the production scheduling for job shop production in ... · not have delayed job. sarwitee (2013),...

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

[email protected]

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.

mailto:[email protected]

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



Sub jobs

Sort delivery date form low to high

Same delivery date

Choose sub job

Sort arrival date form low to high

Finish


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



Sub jobs


Same delivery date

Choose sub job

Sort process time form high to low

Finish


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



Sub jobs


Same delivery date

Choose sub job

Sort process time form low to high

Finish


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.



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


completion time2 (Ci2) all process

Time2 = Ai2, Ci2

Step3



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



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



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.


Step5

Time4 = Ai4, Ci4

Choose JOB_PROCESS_NAME


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


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 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 LA MC-L-009 2/8/2012 9:50 2/8/2012 10:20


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 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


006-12-08-077 20-August-12 2850 120 MC-M-003 17/8/2012 17:00 18/8/2012 11:00



006-12-08-077 20-August-12 240 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


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-12 240 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


006-12-08-054 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-12 600 MC-L-002 29/8/2012 15:50 31/8/2012 10:50


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.

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