Upload
others
View
3
Download
0
Embed Size (px)
Citation preview
The Production Scheduling for Job Shop Production in Parallel Machines
by
Pornkiat Phakdeewongthep
College of Logistics and Supply Chain Suan Sunandha Rajabhat University
Nakhonpathom Learning Center
111/3-5 Moo 2, Tambon Klongyong Phutthamonthon District Nakhonprthom 73170, Thailand
+66991256355
ABSTRACT
This research aimed to reduce time of production planning and reduce the delayed jobs by
creating a computer program, visual basic 6.0, structure query language (SQL), the barcode system
and Heuristic method were used to create this computer program. This company has several kind of
products such as mold, jig and fixture, auto parts and machine parts so manufacturing system of this
company is job shop production. The planner officers used a lot of time for production scheduling
because a job had a lot of processes, outsources and machines so each month had a lot of the delayed
jobs. Then, a researcher created computer program, used the barcode system to assist tracking and
increase efficient of planning and used application earliest due date (EDD) method for the production
scheduling by comparing 4 methods were FIFO, EDD_FIFO, EDD_LPT and EDD_SPT. The results
showed that EDD_SPT was the best. When the computer program was used to plan and schedule that
could reduce time of production planning 409 minutes per job to 271 minutes per job. Finally, the
research could reduce number of the delayed jobs from 52 jobs to 38 jobs.
Keywords: Production Scheduling, Job Shop Production, Parallel Machines, Heuristic.
1. INTRODUCTION
Currently, Thailand's automotive industry is the industry that the government has provided
facility. The government’s the goal want to consistently promote Thailand as a hub for automotive
production and export. Results of action in this approach, Thailand's automotive industry is growing
continuously. The last development of the automotive industry has found, it is focused on low-cost
production and the quality production that are a key factor for the competition. Cars are produced
more variety, the shape color technology and the price. For the big manufacturers are TOYOTA,
HONDA, NISSAN, FORD and others. They develop cars all time to be able to meet the needs of
customers that have changed over time. Manufacturers of mold parts must prepare everything to
support the expansion by planning to use manpower machines raw materials and the production
scheduling. If the mold parts manufacturer cannot deliver goods on time, it will impact the delivery
of the big manufacturers.
The production planning in each time need to use a lot of basic information required and
updated information such as specific information, drawing, delivery date, quantity, available
machine, material information, the sequenced process, job status in process, tool for quality control,
packing, testing, etc. If planner takes a long time to receive this information, then production
scheduling will take a long time as well. Finally, the company will have a lot of the delayed jobs.
The foregoing, the time is very important for planning each time. Another important thing is
job sequencing in each machine. That is, when have a lot of jobs to plan in a machine, job
sequencing is very important. If planner sequence inefficient, it causes problems in delayed delivery.
When a factory had the delayed delivery delay, the receiver would be affected. The receiver
must change process modal, time modal, transportation model, packaging modal and others for the
delayed product was sent to consumer on time (Duangjai Jandasang, Wannee Sutthachaidee,. 2015).
The factory is a small and medium enterprise (SME) and has several kinds of products such
as mold, jig and fixture, auto parts and machine parts. The results of study showed that a factory had
a lot of delayed jobs that is shown in table 1.
Table 1 The percentage of the delayed jobs from January, 2012 to June, 2012.
Month Total jobs Delayed job The percentage of the delayed job
January 42 25 59.52
February 37 13 35.14
March 46 17 36.96
April 36 10 27.78
May 59 27 45.76
June 29 17 58.62
From table 1, the analysis of the causes of delayed delivery (Pornkiat Phakdeewongthep and
Pairoj Raothanachonkun, 2012) that had three steps. First, a basic analysis by the Quick Scan was
used to analyze for cutting out irrelevant departments. Later, an advanced analysis by Supply Chain
Operations Reference Model (SCOR Model) was used to analyze for showing details of delayed
delivery. Finally, a depth analysis by Why-Why Analysis was used to analyze for showing root cause
of delayed delivery (Danai Aphiraksanti, 2013). The results showed that delayed delivery of this
factory had root cause at planning department. A root cause was planner used a lot of time for
production scheduling because planner used a lot of time to find information for production
scheduling and production scheduling was difficult because process was job shop.
The case when there were a lot of assembly jobs, a factory will have a lot of quantity of
delayed jobs that is shown in table 2 because an assembly job had about 150 sub-jobs and job
scheduling was difficult than normal jobs
Table 2 Assembly job ratio
Month Delayed job Normal job Assembly job The percentage of assembly job
January 25 15 10 40.00
February 13 9 4 30.77
March 17 9 8 47.06
April 10 4 6 60.00
May 27 12 15 55.56
June 17 8 9 52.94
Figure 1 All problems of planning
Therefore, from table 2 and figure 1, this research aimed to: (1) reduce time for production
planning (Suthathip Budsaba, 2011) and (2) reduce quantity of delayed jobs (Kridsada
Chawbangprom, 2011) by creating a computer program that visual basic 6.0, structure query
language (SQL), the barcode system and Heuristic method were used to create this computer
program (Wirote Pintaruk, 2010). Information was used for production scheduling.
2. LITERATURE REVIEW
Kanate (2011) conducted a study of algorithm development for solving flexible job shop
scheduling problem by genetic algorithm (GA), particle swarm optimization (PSO) and the
Makespan Tree. There were 2 steps. First, jobs must be assigned to machines. Second, jobs must be
sequenced. The results for this research showed that the Makespan Tree had the shortest process time
was 57.41%
Pongtada (2013), this study was comparison unrelated parallel machines scheduling of result
from scheduling program and assignment problem. The purpose of this thesis was the studying
production scheduling and increasing the efficiency of job scheduling by using computer program
and comparison method, Earliest Due Date (EDD), Minimum Slack Time (MS), First Come First
Served (FCFS), Longest Processing Time (LPT) and Shortest Processing Time (SPT). The results for
this research showed that EDD had the minimum of tardiness and number of late jobs.
Rukkiat (2012) studied of multi-objective scheduling and sequencing, a case study of a
plastic injection factory. This research aimed to solve tardiness problem by comparison 15
scheduling (EDD, LWKR, MWKR, MOPNR, SMT, SPT, STPT, LWKT (with setup time), MWKR
(with setup time), MOPNR (with setup time), SMT (with setup time), SPT (with setup time), STPT
(with setup time), Heuristic1, Heuristic1 and Heuristic3). The results for this research showed that
Heuristic2 was used and the percentage of tardiness reduced from 17.85% to 0%.
Sakorn (2013) studied of production scheduling of pressing process for automobile by using
Heuristic method. This research aimed to create production scheduling and the minimum delayed job
Used the time to find the
information too long.
Machine group 1 Machine group n
Outsourcing
had a lot of sub jobs too
many.
could not track the status of
all.
Sequencing problem Assembly problem Finding information problem Tracking problem
by comparison Shortest Processing Time (SPT), Longest Processing Time (LPT), Earliest Deadline
First (EDF) and Least Slack Time First (LSTF). The results for this research showed that EDF did
not have delayed job.
Sarwitee (2013), this research was the study of production scheduling of printer machine in
plastic packaging. This research aimed to develop job scheduling algorithm by using the
mathematical model of the production scheduling and Excel solver. The mathematical models were
used this research that had Shortest Processing Time (SPT) and Longest Processing Time (LPT). The
results for this research showed that SPT was the best. The makespan time was reduced from 4,500
minutes to 3,066 minutes. The mean flow time was reduced from 843.75 minutes to 574.4 minutes.
The total time was reduced from 27,000 minutes to 18,382 minutes.
3. RESEARCH METHODOLOGY
3.1 Population and Sample
Population and sample size had 30 employees. A factory is in Pathum Thani province,
Thailand.
3.2 Planning improvements
Planning improvements aimed to reduce time for production planning and reduce quantity of
delayed jobs. There were 2 parts: (1) the study the information and (2) designing program.
3.2.1 The study the information
The study the information was used for production scheduling by the study the Work
Instruction (WI), the study the actual work and interviewing staff, supervisor and manager. The
results of study were used for designing the program.
3.2.2 Designing program
The program was designed that it had multiple functions to: (1) connected information for
production planning; (2) supported Bar Codes to be used to track jobs; (3) printed reports such as
delivery report, backlogs report, etc. and (4) scheduled jobs by application Heuristic method to
reduce quantity of delayed jobs.
From the reviewing researches showed that job scheduling by Earliest Due Date (EDD)
(Yodduangjai Nakpathom, 2012) was most effective to reduce delayed jobs, so this research had the
scope application of EDD for job scheduling case assembly job, job shop production in parallel
machines, 27 machines and 11 machine groups.
3.3 Heuristic Method
Heuristic method for assembly job had 2 parts: (1) job scheduling and (2) finding the arrival
time and completion time.
3.2.1 Job scheduling
Job scheduling for assembly job by application Heuristic method to reduce quantity of
delayed jobs. Heuristic method could not be used directly because an assembly job had about 150
sub-jobs that some sub jobs will be done only when other sub jobs were completed. Therefore, this
research has 4 test methods were First Come First Serve (FCFS), Earliest Due Date and First Come
First Serve (EDD_FCFS), Earliest Due Date and Longest Processing Time (EDD_LPT) and Earliest
Due Date and Shortage Processing Time (EDD_SPT).
1. First Come First Serve (FCFS) was job sequencing by the first arrival job will be done that
is shown in figure 2. Currently, the factory used this method to sequencing.
Figure 2 First Come First Serve (FCFS) for assembly job
2. Earliest Due Date and First Come First Serve (EDD_FCFS) was job sequencing by sorting
delivery dates from low to high. If jobs had same delivery date, the first arrival job will be done that
is shown in figure 3.
Start
Assembly job Job scheduling for normal job
Job scheduling for assembly job
Sub jobs
Sort arrival date form low to high
Choose sub job
Finish
All sub jobs are done
No
Yes
Have
Do not have
Yes
No
Figure 3 Earliest Due Date and First Come First Serve (EDD_FCFS) for assembly job
3. Earliest Due Date and Longest Processing Time (EDD_LPT) was job sequencing by
sorting delivery dates from low to high. If jobs had same delivery date, the longest time job will be
done that is shown in figure 4.
Start
Assembly job Job scheduling for normal job
Job scheduling for assembly job
Sub jobs
Sort delivery date form low to high
Same delivery date
Choose sub job
Sort arrival date form low to high
Finish
All sub jobs are done
No
Yes
Have
Do not have
No
Yes
Yes
No
Figure 4 Earliest Due Date and Longest Processing Time (EDD_LPT) for assembly job
4. Earliest Due Date and Shortage Processing Time (EDD_SPT) was job sequencing by
sorting delivery dates from low to high. If jobs had same delivery date, the shortest time job will be
done that is shown in figure 5.
Start
Assembly job Job scheduling for normal job
Job scheduling for assembly job
Sub jobs
Sort delivery date form low to high
Same delivery date
Choose sub job
Sort process time form high to low
Finish
All sub jobs are done
No
Yes
Have
Do not have
No
Yes
Yes
No
Figure 5 Earliest Due Date and Shortage Processing Time (EDD_SPT) for assembly job
3.2.2 The finding the arrival time and the completion time
The finding the arrival time and the completion time for assembly job by comparison the
arrival time and the completion time of processes and machines. There were 6 steps.
1. Step1: Start to schedule jobs by selecting a method. Then, set 2 parameters are
JOB_Process_ID and JOB_Seq. Later, find arrival time1 (Ai1) and completion time1 (Ci1) of all
jobs. Finally, record Ai1 and Ci1 that is shown in figure 6 and table 3.
Figure 6 Flowchart of step 1
Start
Assembly job Job scheduling for normal job
Job scheduling for assembly job
Sub jobs
Sort delivery date form low to high
Same delivery date
Choose sub job
Sort process time form low to high
Finish
All sub jobs are done
No
Yes
Have
Do not have
No
Yes
Yes
No
Step1
Scheduled jobs by Heuristic method
Set JOB_Process_ID, JOB_Seq
Find Arrival time1 (Ai1) and
completion time1 (Ci1) all jobs
Time1 = Ai1, Ci1
Step2
Table 3 Example of Step 1
EDD Ai1 Ci1
JOB_DESC DELIVERLY_DATE JOB_PROCESS_ID JOB_PROCESS_NAME JOB_Seq Process_Time Due_Time Start_Time Finish_Time
001-13-01-002 31/1/2013 1 CT 1 10 31680 0 10 001-13-01-002 31/1/2013 2 CL 1 30 31680 10 160 001-13-01-002 31/1/2013 3 QC 1 10 31680 160 170 001-13-01-002 31/1/2013 4 HE 1 7200 31680 170 10170 001-13-01-002 31/1/2013 5 CL 1 60 31680 10170 10320 001-13-01-002 31/1/2013 6 QC 1 20 31680 10320 10340 001-13-01-002 31/1/2013 7 CO 1 20 31680 10340 10360 001-13-01-002 31/1/2013 8 QC(F) 1 10 31680 10360 10370 001-13-01-001 31/3/2013 1 CT 2 10 116640 0 10 001-13-01-001 31/3/2013 2 CL 2 30 116640 10 160 001-13-01-001 31/3/2013 3 QC 2 10 116640 160 170 001-13-01-001 31/3/2013 4 HE 2 7200 116640 170 10170 001-13-01-001 31/3/2013 5 CL 2 60 116640 10170 10320 001-13-01-001 31/3/2013 6 QC 2 20 116640 10320 10340 001-13-01-001 31/3/2013 7 CO 2 20 116640 10340 10360 001-13-01-001 31/3/2013 8 QC(F) 2 10 116640 10360 10370
2. Step2: Start to sort JOB_Process_ID from low to high. Then, find arrival time2 (Ai2) and
completion time2 (Ci2) of all process. Finally, record Ai2 and Ci2 that is shown in figure 7 and table
4.
Figure 7 Flowchart of step 2
Table 4 Example of Step 2
EDD Ai1 Ci1 Time2
JOB_DESC JOB_PROCESS_ID JOB_PROCESS_NAME JOB_Seq Process_Time Due_Time Start_Time Finish_Time Ai2 Ci2
001-13-01-002 1 CT 1 10 31680 0 10 0 10 001-13-01-001 1 CT 1 10 116640 0 10 10 20
3. Step3: Start to compare Ci2 with Ai1 and all process. Then find Ai3 and Ci3. Finally,
record Ai3 and Ci3 that is shown in figure 8 and table 5.
Step2
Sort JOB_Process_ID from low to high
Find Arrival time2 (Ai2) and
completion time2 (Ci2) all process
Time2 = Ai2, Ci2
Step3
Figure 8 Flowchart of step 3
Table 5 Example of Step 3
Ai1 Ci1 Time3
JOB_DESC JOB_PROCESS_ID JOB_PROCESS_NAME JOB_Seq Process_Time Start_Time Finish_Time Ai2 Ci2 Ai3 Ci3
001-13-01-002 1 CT 1 10 0 10 0 10 0 10 001-13-01-002 2 CL 1 30 10 160 10 160 001-13-01-002 3 QC 1 10 160 170 160 170 001-13-01-002 4 HE 1 7200 170 10170 170 10170 001-13-01-002 5 CL 1 60 10170 10320 10170 10320 001-13-01-002 6 QC 1 20 10320 10340 10320 10340 001-13-01-002 7 CO 1 20 10340 10360 10340 10360 001-13-01-002 8 QC(F) 1 10 10360 10370 10360 10370 001-13-01-001 1 CT 2 10 0 10 10 20 10 20 001-13-01-001 2 CL 2 30 10 160 20 170 001-13-01-001 3 QC 2 10 160 170 170 180 001-13-01-001 4 HE 2 7200 170 10170 180 10180 001-13-01-001 5 CL 2 60 10170 10320 10180 10330 001-13-01-001 6 QC 2 20 10320 10340 10340 10360 001-13-01-001 7 CO 2 20 10340 10360 10360 10380 001-13-01-001 8 QC(F) 2 10 10360 10370 10380 10390
4. Step4: Start to set Qty_MC, MC_NO, MC_NAME and MC_Loop. Then sort
JOB_Process_ID from low to high. Finally, put name machine to JOB_Process_ID that is shown in
figure 9 and table 6.
Step3
Ci3 = Ai3 + Process_ Time
Ci2 > Ai1
Ai3 = Ci2 of before processAi3 = Ai1
Time3 = Ai3, Ci3
Step4
No Yes
Figure 9 Flowchart of step 4
Table 6 Example of Step 4
JOB_DESC JOB_PROCESS_ID JOB_PROCESS_NAME Qty_MC Process_Time Ai3 Ci3 MC_NO MC_NAME MC_Loop
001-13-01-002 1 CT 1 10 0 10 1 1 001-13-01-002 2 CL 2 30 10 160 1 MCL001 1 001-13-01-002 3 QC 1 10 160 170 1 1 001-13-01-002 4 HE 1 7200 170 10170 1 1 001-13-01-002 5 CL 2 60 10170 10320 1 MCL001 2 001-13-01-002 6 QC 1 20 10320 10340 1 1 001-13-01-002 7 CO 1 20 10340 10360 1 1 001-13-01-002 8 QC(F) 1 10 10360 10370 1 1 001-13-01-001 1 CT 1 10 10 20 1 1 001-13-01-001 2 CL 2 30 20 170 2 MCL002 1 001-13-01-001 3 QC 1 10 170 180 1 1 001-13-01-001 4 HE 1 7200 180 10180 1 1 001-13-01-001 5 CL 2 60 10180 10330 2 MCL002 2 001-13-01-001 6 QC 1 20 10340 10360 1 1 001-13-01-001 7 CO 1 20 10360 10380 1 1 001-13-01-001 8 QC(F) 1 10 10380 10390 1 1
5. Step5: Start to choose JOB_PROCESS_NAME. Then find Ai4 and Ci4. Finally, record
Ai4 and Ci4 that is shown in figure 10 and table 7.
Step4
Qty_MC > 1
MC_NAME = Machine1 for job1
MC_NAME = Machine n for job nMC_NAME = Machine1
Step5
No Yes
Set Qty_MC, MC_NO, MC_NAME,
MC_Loop
Sort JOB_Process_ID from low to high
End
JOB_Process_IDJOB_Process_ID +1
Yes
No
Figure 10 Flowchart of step 5
Table 7 Example of Step 5
JOB_DESC JOB_PROCESS_ID JOB_PROCESS_NAME Qty_MC Ai3 Ci3 MC_NO MC_NAME Ai4 Ci4
001-13-01-002 2 CL 2 10 160 1 MCL001 10 160
001-13-01-002 5 CL 2 10170 10320 1 MCL001 10170 10320 001-13-01-001 2 CL 2 20 170 2 MCL002 20 170 001-13-01-001 5 CL 2 10180 10330 2 MCL002 10180 10330
6. Step6: Start to compare Ai3 with Ci4, if there are same MC_NAME. Then find Ai5 and
Ci5. Finally, record Ai5, Ci5 and transfer them to date that is shown in figure 11 and table 8.
Figure 11 Flowchart of step 6
Step5
Time4 = Ai4, Ci4
Choose JOB_PROCESS_NAME
Find Arrival time4 (Ai4) and
completion time4 (Ci4) all machines
Step6
Step6
Same MC_NAME
Transfer Time5 to date
No
Yes
Ai3 > Ci4 of
before process
Ai5 = Ai3Ai5 = Ci4 of before process
Ci5 = Ai4 + Process_ Time
Time5 = Ai5, Ci5
Yes
No
Finish
Table 8 Example of Step 6
JOB_DESC JOB_PROCESS_NAME Qty_MC Ai3 Ci3 Ai4 Ci4 Ai5 Ci5
001-13-01-002 CT 1 0 10
0 10 001-13-01-002 CL 2 10 160 10 160 10 160 001-13-01-002 QC 1 160 170
160 170
001-13-01-002 HE 1 170 10170
170 10170 001-13-01-002 CL 2 10170 10320 10170 10320 10170 10320 001-13-01-002 QC 1 10320 10340
10320 10340
001-13-01-002 CO 1 10340 10360
10340 10360 001-13-01-002 QC(F) 1 10360 10370
10360 10370
001-13-01-001 CT 1 10 20
10 20 001-13-01-001 CL 2 20 170 20 170 20 170 001-13-01-001 QC 1 170 180
170 180
001-13-01-001 HE 1 180 10180
180 10180 001-13-01-001 CL 2 10180 10330 10180 10330 10180 10330 001-13-01-001 QC 1 10340 10360
10340 10360
001-13-01-001 CO 1 10360 10380
10360 10380 001-13-01-001 QC(F) 1 10380 10390
10380 10390
3.4 Performance measurement
Performance measurement of program to measure: (1) the time was used for production
scheduling by starting to receive customer order process until to deliver products process and (2)
delayed jobs by comparison each Heuristic method with method of a factory.
4. RESULTS
4.1 The results of planning improvements
The results of planning improvements showed that there were 2 parts: (1) the results of the
study the information; (2) the results of designing program.
4.1.1 The results of the study the information
The results of the study the information for assembly job showed that there were 7
information were used for production scheduling. Information was: (1) customer requirement; (2)
material; (3) drawing; (4) machine; (5) process; (6) process time and (7) job status.
1. Customer requirement for an assembly job was data from the customer orders such as
product name, quantity, delivery date, etc.
2. Material for an assembly job was kind of material such as code SKD61, S50C, SKS3, etc.
Normally, purchasing department had mixed systems between make to order with make to stock.
3. Drawing for an assembly job had about 150 sub jobs. Assembly job had both rough parts
and fine parts. Rough parts had the error more than 0.015 mm. Rough parts had the error did between
0.004-0.015 mm. that is shown in figure 12.
Figure 12 Drawing of assembly job
4. Machine for an assembly job had 27 machines and 11 machine groups that are shown in
table 9.
Table 9 Machines list
Machine Groups Quantity
Manual Lathe 2
CNC Lathe 7
Manual Milling 3
CNC Milling 4
Rough Milling 1
Sphere Grinding 3
Small Horizontal Grinding 1
Medium Horizontal Grinding 1
Internal Grinding 2
Radial 1
Cutting 1
5. Process for an assembly job was used for job scheduling. A sub job had about 8 processes.
Processes had both internal process and external process such as cutting, roughing, milling, heat
treatment, coating, etc.
6. Process time for an assembly job was used for job scheduling. Normally, process time was
written minute form such as cutting (50 min.), roughing (90 min.), milling (120 min.), heat treatment
(480 min.), coating (960 min.), etc.
7. Job status for an assembly job had 2 statuses: (1) finished and (2) did not finish. If status
did not finish, planner must estimate time again such as cutting (finished), roughing (finished),
milling (60 min.), heat treatment (480 min.), coating (960 min.), etc.
4.1.1 The results of designing program
The results of designing program that it had multiple functions such as connecting
information, supporting Bar Code to be used to track jobs, printing reports, job scheduling, etc. They
are shown in figure 13, figure 14, figure 15, figure 16 and figure 17.
Figure 13 Screen for inputting data
Figure 14 Screen for inputting process and setting standard process
Figure 15 Screen for application Bar code
Figure 16 Example of reports
Figure 17 Screen for application Heuristic method
4.2 The results of application Heuristic method
This research had 4 test methods were First Come First Serve (FCFS), Earliest Due Date and
First Come First Serve (EDD_FCFS), Earliest Due Date and Longest Processing Time (EDD_LPT)
and Earliest Due Date and Shortage Processing Time (EDD_SPT) are shown in table 10, table 11,
and table 12.
Table 10 Example of EDD_FIFO
JOB NO PLAN DATE DELIVERLY_DATE PROCESS_NAME Machine Start Date Finish Date
006-12-08-019 1-August-12 20-August-12 CT MC-C-001 2/8/2012 9:00 2/8/2012 9:10
006-12-08-019 1-August-12 20-August-12 CL MC-L-001 2/8/2012 9:10 2/8/2012 9:40
006-12-08-019 1-August-12 20-August-12 LA MC-L-005 2/8/2012 9:40 2/8/2012 10:10
006-12-08-019 1-August-12 20-August-12 QC QC-Tooling 2/8/2012 10:10 2/8/2012 10:20
006-12-08-019 1-August-12 20-August-12 HE Outsource-HE 2/8/2012 10:20 7/8/2012 10:20
006-12-08-019 1-August-12 20-August-12 CL MC-L-004 7/8/2012 10:20 7/8/2012 11:20
006-12-08-019 1-August-12 20-August-12 QC QC-Tooling 7/8/2012 11:20 7/8/2012 11:40
006-12-08-019 1-August-12 20-August-12 CO Outsource-CO 7/8/2012 11:40 10/8/2012 11:40
006-12-08-027 2-August-12 20-August-12 CT MC-C-001 2/8/2012 9:10 2/8/2012 9:20
006-12-08-027 2-August-12 20-August-12 CL MC-L-002 2/8/2012 9:20 2/8/2012 9:50
006-12-08-027 2-August-12 20-August-12 LA MC-L-009 2/8/2012 9:50 2/8/2012 10:20
006-12-08-027 2-August-12 20-August-12 QC QC-Tooling 2/8/2012 10:20 2/8/2012 10:30
006-12-08-027 2-August-12 20-August-12 HE Outsource-HE 2/8/2012 10:30 7/8/2012 10:30
006-12-08-027 2-August-12 20-August-12 CL MC-L-001 7/8/2012 10:30 7/8/2012 11:30
006-12-08-027 2-August-12 20-August-12 QC QC-Tooling 7/8/2012 11:40 7/8/2012 12:00
006-12-08-027 2-August-12 20-August-12 CO Outsource-CO 7/8/2012 12:00 10/8/2012 12:00
Table 11 Example of EDD_LPT
JOB NO DELIVERLY_DATE Total_Time Process_Time Machine Start Date Finish Date
006-12-08-054 20-August-12 3900 100 MC-C-001 11/8/2012 17:00 12/8/2012 10:40
006-12-08-054 20-August-12 300 MC-L-001 17/8/2012 10:30 17/8/2012 16:30
006-12-08-054 20-August-12 300 MC-M-006 17/8/2012 16:30 18/8/2012 14:30
006-12-08-054 20-August-12 200 MC-M-007 24/8/2012 9:50 24/8/2012 14:10
006-12-08-054 20-August-12 100 QC-Tooling 24/8/2012 14:10 24/8/2012 15:50
006-12-08-054 20-August-12 2100 Outsource-HE 24/8/2012 15:50 29/8/2012 15:50
006-12-08-054 20-August-12 600 MC-L-002 29/8/2012 15:50 31/8/2012 10:50
006-12-08-054 20-August-12 200 QC-Tooling 15/9/2012 11:54 15/9/2012 16:14
006-12-08-077 20-August-12 2850 120 MC-M-003 17/8/2012 17:00 18/8/2012 11:00
006-12-08-077 20-August-12 10 QC-Tooling 24/8/2012 12:30 24/8/2012 12:40
006-12-08-077 20-August-12 2100 Outsource-HE 24/8/2012 12:40 29/8/2012 12:40
006-12-08-077 20-August-12 240 MC-G-005 3/9/2012 16:20 4/9/2012 12:20
006-12-08-077 20-August-12 240 MC-M-003 4/9/2012 12:20 5/9/2012 9:20
006-12-08-077 20-August-12 120 MC-L-007 5/9/2012 16:20 6/9/2012 10:20
006-12-08-077 20-August-12 20 QC-Tooling 8/9/2012 15:14 8/9/2012 15:34
Table 12 Example of EDD_SPT
JOB NO DELIVERLY_DATE Total_Time Process_Time Machine Start Date Finish Date
006-12-08-077 20-August-12 2850 120 MC-M-003 17/8/2012 17:00 18/8/2012 11:00
006-12-08-077 20-August-12 10 QC-Tooling 24/8/2012 12:30 24/8/2012 12:40
006-12-08-077 20-August-12 2100 Outsource-HE 24/8/2012 12:40 29/8/2012 12:40
006-12-08-077 20-August-12 240 MC-G-005 3/9/2012 16:20 4/9/2012 12:20
006-12-08-077 20-August-12 240 MC-M-003 4/9/2012 12:20 5/9/2012 9:20
006-12-08-077 20-August-12 120 MC-L-007 5/9/2012 16:20 6/9/2012 10:20
006-12-08-077 20-August-12 20 QC-Tooling 8/9/2012 15:14 8/9/2012 15:34
006-12-08-054 20-August-12 3900 100 MC-C-001 11/8/2012 17:00 12/8/2012 10:40
006-12-08-054 20-August-12 300 MC-L-001 17/8/2012 10:30 17/8/2012 16:30
006-12-08-054 20-August-12 300 MC-M-006 17/8/2012 16:30 18/8/2012 14:30
006-12-08-054 20-August-12 200 MC-M-007 24/8/2012 9:50 24/8/2012 14:10
006-12-08-054 20-August-12 100 QC-Tooling 24/8/2012 14:10 24/8/2012 15:50
006-12-08-054 20-August-12 2100 Outsource-HE 24/8/2012 15:50 29/8/2012 15:50
006-12-08-054 20-August-12 600 MC-L-002 29/8/2012 15:50 31/8/2012 10:50
006-12-08-054 20-August-12 200 QC-Tooling 15/9/2012 11:54 15/9/2012 16:14
4.3 Results of performance measurement
4.3.1 Time
The results of comparison time between the current operating with program that is shown in
table 13.
Table 13 Comparison time between the current operating with program
Sequence Procedure The current (minutes / job) Program (minutes / job)
1 Received order 7 7 2 Inspected order 39 32 3 Checked capacity 45 9 4 Recorded order 27 14 5 Sent orders to planning department. 1 1 6 Did production scheduling 36 3 7 Adjusted production scheduling 24 12 8 Copied production scheduling 8 8 9 Sent orders to production department. 1 1 10 Checked jobs status 21 1 11 Reported products to transport department. 20 3 12 Delivered products 180 180
Total Time 409 271
4.3.2 Delayed job
The results of comparison delayed jobs each Heuristic method between the current operating
with program that is shown in table 14.
Table 14 Comparison delayed jobs between the current operating with program
Method Total jobs Delayed job The percentage of the delayed job
FIFO 52 52 100
EDD_FIFO 52 47 90.38
EDD_LPT 52 45 86.53
EDD_SPT 52 38 73.07
5. CONCLUSION
This research aimed to reduce time of production planning and reduce the delayed jobs by
creating a computer program, visual basic 6.0, structure query language (SQL), the barcode system
and Heuristic method were used to create this computer program. The results showed.
1. From table 13, the results of comparison time between the current operating with program
that using program could reduce time operating from 409 minutes per job to 271 minutes per job; the
percentage of reducing was 33.75.
2. From table 14, the results of comparison delayed jobs between the current operating with
program that using EDD_SPT program was the best and could reduce delayed jobs from 52 jobs to
38 jobs, the percentage of reducing was 26.92.
REFERENCES
Aphiraksanti, Danai (2013). Job Scheduling on Non-Identical Parallel Printing Machines in
Pharmaceutical Industry, King Mongkut’s University of Technology Thonburi.
Budsaba, Suthathip (2011). The Production Scheduling Generation with Heuristic Methods: Case
Study VIP Hat Co.,TH., Silpakorn University.
Chaivohan, Sakorn (2013). Production Scheduling of Pressing Process for Automobile Seat Frame
Components, King Mongkut’s University of Technology Thonburi.
Chawbangprom, Kridsada (2011). Decision Support System for Master Production Scheduling and
Material Requirement Planning: A Case Study of Flavor Squid Factory, National Institute of
Development Administration.
Jandasang, Duangjai (2015). “Factors Affecting the Service of Transportation use Outbound Freight:
Case study of Operators,” Ph.D in Social Science journal, 5(3). 52-67.
Kurukidcumchorn, Pongtada (2013). Comparison Unrelated Parallel Machines Scheduling of Result
From Scheduling Program and Assignment Problem, Silpakorn University.
Nakpathom, Yodduangjai (2012). A Study of Job Shop Production Scheduling on Textile Industry,
Silpakorn University.
Phakdeewongthep, Pornkiat and Raothanachonkun, Pairoj (2012). “Analysis the cause of the delayed
delivery for mold parts the manufacturer,” The International Journal of the 12th Thai Value Chain
Management and Logistics.
Pintaruk, Wirote (2010). Application of Single Machine Scheduling Technique for Chemical
Process, North Bangkok University.
Ploydanai, Kanate (2011). Algorithm Development for Solving Flexible Job Shop Scheduling
Problem, Kasetsart University.
Singhparn, Sarwitree (2013). Production Scheduling of Printer Machine in Plastic Packaging, King
Mongkut’s University of Technology Thonburi.
Sutthachaidee, Wannee (2015). The Logistics Management of Coconut-Shell Products: A Case
Study of Samut Songkram Province, Thailand, Suan Sunandha Rajabhat University.
Wongklang, Rukkiat (2012). Multi-Objective Scheduling and Sequencing: A Case Study of a Plastic
Injection Factory, Dhurakij Pundit University.