A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems
Abstract
:1. Introduction
- How can supervised machine-learning models be employed to find an efficient method for supplier classification, categorizing suppliers based on specific tasks?
- What type of mathematical model can be developed considering the optimization of various conflicting objectives such as completion time, energy consumption, interoperability, machine utilization rate, service utilization, and utility?
- In what way can evolutionary algorithms be utilized to optimize scheduling and planning?
- What are the benefits of the proposed approach on the considered problem, and how would these effects influence the manufacturing system in a real-time environment?
- How can the effectiveness of the proposed Hybridized evolutionary (HMFO) algorithm be validated?
- Proposing an integrated text-mining assisted process planning framework for distributed manufacturing systems;
- Employing a machine-learning-based text-mining method to identify the potential enterprises and sharing of resources effectively across the network and tested its feasibility with various other machine-learning algorithms;
- A multi-objective evolutionary algorithm-based Hybridized Moth Flame Optimization Algorithm (HMFO) is used to solve the considered problem in the scenario of distributed gear manufacturing industries;
2. Literature Review
3. Problem Description
- Job pre-emption is prohibited.
- Until the previous job is completed, the successive job cannot be processed.
- Only one job can be processed in an enterprise at a time.
- The reliability of a machine with respect to time is constant, and its value is the same for that particular machine for every operation while processing a job.
- At time t = 0, all machines and jobs are concurrently available.
- The operations of every job and its respective sequence consisting of future processing tasks need to be pre-defined.
- Xvp 1 The pth alternative process plan of job v is selected
- 0 Under other conditions
- Yvkpwtur 1 The operation Qvkp preceding over the operation Qwtu on given machine r
- 0 Under other conditions
- Zvkpr 1 If given machine r is selected for Qvkp
- 0 Under other conditions
3.1. Objectives
3.2. Subject to Constraints
4. A Framework of the Proposed Classifier-Assisted Evolutionary Algorithm Approach
5. Experimentation Part Text-Mining
5.1. Task-Specific Supplier Classification through Supervised Machine-Learning Algorithms Based on Text Mining
- Step 1.
- Creation of Supplier Corpus.
- Step 2.
- Pre-Processing of Text Corpus and Creation of Document Term Matrix.
- Step 3.
- Classification into Task-Specific Suppliers.
5.2. Proposed Multi-Objective Evolutionary Algorithms
- Step 1.
- In HMFO, potential solutions are represented as moths and variables are represented as position in the moth space. A matrix consists of all the moths (n), and their dimension is d.
M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M9 | M10 | M11 | M12 | |
O1 | [12 | 18 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O2 | 14 | 9 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O3 | 11 | 16 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0]; |
- Step 2.
M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M9 | M10 | M11 | M12 | |
O1 | [175 | 312 | 198 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O2 | 400 | 234 | 330 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O3 | 325 | 429 | 352 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0; |
O5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0]; |
- Step 3.
- A score function is defined that helps to select a suitable process plan, and it is shown in Equation (12), where a higher score value indicates that the probability of selecting the process plan is lesser. The lower the score value, the better the process plan. The formula for the score function is shown below.
- Step 4.
- A matrix CL is formed considering all the moths into the objective functions that are stored in FK represented below.
- Step 5.
- After an appropriate process plan is selected, we consider the rows as individual light sources through which we find the minimum values.
- Step 6.
- The matrices are explored row-wise to find a minimum entry in their respective rows once the required inputs are received and the search space is clearly initialized.
- Step 7.
- Moths maintain the best solution by updating its position Equation (13) by moving around the flag that is dropped by themselves during the search process. Update the position of the moth with respect to one flame. The spiral motion follows the Equation (13) represented as
- Step 8.
- After finding the minimum entry in the summed matrix and converting all ∞’s to 0s, the sum of all the values is found in their respective objective function matrices.
- Step 9.
- Finally, we solve the function to generate optimal values for all objectives.
6. Discussion and Results
6.1. Validation of Proposed HMFO Algorithm with the Experimental Instances
6.2. Evolution of Proposed HMFO with Practical Instances
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Notation | Description |
---|---|
E | The number of all the available jobs |
G | The number of all the available machines |
Hv | The number of all the available alternative process plans of job v. |
Qvpk | pth alternative process plan for kth operation of job v |
Svp | The number of all the available operations in the pth alternative process plan of the job v |
L | Maximum completion time of vth job from the all the available process plans |
Dvkpr | For operation Qvkp corresponding processing time of the on machine r |
B | An arbitrary Integer which is a very large positive integer. |
Cv | The completion time till the processing of job v |
Cvkpr | The earliest completion time till the operation Qvkp on machine r |
Evkr | Indicates energy consumption for processing kth operation of job v on machine r |
Relvk | Indicates reliability of the kth operation of job v |
Category 1 | Bevel gear |
Category 2 | Helical and worm |
Category 3 | Helical gear |
Category 4 | Spur gear |
Category 5 | Worm gear |
Category 6 | All types of gear |
Types of machines (CNC, LATHE) |
Types of operations (milling, drilling, and grinding) |
Types of gears (spur, helical, and bevel) |
Types of materials (steel, aluminum, bronze, and brass) |
Types of certifications (ISO 9000, ISO 14000 |
Types of manufacturing process (casting, forging, and extrusion) |
Decision Tree (J48) | ||||||||
TP Rate | FP Rate | Precession | Recall | F-Measure | MCC | ROC Area | PRC Area | Class |
0.400 | 0.000 | 1.000 | 0.400 | 0.571 | 0.604 | 0.881 | 0.694 | All types |
1.000 | 0.033 | 0.857 | 1.000 | 0.923 | 0.910 | 0.983 | 0.857 | Bevel gear |
1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | Helical and worm gear |
1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | Helical gear |
1.000 | 0.071 | 0.800 | 1.000 | 0.889 | 0.862 | 0.991 | 0.950 | Spur gear |
1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | Worm gear |
0.917 | 0.021 | 0.932 | 0.917 | 0.903 | 0.899 | 0.979 | 0.923 | Weighted Avg. |
Naïve Bayes | ||||||||
TP Rate | FP Rate | Precession | Recall | F-Measure | MCC | ROC Area | PRC Area | Class |
0.200 | 0.097 | 0.250 | 0.200 | 0.222 | 0.114 | 0.490 | 0.181 | All types |
0.833 | 0.033 | 0.833 | 0.833 | 0.833 | 0.800 | 0.978 | 0.897 | Bevel gear |
0.500 | 0.125 | 0.333 | 0.500 | 0.400 | 0.316 | 0.672 | 0.573 | Helical and worm gear |
1.000 | 0.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | Helical gear |
0.875 | 0.000 | 1.000 | 0.875 | 0.933 | 0.919 | 0.938 | 0.920 | Spur gear |
0.714 | 0.069 | 0.714 | 0.714 | 0.714 | 0.645 | 0.808 | 0.723 | Worm gear |
0.722 | 0.046 | 0.738 | 0.722 | 0.727 | 0.681 | 0.838 | 0.750 | Weighted Avg. |
Random Forest | ||||||||
TP Rate | FP Rate | Precession | Recall | F-Measure | MCC | ROC Area | PRC Area | Class |
0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.771 | 0.435 | All types |
1.000 | 0.067 | 0.750 | 1.000 | 0.857 | 0.837 | 1.000 | 1.000 | Bevel gear |
0.500 | 0.063 | 0.500 | 0.500 | 0.500 | 0.438 | 0.969 | 0.817 | Helical and worm gear |
1.000 | 0.033 | 0.857 | 1.000 | 0.923 | 0.910 | 1.000 | 1.000 | Helical gear |
0.875 | 0.036 | 0.875 | 0.875 | 0.875 | 0.839 | 0.996 | 0.986 | Spur gear |
1.000 | 0.069 | 0.778 | 1.000 | 0.875 | 0.851 | 1.000 | 1.000 | Worm gear |
0.778 | 0.045 | 0.669 | 0.778 | 0.717 | 0.692 | 0.964 | 0.898 | Weighted Avg. |
Support Vector Machines | ||||||||
TP Rate | FP Rate | Precession | Recall | F-Measure | MCC | ROC Area | PRC Area | Class |
0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.500 | 0.139 | All types |
0.667 | 0.033 | 0.800 | 0.667 | 0.727 | 0.683 | 0.817 | 0.589 | Bevel gear |
0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.500 | 0.111 | Helical and worm gear |
0.500 | 0.000 | 1.000 | 0.500 | 0.667 | 0.674 | 0.750 | 0.583 | Helical gear |
0.875 | 0.286 | 0.467 | 0.875 | 0.609 | 0.497 | 0.795 | 0.436 | Spur gear |
0.857 | 0.241 | 0.462 | 0.857 | 0.600 | 0.507 | 0.808 | 0.423 | Worm gear |
0.483 | 0.093333 | 0.454833 | 0.483 | 0.433 | 0.3935 | 0.695 | 0.380 | Weighted Avg. |
Process Parameters | HMFEO | NSGA II |
---|---|---|
Population Size/No. of Moths | 200 | 200 |
Number of generations | 1500 | 1500 |
Mutation Probability | - | 0.07 |
Cross-Over Probability | - | 0.76 |
Machine | M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M9 | M10 | M11 | M12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Energy consumption | 15 | 29 | 32 | 14 | 11 | 12 | 19 | 24 | 14 | 16 | 22 | 11 |
Reliability | 0.76 | 0.82 | 0.78 | 0.84 | 0.84 | 0.92 | 0.89 | 0.94 | 0.88 | 0.95 | 0.84 | 0.92 |
Jobs | Machines | Processing Time Range | GA-SA (Instance 1 to 32) GA-MA (Instance 33 to 35) | Proposed HMFO | |||
---|---|---|---|---|---|---|---|
Makespan | Energy Consumption | Makespan | Energy Consumption | ||||
Instance 1 | 3 | 5 | [1, 10] | 41 | 138.1 | 30.8 | 26.3 |
Instance 2 | 3 | 7 | [1, 10] | 54.1 | 205.4 | 43 | 190.5 |
Instance 3 | 3 | 10 | [1, 10] | 61.2 | 229.1 | 49.7 | 204 |
Instance 4 | 3 | 5 | [1, 50] | 190.3 | 708.7 | 171 | 617 |
Instance 5 | 3 | 7 | [1, 50] | 252.8 | 960.6 | 226.7 | 834 |
Instance6 | 3 | 10 | [1, 50] | 333.8 | 1273.3 | 301.7 | 1110 |
Instance7 | 3 | 5 | [1, 100] | 375.4 | 1307.1 | 319.3 | 1134 |
Instance8 | 3 | 7 | [1, 100] | 531.9 | 1895.4 | 516.7 | 1644.9 |
Instance9 | 3 | 10 | [1, 100] | 729.1 | 2830.5 | 698 | 2467.5 |
Instance10 | 5 | 5 | [1, 10] | 35 | 140.4 | 24 | 126.1 |
Instance11 | 5 | 7 | [1, 10] | 46 | 186.3 | 30 | 169 |
Instance12 | 5 | 10 | [1, 10] | 51.5 | 199.9 | 43.7 | 177 |
Instance13 | 5 | 5 | [1, 50] | 165.5 | 671.5 | 149.8 | 583.9 |
Instance14 | 5 | 7 | [1, 50] | 225.2 | 951.2 | 201.7 | 828 |
Instance15 | 5 | 10 | [1, 50] | 317 | 1303.6 | 306.8 | 1139 |
Instance16 | 5 | 5 | [1, 100] | 325.5 | 1253.2 | 311.7 | 1098 |
Instance17 | 5 | 7 | [1, 100] | 436.9 | 1909 | 410 | 1663 |
Instance18 | 5 | 10 | [1, 100] | 610.3 | 2587.5 | 598 | 2257 |
Instance19 | 7 | 5 | [1, 10] | 28.7 | 110.9 | 19.6 | 96.7 |
Instance20 | 7 | 7 | [1, 10] | 39.3 | 162.2 | 24 | 146 |
Instance21 | 7 | 10 | [1, 10] | 56.5 | 241.6 | 49 | 218 |
Instance22 | 7 | 5 | [1, 50] | 159.7 | 607 | 143 | 524 |
Instance23 | 7 | 7 | [1, 50] | 220.8 | 919.1 | 206 | 846 |
Instance24 | 7 | 10 | [1, 50] | 304.6 | 1310.5 | 265.002 | 1150.9 |
Instance25 | 7 | 5 | [1, 100] | 351 | 1422.9 | 305.37 | 1287.9 |
Instance26 | 7 | 7 | [1, 100] | 426.1 | 1978.3 | 370.7 | 1725 |
Instance27 | 7 | 10 | [1, 100] | 625.9 | 2664.1 | 544.5 | 2319.7 |
Instance28 | 10 | 10 | [1, 200] | 939.04 | 9873.2 | 816.96 | 8597.6 |
Instance29 | 15 | 15 | [1, 200] | 1554.12 | 22,505.2 | 1352.08 | 19,579.5 |
Instance30 | 20 | 20 | [1, 200] | 4778.07 | 80,577.2 | 4156 | 70,102.16 |
Instance31 | 20 | 20 | [1, 200] | 7753.04 | 100,073.4 | 6749 | 87,263.8 |
Instance32 | 20 | 20 | [1, 200] | 15,062.5 | 197,787.5 | 13,115 | 172,975.7 |
Instance 33 | 18 | 15 | [1, 200] | 531 | 13,340.3 | 502 | 12,986 |
Instance 34 | 18 | 15 | [1, 200] | 810 | 2036.32 | 739 | 1956 |
Instance 35 | 18 | 15 | [1, 200] | 680 | 2267.88 | 593 | 1837.6 |
Case | Process Plans Selected | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Jobs | Machines | J1 | J2 | J3 | J4 | J5 | J6 | J7 | J8 | |
Instance1 | 6 | 6 | 2 | 2 | 2 | 2 | 2 | 2 | - | - |
Instance2 | 6 | 6 | 1 | 1 | 2 | 2 | 1 | 2 | - | - |
Instance3 | 6 | 8 | 3 | 1 | 2 | 3 | 3 | 4 | - | - |
Instance4 | 8 | 8 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 3 |
Instance5 | 8 | 8 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 2 |
Instance6 | 6 | 12 | 1 | 1 | 1 | 1 | 2 | 1 | - | - |
Instance7 | 6 | 12 | 2 | 2 | 2 | 3 | 2 | 2 | - | - |
Instance8 | 6 | 12 | 2 | 2 | 2 | 2 | 2 | 2 | - | - |
Instance9 | 6 | 12 | 2 | 1 | 1 | 2 | 2 | 1 | - | - |
Instance10 | 6 | 12 | 2 | 1 | 1 | 3 | 2 | 1 | - | - |
Jobs | Machines | Proposed HMFO | NSGA-II | |||
---|---|---|---|---|---|---|
Makespan | Energy Consumption | Makespan | Energy Consumption | |||
Instance 1 | 6 | 6 | 30 | 8906 | 35 | 9083 |
Instance 2 | 6 | 6 | 27 | 8325 | 38 | 8700 |
Instance 3 | 6 | 8 | 179 | 39,658 | 187 | 44,920 |
Instance 4 | 8 | 8 | 42 | 13,089 | 50 | 14,040 |
Instance 5 | 8 | 8 | 50 | 10,129 | 62 | 11,189 |
Instance6 | 6 | 12 | 986 | 15,553 | 1083 | 17,694 |
Instance7 | 6 | 12 | 1179 | 15,224.5 | 1256 | 16,785 |
Instance8 | 6 | 12 | 1026 | 13,881 | 1094 | 14,205.5 |
Instance9 | 6 | 12 | 669 | 14,298 | 756 | 14,898.5 |
Instance10 | 6 | 12 | 814 | 14,143.5 | 884 | 14,678 |
Indicator | Algorithm | Instance | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
α | HMFEO NSGA II | 8.5 8.0 | 10.8 10.5 | 9.7 9.4 | 10.6 10.6 | 8.8 8.6 |
β | HMFEO NSGA II | 8.1 7.0 | 10.7 9.5 | 9.5 9.3 | 10.6 9.3 | 8.6 8.3 |
ᵝ/ᵅ | HMFEO NSGA II | 0.9529 0.8750 | 0.9900 0.9047 | 0.9700 0.9893 | 1.0000 0.8773 | 0.9700 0.9651 |
ϒ | HMFEO NSGA II | 0.5264 0.4736 | 0.5079 0.4921 | 0.6012 0.3988 | 0.5000 0.5000 | 0.5264 0.4736 |
K | HMFEO NSGA II | 0.0800 0.1250 | 0.0100 0.0953 | 0.0300 0.0107 | 0.0000 0.1227 | 0.0300 0.0349 |
λ | HMFEO NSGA II | 0.3693 12.123 | 0.0037 9.236 | 0.6289 8.265 | 0.0000 8.6321 | 0.0042 9.5632 |
π | HMFEO NSGA II | 0.4236 0.4125 | 0.4856 0.5563 | 0.4982 0.6029 | 0.4932 0.6765 | 0.4495 0.5988 |
HR | HMFEO NSGA II | 0.8526 1.2536 | 0.7445 1.1456 | 0.9538 0.9469 | 0.6984 0.9548 | 0.4495 0.9854 |
Indicator | Algorithm | Instance | ||||
---|---|---|---|---|---|---|
6 | 7 | 8 | 9 | 10 | ||
α | HMFEO NSGA II | 12.5 12.0 | 13.8 10.5 | 9.7 9.4 | 16.6 16.6 | 9.8 8.8 |
β | HMFEO NSGA II | 12.1 11.0 | 12.7 9.2 | 9.5 9.3 | 16.6 15.0 | 9.6 8.3 |
ᵝ/ᵅ | HMFEO NSGA II | 0.9682 0.9166 | 0.9202 0.8761 | 0.9700 0.9893 | 1.0000 0.9036 | 0.9795 0.9431 |
Ω | HMFEO NSGA II | 0.5151 0.4848 | 0.5070 0.4929 | 0.5078 0.4922 | 0.5000 0.5000 | 0.5057 0.4942 |
K | HMFEO NSGA II | 0.0800 0.1250 | 0.0104 0.0835 | 0.0310 0.0307 | 0.0000 0.1027 | 0.0400 0.0349 |
λ | HMFEO NSGA II | 0.4693 12.123 | 0.0089 9.236 | 0.5259 8.265 | 0.0000 8.6321 | 0.0063 10.5632 |
π | HMFEO NSGA II | 0.5136 0.4125 | 0.4856 0.4563 | 0.4982 0.5129 | 0.4932 0.5365 | 0.4445 0.5488 |
HR | HMFEO NSGA II | 0.7415 0.9438 | 0.8556 1.2465 | 0.8629 1.1803 | 0.7875 1.9888 | 0.7589 0.8765 |
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Ramakurthi, V.B.; Manupati, V.K.; Machado, J.; Varela, L. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Appl. Sci. 2021, 11, 6314. https://doi.org/10.3390/app11146314
Ramakurthi VB, Manupati VK, Machado J, Varela L. A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Applied Sciences. 2021; 11(14):6314. https://doi.org/10.3390/app11146314
Chicago/Turabian StyleRamakurthi, Veera Babu, V. K. Manupati, José Machado, and Leonilde Varela. 2021. "A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems" Applied Sciences 11, no. 14: 6314. https://doi.org/10.3390/app11146314
APA StyleRamakurthi, V. B., Manupati, V. K., Machado, J., & Varela, L. (2021). A Hybrid Multi-Objective Evolutionary Algorithm-Based Semantic Foundation for Sustainable Distributed Manufacturing Systems. Applied Sciences, 11(14), 6314. https://doi.org/10.3390/app11146314