Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry
Abstract
:1. Introduction
- Step 1. Preliminary study.
- Step 2. System requirements specification.
- Step 3. Identify data sources and choose relevant analytics that fits the problem.
- Step 4. Design system and data architecture with consideration for integration with extant systems.
- Step 5. Implement with considerations for development methodologies, continuous innovation, and long-term adaptability.
2. Related Work
2.1. An Overview of SMIs and SMEs in Malaysia
2.2. Optimization Algorithm for Scheduling
2.2.1. Artificial Intelligence (AI) Methods, Local Search Methods, and Metaheuristic Methods
2.2.2. Dispatch Rule Algorithms in Constructive Methods
3. Methods
3.1. Definition
- Pi = The processing time for job i;
- Df = Due date, i.e., the delivery date of the finished goods;
- STi = Setup time; the time for setup, including material change;
- Ci = Completion time for job i;
- Ci = di + Ci,j-1 + Pij (SOi + Pi +STi).
- Makespan time: the length of time that elapses from the start of work to the end.
- Setup time: setup time that includes material changing time.
- Total setup time: the sum of setup time
3.2. Assumption
- (1)
- Machines are always available and do not break down suddenly.
- (2)
- Each machine can only process one job at a time.
- (3)
- No changes are allowed once the schedule is confirmed by the manager.
- (4)
- Every material data extracted from the ERP System is the latest data.
- (5)
- Material is always available for production
- (6)
- All finished goods produced on that date will be delivered to the customer.
- (7)
- Inputs such as machine detail, holiday detail, shift detail, and machine breakdown detail are keyed in by the user and we expect all the inputs are correct
- (8)
- Unscheduled orders that have the same due date are postponed to the next day.
- (9)
- The Application Programming Interface (API) for production sheets only generates confirmed orders.
- (10)
- All confirmed orders on a selected day belong to a day prior
- (11)
- The expected setup time including material change is fixed in a time of 15 min
- (12)
- Machine capacity in a time range from 8 am to 5 pm is 45k square feet.
3.3. Dispatch Rule Algorithms
3.4. Proposed Method
3.4.1. General Data Flow Chart
3.4.2. Scheduling Algorithm Flow Chart
3.4.3. Algorithm for Optimal Sorting
3.4.4. Balancing Algorithm in Reducing Material Changing Lead Time
4. Result and Discussion
4.1. Result in One Machine Test
4.2. Result in Two Machines Test
4.3. Information Provided through User Interface
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
Algorithm A1 Arrange Orders with Priority Level (di + Ci,j-1 + Pij) |
1 Begin 2 /* select order with delivery date */ 3 Input Df 4 /* Arrange order by First In First Out and Priority */ 5 Ao = order arranged according to priority level (color, thickness, product and 6 Length (desc)) 7 /* Grouping according to same product number*/ 8 Go = Group orders according to the production number 9 END |
Appendix C
Algorithm A2 Assign Order to Machine Using SPT and Balancing (Balancing + Pij) |
1 Begin 2 /* get machine available time */ 3 Get MAT 4 /* get Grouped Order */ 5 Get Go 6 /* balancing with more than one machine*/ 7 FOR each o in Go 8 Create Job in schedule 9 /* get the machine that has more available time */ 10 Get M = MAT > = Pi 11 /* check with previous color and thickness */ 12 IF Coc == Poc && Cot == Pot THEN 13 Do not Add STi 14 Assign to previous M 15 ELSE 16 Add STi 17 Get M = MAT > = Pi 18 END IF 19 END LOOP 20 END |
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Category | Manufacturing | Services and Other Sectors |
---|---|---|
Medium | Sales Turnover: RM15 mil to RM50 mil OR Employees: From 75 to 200 people | Sales Turnover: RM3 mil to RM20 mil OR Employees: From 75 to 200 people |
Small | Sales Turnover: RM300,000 to RM 15 mil OR Employees: From 5 to 75 people | Sales Turnover: RM300,000 to RM3 mil OR Employees: From 5 to 30 people |
Micro | Sales Turnover: Less than RM300,000 OR Employees: Less than 5 people | Sales Turnover: Less than RM300,000 OR Employees: Less than 5 people |
Rule | Definition | Description |
---|---|---|
(1) FIFO | Ci,j−1 | Jobs are scheduled for work in the same sequence as they arrive at the machine. |
(2) SPT | Pij | Jobs are scheduled in ascending order of processing times. |
(3) EDD | di | Jobs are scheduled in ascending order of due dates. |
Notation | Description |
---|---|
i | Job (i, i + 1 ∈ I) |
I | Total of jobs |
k | Machines (k ∈ M) |
M | The total number of machines |
o | Order (o,o + 1 ∈ PO) |
PO | Total production orders |
Ami | The set of alternative machines on which job i can be processed (AMi ⊆ M) |
Oitc | Demand of job that cannot be produced on time in day tc |
Ti | Set of total items in the orders |
Ao | Arranged orders |
Go | Grouped orders |
Mc | Machine capacity |
Coc | Current order color |
Cot | Current order thickness |
Poc | Previous order color |
Pot | Previous order thickness |
STi | Setup time of job i |
Type of Machine | Number of Orders in Schedule | Total Setup Time (min) |
---|---|---|
one machine with using algorithms | 37 | 150 |
one machine without using algorithms | 20 | 285 |
Total Number of Orders in Schedule | Total Setup Time (min) | |
---|---|---|
Two machines without using algorithms | 42 | 570 |
Two machines using algorithms | 75 | 345 |
Machines | Total Number of Orders in Schedule | Total Setup Time (min) |
---|---|---|
M1 without algorithms | 23 | 300 |
M1 with algorithms | 36 | 210 |
M2 without algorithms | 19 | 270 |
M2 with algorithms | 39 | 135 |
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Ren, S.C.X.; Chaw, J.K.; Lim, Y.M.; Lee, W.P.; Ting, T.T.; Fong, C.W. Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry. Appl. Sci. 2022, 12, 6499. https://doi.org/10.3390/app12136499
Ren SCX, Chaw JK, Lim YM, Lee WP, Ting TT, Fong CW. Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry. Applied Sciences. 2022; 12(13):6499. https://doi.org/10.3390/app12136499
Chicago/Turabian StyleRen, Samuel Ching Xin, Jun Kit Chaw, Yee Mei Lim, Wah Pheng Lee, Tin Tin Ting, and Cheng Weng Fong. 2022. "Intelligent Manufacturing Planning System Using Dispatch Rules: A Case Study in Roofing Manufacturing Industry" Applied Sciences 12, no. 13: 6499. https://doi.org/10.3390/app12136499