SIMULATION AND COMPARISON ANALYSIS OF DUE DATE ASSIGNMENT METHODS USING SCHEDULING RULES IN A JOB SHOP PRODUCTION SYSTEM

International Journal of Computer Science & Engineering Survey (IJCSES) Vol.6, No.5, October 2015
DOI:10.5121/ijcses.2015.6503 29
SIMULATION AND COMPARISON ANALYSIS OF DUE
DATE ASSIGNMENT METHODS USING SCHEDULING
RULES IN A JOB SHOP PRODUCTION SYSTEM
Fitsum Getachew and Eshetie Berhan
School of Mechanical and Industrial Engineering, Addis Ababa University, Addis Ababa
ABSTRACT
This paper presents a simulation and comparison analysis conducted to investigate the due-date
assignment methods through various scheduling rules. The due date assignment methods investigated are
flow time due date (FTDD) and total work content (TWK) method. Three scheduling rules are integrated in
the simulation for scheduling of jobs on machines. The performance of the study is evaluated based on the
configuration system of Hibret manufacturing and machine building Industry, subsidiary company of
Metals and Engineering Corporation were thoroughly considered. The performance of the system is
evaluated based on maximum tardiness, number of tardy jobs and total weighted tardiness. Simulation
experiments are carried in different scenarios through combining due-date assignment methods and
scheduling rules. A two factor Analysis of variance of the experiment result is performed to identify the
effect of due-date assignment methods and scheduling rules on the performance of the job shop system. The
least significant difference (LSD) method was used for performing comparisons in order to determine
which means differ from the other. The finding of the study reveals that FTDD methods gives less mean
value compared to TWK when evaluated by the three scheduling rules.
KEYWORDS
Due dates assignment, Scheduling rules, Tardiness, Flow time due date, Total work content
1. INTRODUCTION
In a typical job-shop, potential customers dynamically arrive with a request for work. The shop
management and the customer negotiate with respect to the volume, mix, and specification of
products desired, the promised due-date, and the price [1]. Negotiating and meeting due dates is
one of the most important and challenging problems in production management.
The performance system of a Job shop scheduling is optimized with scheduling and sequencing
jobs in any production system. This measure includes job finish times and estimation of due dates
that have major impact on the current global competition. However, the ability to meet due dates
is dependent not only on the performance measures but also the variation relationships between
job dispatching procedures and the reasonableness of the due dates. The reasonableness of the
promised due dates can be seen in to two different ways. One is delivery reliability which is the
ability consistently meet promised delivery dates; second is the ability to deliver orders to
customer with shortest lead times [2].
In today’s production thinking, manufacturing companies are striving to reduce the risk of failure
in meeting due-dates by controlling the performance. Failing to meet due dates results inventory
carrying cost when early job completion exists and penalties for a tardy job completion. The due
date assignment methods consist of making an estimate of flow times for a certain job depending
on the shop utilization ratio and setting a due date on the basis of the estimation with some
performance criteria.

30
The present paper focus on a simulation and comparison of the interaction between due-date
assignment methods and scheduling rules in a job shop production system. Two due-date
assignment methods and three scheduling rules are considered for the investigation. The
performance of the case industry job shop system is evaluated using tardiness as a parameter
aided with statistical analysis.
The rest of the paper is organized as follow: Section 2 deals with the problem definition used in
the present study. In section 3 the research methodology is presented. Section 4 contains the
literature about shop floor configuration. In section 5 the development of simulation analysis.
This section also describes the simulation experiments. Result and discussion are provided in
section 6. Finally in section 7 a conclusion is given for the study.
2. PROBLEM STATEMENT
The Metal and Engineering Corporation (METEC) is established by the Federal Republic of
Ethiopia (FDRE) under council of minster regulation No 183/2010 in June 2010 [3]. METEC is
comprised of more than 15 semi-autonomous and integrated manufacturing companies that are
operating in different sectors. Hibret manufacturing and machine building industry is one of the
manufacturing company under METEC. HMMBI has five factories: machine building factory,
conventional manufacturing factory, precision machinery factory and material treatment and
engineering factory. HMMBI has a challenge to meet duet of its internal and external customers.
Through a structured interview and questionnaire conducted with production department heads,
the basic procedures and practices in the HMMBI are thoroughly examined. Based on the analysis
the critical problem for the late deliveries in the case industry has been identified. By taking the
actual shop floor data of the case company, the performance of the delivery system is evaluated.
Simulation and comparison analysis are carried in different scenarios through combining due-date
assignment methods and scheduling rules. Finally the performance of the job shop system is
evaluated tardiness parameters including maximum tardiness, number of tardy jobs and total
weighted tardiness.
3. RESEARCH METHODOLOGY
The aim of this research is to compare and select one of the due-date assignment methods for
different scheduling rules based on performance measure parameters. A case industry HMMBI
conventional manufacturing section having a job shop system is considered for investigation in
the present study. The system consists of four work stations namely lathe, milling, heat treatment
and surface finish stations in each station different machine performing different operation. Two
methods from the literature are used for setting due dates of jobs; flow time due date (FTDD) and
total work content (TWK) and three scheduling rules are used for the scheduling of jobs. These
rules include shortest processing time (SPT), earliest due date (EDD) and critical ratio (CR). The
performance measures considered for analysis are maximum tardiness, total tardiness and number
of late jobs. Simulation experiments have been conducted and subjected to a statistical analysis
through different scenarios that arise out of the combination of due-date setting methods and
scheduling rules.
4. JOB SHOP SYSTEM CONFIGURATION
An actual case company HMMBI conventional manufacturing job shop configuration has been
used for investigating the present study. The configuration consists four station having different
machines and perform different operation.

31
4.1. Current Scenario of the Shop Floor
The present study focus on scheduling and sequencing a job shop system having the following
characteristics:
• There are a more than one machines in the four workstations.
• Each machine in each workstation can perform only one operation at a time.
• An operation of a job can be loaded in any of machine in the workstation according
to the process plan of the job order.
• Each machine is continuously available and no machine is idle.
4.2. Job and Machine Data
According to the process plan of the production station each job order consists of a set of
operations to be performed on the different station in the shop. The routing of a job through the
machines is extracted from the process plan document and presented below in tabular form. The
process plan conveys the stations with respect to the machines, the processing time of each
operations of a job. In generating the job data, the case company standard operation plans have
been taken in to consideration.
Table 1: Job and Machine data
4.3. Due-Date Assignment
When a job arrives at shop at the processing, its due date needs to be assigned. Currently the case
industry assigns due dates for customers according to the shop load utilization but due dates
scientifically can be assigned. Among the different due date assignments methods, for this study
two different due date assignment methods are investigated and compared through statistical

32
analysis. Both the selected methods belong to the category of internally set due date’s i.e.
endogenous due-date methods. These methods are discussed as follow. In addition to the
following notations used:
Notations:
DDi = Due date for order I,
ATi = Arrival time for order I.
TWKPi = Total run time for order I.
Ki = Planning factor used for all jobs
TWKSUi = Average setup time for all jobs.
NOPi = Number of operations for order I
Ft= average flow time of a job
Ki = tightness level at the time
λ = average job arrival rate
Up = mean operation processing time
Ug = average number of operation per job
Pij = processing time of operation j of job i
Wij = waiting time of operation j of job i
gi = number of operations in job
E(F) = standard deviation
σ = mean value
Ri = arrival date of the job
Z = shop load ratio
P = shop utilization
m = number of machine
4.3.1 Flow Time due Date Assignment (FTDD) Method
The time a job spends in the shop, from the order release to completion is called its flow time.
The mean job flow time is the basic measure of a shop’s performance at turning around orders,
and it is therefore often used as an indicator of success quickly to customers [8]. Due to the
complexity of job shop structure, flow times elements become uncertain which makes prediction
of due dates more difficult. However accurate prediction of flow time in job shop is one of the
most important factors for efficiency of scheduling. If flow time predictions can be significantly
improved through use of shop utilization information errors between actual competitions times
and promised delivery dates can be reduced.
The mean and the standard deviation of job:
Flow-time F is the difference between the arrival time of job J, in the shop and the completion
time of the last. Operation of J" F, can be derived from the sum of the total processing times
and the total waiting times of J, in the shop as follows:
The waiting time, W'j, can be expressed as:

33
The standard deviation and the mean will result in:-
The due date formula is (Di) = Ril +E(F)+Z( ) , Where Ri is arrival date of job and E(f) and
are found from the above formula and Z is shop load ratio, considering the shop utilization
[4].
4.3.2. Total Work Content (TWK) Assignment Method
This model follows the job dependent assignment procedure. The assigned due dates are
proportional to the total estimated processing time of a job. The TWK methodology has been
employed by numerous researchers; it is based on the average setup time of a job, total estimated
processing time and planning factor allowance that accounts for delays.
In this method the due date of each job is set equal to the sum of job arrival time and a multiple
(allowance factor) of the total processing time. For each new order I, the due date can be
calculated as follows: [4]
Dynamic total work content (DTWK) method it is a modification of the TWK method, where in
the due-date planning factor K, is determined using the information about the Status of the job
shop at the time a job arrives at the shop [5].The application of Little’s law (1961) for a job shop
in the steady state operation results in the following relationship, If Ns denote the number of jobs
in the system, denote the average job arrival rate and F denote the job flow time, then Little’s
law for a shop in steady state can be expressed by If it is assumed that the shop load is relatively
steady for a short period of time, then any given time t, it can be approximated the average flow
time of a job Ft, in the shop with Ns number of jobs in this period as:
The dynamic allowance factor for a job newly arrived at time t would be determined by the
current average flow time. Denoting the mean operation processing time and average number of
operations per job by Up and Ug, respectively, we see Up Ug that is the average total job
processing time. Let KI denote the real tightness level at the time t when a new job arrives,
Then KI =

34
5. SCHEDULING RULES
Scheduling in job shop is an important aspect of shop floor management system, which has
significant impact on the performance of the shop-floor. The decision as to which job is to be
loaded on a machine, when the machine becomes free is normally made with dispatching rules
[6].Scheduling rules can be classified in a number of ways, one such classification follows
process-time based rules, and due-date based rules and combination rules. In this simulation study
of job shop system all three ways used, the shortest process-time (SPT) is an example of process
time based rule. This rule has been found to minimize the mean flow time and a good
performance with respect to the mean tardiness. The due-date based rule schedule the rule based
on their due-date information, an example is earliest due date (EDD) mostly used for light load
conditions. A combination rule makes use of both process-time and due-date information,
example is critical ratio (CR).
6. SCHEDULING RULES
The simulation analysis is developed for evaluating the performance of the two due date
assignment methods using scheduling rules. The entities in the job shop system are jobs and
machines. Simulation is done using Lekin scheduler. Based on the simulation results a
comparison analysis is done using ANOVA (SPSS statistics 20).
6.1. Structure of Simulation and Comparison Analysis
In this paper the simulation and comparison analysis are structured in modular way consisting
three modules, each of which performing a specific role.
Table 2 : Simulation and comparison modules
Factors Levels
Due–date assignment module Flow time due date(FTDD)
Total work content(TWK)
Scheduling module Shortest processing time(SPT)
Earliest due date(EDD)
Critical ratio(CR)
Result module Tardy jobs
Average Tardiness
Mean flow time
Due-Date assignment module: This module generates the final due date of the jobs that
arrive at the system. The due date method used are the flow time due date (FTDD) and
total work content (TWK). The number of operations, number of machines required for
operation and processing time of jobs were used from the actual data of the case
company to finally incorporate the due date with the two methods.
Scheduling module: According to the process plan this module contains subroutines to
deal with the scheduling of jobs on machines using various scheduling rules. For
making decision, a scheduling rule is used to assign to each of the waiting jobs, a

35
scheduling rule. As aforementioned on the above sections the study used three
scheduling rules from literature. These are shortest processing time (SPT), earliest due
date (EDD) and critical ratio (CR).
Result module: This module presents the output of the simulation analysis that aids the
comparison analysis of the two assignment methods through performance measures
such as mean flow time, tardy jobs and average tardiness.
Notations:
Cmax-Maximum Span
Tmax- Maximum Tardiness
∑Tj- Total Tardiness
∑WjCj- Total weighted
tardiness
∑Uj- No of late jobs
7. RESULT AND DISCUSSION
By analysing the two due date assignment through three scheduling rules, the job due date and
simulation results for both methods is presented below:
Table 3: Job due date with FTDD and TWK
Job number 1 2 3 4 5 6
Due date using FTDD 17 12 8 13 14 11
Due date using TWK 6 1 1 21 4 2
Table 4: Simulation result FTDD and TWK through scheduling rules
FTDD
DR Cmax Tmax ∑Tj ∑Uj ∑Cj ∑WjTj
SPT 30 16 54 5 129 54
CR 31 17 57 6 132 57
EDD 31 17 60 5 135 60
TWK SPT 30 26 94 6 129 94
CR 33 25 100 6 135 100
EDD 37 27 104 6 139 104
Based on the above simulation result a statistical analyses using the analysis of variance
(ANOVA) procedure in order to study the effect of due-date assignment methods and scheduling
rules on the performance of the job shop system considered. Two factors ANOVA methods are
used where in due-date assignment method and scheduling are the two factors. The least
significant difference (LSD) method was used for performing pairwise comparisons in order to
determine which means differ from the other.
7.1. Statistical Analysis
A two factor Analysis of variance of the experiment result is performed to identify the effect of
due-date assignment methods and scheduling rules on the performance of the job shop system.
The least significant difference (LSD) method was used for performing pairwise comparisons in

36
order to determine which means differ from the other. The ANOVA results are displayed in Table
5 from the analysis; we can draw the following conclusions. It is evident that the percentage of
jobs late depends on:
a) The dispatching rule used
b) The due-date assignment method employed(FTDD and TWK)
c) The interaction between the dispatching rule and the due-date assignment method
From the interaction matrix (Due date*Dispatching rule) and plots of their mean values we can
draw a conclusion that the FTDD method result less value than TWK for all scheduling categories
used.
Table 5: A two factor variance analysis result
Tests of Between-Subjects Effects
Source Dependent
Variable
Type III
Sum of
Squares
df Mean
Square
F
Corrected Model Cmax 36.000a
5 7.200 .
Tmax 133.333a
5 26.667 .
Tj 2756.833a
5 551.367 .
Uj 1.333a
5 .267 .
Cj 76.833a
5 15.367 .
WjTj 2756.833a
5 551.367 .
Intercept Cmax 6144.000 1 6144.000 .
Tmax 2730.667 1 2730.667 .
Tj 36660.167 1 36660.167 .
Uj 192.667 1 192.667 .
Cj 106400.167 1 106400.167 .
WjTj 36660.167 1 36660.167 .
Duedate Cmax 10.667 1 10.667 .
Tmax 130.667 1 130.667 .
Tj 2688.167 1 2688.167 .
Uj .667 1 .667 .
Cj 8.167 1 8.167 .
WjTj 2688.167 1 2688.167 .
Disprule Cmax 16.000 2 8.000 .
Tmax 1.333 2 .667 .
Tj 64.333 2 32.167 .
Uj .333 2 .167 .
Cj 64.333 2 32.167 .
WjTj 64.333 2 32.167 .
Duedate*Disule Cmax 9.333 2 4.667 .
Tmax 1.333 2 .667 .
Tj 4.333 2 2.167 .
Uj .333 2 .167 .
Cj 4.333 2 2.167 .
WjTj 4.333 2 2.167 .
Error Cmax .000 0 .
Tmax .000 0 .

37
Table 6: Estimated Mean Value with Due Date* Dispatching Rule matrix
CR 31.000
EDD 31.000
SPT 30.000
CR 33.000
EDD 37.000
SPT 30.000
CR 17.000
EDD 17.000
SPT 16.000
CR 25.000
EDD 27.000
SPT 26.000
CR 57.000
EDD 60.000
SPT 54.000
CR 100.000
EDD 104.000
SPT 94.000
CR 6.000
EDD 5.000
SPT 5.000
CR 6.000
EDD 6.000
SPT 6.000
CR 132.000
EDD 135.000
SPT 129.000
CR 135.000
EDD 139.000
SPT 129.000
CR 57.000
EDD 60.000
SPT 54.000
TWK CR 100.000
EDD 104.00
SPT 94.00
WjTj
FTDD
Uj
FTDD
TWK
Cj
FTDD
TWK
Tmax
FTDD
TWK
Tj
FTDD
TWK
Dependent
Variable
Due date
Dispatchin
g rule
Mean
Cmax
FTDD
TWK

38
Figure 1: Estimated Marginal Means of Cmax
Figure 2: Estimated Marginal Means of Tmax and Tj

39
Figure 3: Estimated of Marginal Means of Uj and Cj
Figure 4: Estimated Marginal Means of WjTj
8. CONCLUSIONS
Researches in job shop production system simulation studies presented that the assignment of due
dates is done in an environment that differs greatly from the environment in the production
control department [7]. One difference is in the setting of due dates. In the operating situation,
each job has many characteristics that may be combined to produce a due date; many, if not all, of
non-quantities factors associated with real jobs are not present in simulation studies [7]. In this
paper the simulation analysis considers the actual environment in the production control of the
case company; this will reduce and compensate any arbitrary assumptions made. The purpose of

40
this study is to investigate two due date assignment models FTDD and TWK based on the three
scheduling rules (SPT, CR and EDD) in a job shop production system. An attempt to find
functional interaction between due-date assignment method and dispatching rule is accomplished
through the use of simulation and comparison analysis. The performance of the system is
evaluated based on maximum tardiness, number of tardy jobs and total weighted tardiness. Based
on the findings of the study it can be concluded that the combined effect of scheduling rules and
due-date assignment methods result more reliable due dates with FTDD model compared with
TWK through SPT, EDD and CR dispatching rules in a job. Therefore, HMMBI is recommended
to use FTDD whenever there is a need to combine scheduling rules with due-date assignment
methods.
REFERENCES
[1] P.B.C. Lawrence M.Wein, “A broder view of the job shop scheduling problem,” Mangment Science,
p.7, 1992.
[2] A.Baykasong'lu, “New Approaches to Due Date Assignment in Job Shops,” European Journal of
Operation Research, p.15, 2007.
[3] B.B.D.K.a.F.G. Ameha M., “Outsourcing as Means of Technological Capablity Development,” 12th
Globalics International Conference, 2014.
[4] T.Cheng, “Simulation Study of Job Shop Scheduling with Due Dates,” International Journal of
System Science, pp.5-16, 2007.
[5] V.a.R.Sridharan, “Simulation Modeling and Analysis of Due Date Assignment Methods and
Scheduling Decistion Rules in Dynamic Job Shop Production System,” International Journal
Production Economics, p. 130, 2010.
[6] O.H.a.C.Rajendran, “Efficient Dispatching rules for scheduling in a job shop,” International journal of
production Economics, p.1, 1997.
[7] M.L.Smith, “Due Date Selection Procedure For Job-Shop Simulation,” Computer and Industrial
Engineering, p.7, 1983.
[8] K.R.Baker, “Sequencing Rules and Due-Date Assignments in a Job shop,” Mangment Science,
vol.30, no.9, pp. 1093-1104, 1984.
AUTHORS
Fitsum Getachew was born in Addis Ababa, Ethiopia, in 1991 G.C. He received BSc
degree in Mechanical Engineering from Addis Ababa University, Addis Ababa Institute
of Technology, Ethiopia, in2012/13. Currently he is working as an assistant lecturer and
doing his M.Sc. in Industrial Engineering.
Dr.-Ir. Eshetie Berhan was born in Gonder, Ethiopia, 1974. He received PhD in
Mechanical Engineering on July 4, 2013. MSC in Industrial Management on September
15, 2005.MSC in Management, Economics & Consumer Studies, on March 10, 2005.
BSC in Computer Science on April 15, 2010 and BSC in Industrial Engineering on July
14, 20101. Currently he is working as Director of Research, Technology Transfer and
University Industry Linkage, in Addis Ababa University, Addis Ababa Institute of
Technology, Ethiopia.

SIMULATION AND COMPARISON ANALYSIS OF DUE DATE ASSIGNMENT METHODS USING SCHEDULING RULES IN A JOB SHOP PRODUCTION SYSTEM

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