.2, 3, 3.5)
m6 Polishing 2.50 1.1 (4.2, 5, 6.1)
m7 Polishing 3.00 1.4 (3.2, 4, 4.9)
m8 Dimension inspection / / (3.9, 5, 6.1)
m9 Dimension inspection / / (3.5, 4, 5.5)
Tab.1  Energy-rated information on workshop equipment
Fig.3  Implementation procedure of a GA for FJSSP.
Fig.4  Encoding method for the generation of the initial population.
Fig.5  Fuzzy Gantt chart based on chromosomes.
Fig.6  Curves of the Hill function under varying parameters: (a) Upward curves and (b) downward curves.
Fig.7  Multipoint crossover for the job sequencing string.
Fig.8  Gantt chart of an optimal solution in traditional form: (a) Optimistic situation, (b) pessimistic situation, and (c) most plausible situation.
Fig.9  Comparison of the convergent curves of four algorithms.
Algorithm Minimum energy consumption/(kW?h) Average energy consumption/(kW?h) Maximum energy consumption/(kW?h) Convergent generation Run time/s
GA (23.70, 30.55, 37.59) (23.79, 30.62, 37.67) (23.87, 30.65, 37.74) 31.25 13.96
AGA (23.71, 30.56, 37.55) (23.76, 30.59, 37.62) (23.83, 30.63, 37.67) 33.50 15.09
RKGA (23.68, 30.55, 37.53) (23.75, 30.58, 37.61) (23.81, 30.62, 37.66) 19.80 14.42
IAGA (23.66, 30.54, 37.52) (23.75, 30.58, 37.61) (23.80, 30.61, 37.65) 30.25 11.75
Tab.2  Computational results of four algorithms
Instance Type Problem 1 Problem 2 Problem 3
GA Average (0.403, 0.470, 0.567) (379.82, 409.86, 439.86) (54.96, 74.46, 90.59)
Optimal (0.358, 0.446, 0.542) (379.22, 408.94, 438.75) (53.72, 72.67, 88.18)
AGA Average (0.370, 0.456, 0.552) (379.58, 409.64, 439.62) (54.60, 73.92, 89.89)
Optimal (0.358, 0.446, 0.542) (378.97, 408.70, 438.46) (53.60, 72.54, 88.06)
RKGA Average (0.358, 0.446, 0.542) (379.54, 409.53, 439.48) (54.56, 73.85, 89.82)
Optimal (0.358, 0.446, 0.542) (378.97, 408.70, 438.46) (53.48, 72.50, 88.02)
IAGA Average (0.358, 0.446, 0.542) (379.68, 409.53, 439.42) (54.54, 73.70, 89.48)
Optimal (0.358, 0.446, 0.542) (378.97, 408.70, 438.46) (53.37, 72.33, 88.00)
Tab.3  Computational results on three problems
Operations Machines Operation power/kW Idle power/kW Time duration/min
1 #1 3.0 0.9 (2.5, 2.9, 3.4)
#2 2.5 0.7 (3, 3.5, 4)
2 #3 4.0 1.2 (6, 7.5, 8.5)
3 #4 4.5 1.2 (5, 6, 7)
#5 4.0 1.0 (5.5, 6.6, 7.5)
4 #6 7.0 2.1 (7, 8, 9)
5 #7 1.1 0.3 (5, 7, 9)
6 #8 6.5 2.3 (4.2, 5.8, 6.9)
7 #9 3.5 0.8 (1.5, 3.0, 4.5)
8 #10 10.0 2.5 (5, 8, 10)
9 #11 5.5 1.8 (2.5, 4.0, 5.5)
10 #12 7.5 2.5 (6, 8, 10)
11* #13 16.0 4.8 (4, 5, 6)
  Table A1 Processing data in Problem 3
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