Post on 24-Aug-2020
Process Planning and Scheduling with SLK Due-
Date Assignment where Earliness, Tardiness and
Due-Dates are Punished
Halil Ibrahim Demir, Ozer Uygun, Ibrahim Cil, Mumtaz Ipek, and Meral Sari Industrial Engineering Department, Sakarya University, Sakarya, Turkey
Email: {hidemir, ouygun, icil, ipek, meralsari}@sakarya.edu.tr
Abstract—Traditionally process planning, scheduling and
due date assignment are treated separately. Some works are
done on integrated process planning and scheduling and on
scheduling with due-date assignment. However integrating
all of these functions is not treated. Since scheduling
problems alone belong to NP-hard class problems,
integrated problems are even harder to solve. In this study
process planning and scheduling and SLK due date
assignment are integrated using genetic algorithms and
Random search techniques. Earliness, Tardiness and length
of due-dates are punished. While earliness and tardiness
are punished quadratically, due-date is punished linearly.
Three results were compared. One is ordinary solution,
another one is random search solution and the third one is
genetic algorithm solution. Genetic algorithm solution
outperforms the other solutions and Random search
solution is the second best and ordinary solution is the
worst solution.1
Index Terms—process planning, scheduling, due-date
assignment, genetic algorithm, random search
I. INTRODUCTION
Process planning, scheduling and due-date assignment
are three important functions and traditionally they are
treated separately. In this competitive era firms should
work more efficient and keep promises they gave, be
successful in process planning, scheduling and due-date
assignment. Since these three functions are done
separately, they do not care each other and independently
applied functions cause great loss in global performance.
If we look at each function one by one; Process planning
has been defined by Society of Manufacturing Engineers
as the systematic determination of the methods by which
a product is to be manufactured economically and
competitively. As we will see alternative process plans
help to improve quality of scheduling and due-date
assignment. Outputs of process plans are inputs to
scheduling. If process planners do not care about
scheduling and due date assignment they can prepare
poor inputs and they can select some machines
repeatedly and cause bottleneck machines in shop floor.
This can cause unbalanced workload in shop floor. If
there are no alternative process plans then we have no
Manuscript received June 27, 2014; revised November 1, 2014.
flexibility when required. In case of congestion and
customer demand it will be helpful to have alternative
process plans to use idle resource or to response
customer demand better.
According to Zhang and Mallur [1] production
scheduling is a resource allocator, which considers
timing information while allocating resources to the tasks.
Developments in computer makes easy and quality
process plans possible, that’s why it becomes easy to
prepare alternative process plans. Scheduling with
flexible process plans may allow us to take corrective
action in case of need, So that we can redirect jobs from
congested machines to starving machines. This causes
better balanced workshop load and increases throughput
rate and with alternative process plans we can handle
machine breakdowns and unexpected occurrences.
Due dates are determined internally or externally. If
due dates are determined externally we should meet due
dates otherwise we pay for tardiness. In JIT environment
earliness is also undesired so we try to minimize
earliness and tardiness. That’s why integration of process
planning and scheduling with due-dates helps us to
minimize earliness and tardiness cost. If due-dates are
determined internally we should determine best due-date
that minimizes some costs, such as; Tardiness, Earliness
and length of due-date. Tardiness are undesired and it
means loss of customer good-will or even we can lose
customer, Earliness is also unwanted because of
inventory holding cost, space and spoilage etc. Length of
due dates are also important to consider. Long due dates
can cause to lose customer or can be a reason in price
reduction. That’s why we should minimize earliness,
tardiness and due-date related costs and give a due-date
to a customer that minimizes some function of these
costs.
II. RELATED RESEARCHES
There are numerous work done on integrated process
planning and scheduling and about scheduling with due-
date assignment. But integration of these three functions
are not mentioned much in the literature. Demir et al [2]
mentioned about integrated process planning scheduling
and due-date assignment. In this study integration of
process planning and scheduling with SLK due-date
assignment was studied. Earliness, Tardiness and due-
Journal of Industrial and Intelligent Information Vol. 3, No. 3, September 2015
2015 Engineering and Technology Publishing 173doi: 10.12720/jiii.3.3.173-180
dates are punished. Earliness and Tardiness are
symmetrically and quadratically and due-dates are
linearly punished.
We mentioned that there are numerous work on
integrated process planning and scheduling. If we
mention some of these researches; Nasr and Elsayed [3]
developed two algorithms to minimize the mean flow
time in job shop scheduling with alternative machine tool
routings. In general, the proposed algorithms present
efficient solutions to the scheduling problem in such a
system. These algorithms are able to solve large scale
problems in a very short time. Khosnevis and Chen [4]
addressed the basic issues involved in the integration of
the two functions, demonstrated a methodology and the
potential benefits of the integration approach by an
example. Hutchinson et al [5] examined the influences
that scheduling schemes and the degree of routing
flexibility have on random, job shop flexible
manufacturing systems within a static environment. Jiang
and Chen [6] investigated the relationships between the
alternate process planning and the scheduling
performance regarding to three different criteria; they are
mean tardiness, mean work-in-process, and mean
machine utilization. Chen and Khoshnevis [7]
investigated the problem of integrating the process
planning and scheduling functions as a scheduling
problem with flexible process plans. Zhang and Mallur
[1] proposed an integrated model for process planning
and job shop scheduling. Their approach can lead to
more structured decision making on the shop floor and
hence the creation of feasible plans.
While exploiting process plan flexibility in production
scheduling, Brandimarte [8] used multi-objective
approach. The problem of solving scheduling problems
involving multiple process plans per job was addressed
by Kim and Egbelu [9]. In their research Morad and
Zalzala [10] used genetic algorithm and they considered
also some other objectives other than time aspects. These
objectives are minimizing makespan, minimizing total
rejects produced and minimization of total cost of
production. Ming and Mak [11] tried to provide a good
quality solution to the process plan selection by using a
hybrid Hopfield network-genetic algorithm. Tan and
Khoshnevis [12] presented a review on integrated
process planning and scheduling. Lee and Kim [13]
proposed simulation based genetic algorithm as a new
approach to the integration of process planning and
scheduling.
To handle the two functions, process planning and
scheduling, at the same time Kim et al [14] presented a
new method using artificial intelligent search technique,
called symbiotic evolutionary algorithm. Kumar and
Rajotia [15] studied the integration of CAPP (Computer
Aided Process Planning) and scheduling. Usher [16]
addressed the benefit of alternative process plans and
worked on the number of alternative process plans. For
the purpose of increasing the responsiveness of adaptive
manufacturing systems in accommodating dynamic
market changes, Lim and Zhang [17] aimed to develop a
system to integrate dynamic process planning and
dynamic production scheduling. For the integration of
process planning and scheduling problem, a linearized
polynomial mixed-integer programming model (PMIPM)
was presented by Tan and Khoshnevis [18]. Kumar and
Rajotia [19] proposed a framework for integration of
process planning with production scheduling. Ueda et al
[20] proposed a new simultaneous process planning and
scheduling method. Moon et al [21] studied integrated
process planning and scheduling (IPPS) in a supply chain.
Guo et al [22] developed a unified representation
model for integrated process planning and scheduling
(IPPS). Li et al [23] presented a review on integrated
process planning and scheduling. In order to integrate
process planning and shop floor scheduling (IPPS)
Leung et al [24] presented an ant colony optimization
(ACO) algorithm in an agent-based system. Phanden et
al [25] presented a state-of-the-art review of IPPS
(Integration of process planning and scheduling)
If we look at the literature we see that it is hard to
solve integrated problems. Some solutions are only
possible for small problems. For IPPS at the literature
people use genetic algorithms, evolutionary algorithms
or agent based approach for integration, or they
decompose problems because of complexity of the
problem. They decompose problems into loading and
scheduling subproblems. They use mixed integer
programming at the loading part and heuristics at the
scheduling part.
Due-date performance is becoming increasingly
important in today’s competitive environment. Gordon et
al [26] presented a state of the art review on scheduling
with due date assignment. There are two aspects of due-
date performance; delivery reliability and delivery speed
[27]. Delivery reliability is the ability to constantly meet
promised delivery dates. Delivery speed is the ability to
deliver orders to the customer with short leadtimes.
Customers are less willing to accept long promised
leadtimes Philipoom [28]. At literature there are
numerous work done on scheduling with due-date
assignment. In this study SLK due-date assignment is
integrated with scheduling and process planning using
genetic algorithm. Experiment results shows the benefits
of integration of SLK due-date assignment
In his study Baker [29] studied the interaction between
sequencing priorities and the method of assigning due-
dates. Cheng [30] who contributed a lot to this area
studied optimal due-date assignment in a job shop.
Cheng [31] presented a study of a hypothetical job shop
by computer simulation. He aimed to investigate the
main and interaction effects of three shop decision
variables: the job dispatching rule, the due-date
assignment method and, shop load ratio on a shop
performance measure. Luss and Rosenwein [32]
developed a heuristic, due date assignment algorithm, for
multiproduct manufacturing facilities to solve the order
acceptance problem. Their objective was to minimize the
sum of weighted (positive) deviations of the assigned due
dates from the requested dates. Lawrence [33] presented
a methodology for negotiating due-dates between
customers and producers in complex manufacturing
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environments. In their paper Yang et al [34] studied the
generalized job shop scheduling problem with due dates
wherein the objective is to minimize total job tardiness.
For solving this problem they presented an efficient
heuristic algorithm called revised exchange heuristic
algorithm (REHA). Kahlbacher and Cheng [35] studied
the problem of scheduling n jobs on a single machine.
Each job is assigned a processing-plus-wait due date and
objective was to minimize the symmetric earliness and
tardiness costs.
There are many works on single machine scheduling
and due date assignment. Qi et al [36] studied a new
subclass of machine scheduling problems in which due
dates are treated as variables and must be assigned to the
individual jobs. A solution then is a sequence of jobs
along with due date assignments. Lauff and Werner [37]
studied multi-machine problems and penalized earliness
and tardiness and scheduled jobs with common due date.
Cheng et al. [38] studied the problem of scheduling jobs
whose processing times are decreasing functions of their
starting times. They considered the case of a single
machine and a common decreasing rate for the
processing times. The problem was to determine an
optimal combination of the due date and schedule so as
to minimize the sum of due date, earliness and tardiness
penalties. Wang [39] considered a single machine
scheduling problem with a common due date. Job
processing times were controllable to the extent that they
can be reduced, up to a certain limit, at a cost
proportional to the reduction.
Xia et al [40] considered due date assignment and
sequencing for multiple jobs in a single machine shop.
The processing time of each job was assumed to be
uncertain and was characterized by a mean and a
variance with no knowledge of the entire distribution.
Shabty and Steiner [41] studied two due-date assignment
problems in various multi-machine scheduling
environments. They assumed that each job can be
assigned an arbitrary non-negative due date, but longer
due dates have higher cost. At the first function they
included earliness, tardiness and due date assignment
costs. In the second problem they minimized an objective
function that includes number of tardy jobs and due date
assignment costs. Baykasoğlu et al [42] studied new
approaches to due date assignment in job shops. Their
primary objective was to compare the performance of the
proposed due date assignment model (PDDAM) with
several conventional due date assignment models
(CDDAM).
Gordon and Strusevich [43] considered single machine
scheduling and due date assignment problems in which
the processing time of a job depends on its position in a
processing sequence. The objective functions include the
cost of changing due the due dates, the total cost of
discarded jobs that cannot be completed by their due
dates and, possibly, the total earliness of the scheduled
jobs. Tuong and Soukhal [44] studied due date
assignment and just-in-time scheduling for single
machine and parallel machine problems with equal-size
jobs where the objective is to minimize the total
weighted earliness-tardiness and due date cost. Vinod
and Sridharan [45] presented salient aspects of a
simulation study conducted to investigate the interaction
between due-date assignment methods and scheduling
rules in a typical dynamic job shop production system.
The due-date assignment methods investigated are
dynamic processing plus waiting time, total work content,
dynamic total work content and random work content
method.
In their article Yin et al [46] addressed a single
machine scheduling problem with simultaneous
consideration of due-date assignment, generalised
position-dependent deteriorating jobs, and deteriorating
maintenance activities. Lu et al [47] considered a single-
machine earliness-tardiness scheduling problem with
due-date assignment, in which the processing time of a
job is a function of its position in a sequence and its
resource allocation. Hazır and Sidhoum [48] addressed
the optimal batch sizing and just-in-time scheduling
problem. Their objective was to find a feasible schedule
that minimizes the sum of the weighted earliness and
tardiness penalties as well as the setup costs. Yin et al
[49] studied batch delivery scheduling on a single
machine, where a common due-date is assigned to all the
jobs and a rate-modifying activity on the machine may
be scheduled, which can change the processing rate of
the machine
III. PROBLEM DEFINITION
In this study we studied integration of IPPS
(Integrated process planning and scheduling) with Due-
date assignment (with SLK due date assignment). There
are alternative routes of each job in a job shop
environment and we have alternative dispatching rules
(eight dispatching rules with different multipliers we
have ten dispatching rules) and we have SLK due date
assignment method (SLK due date assignment represents
internal due date assignment). For the comparison we
also tested RDM (Random) due date assignment that
represent external due date assignment.
TABLE I. SHOP FLOORS
Shop floor Shop floor 1 Shop floor 2 Shop floor 3
# of machines 20 30 40
# of Jobs 50 100 200
# of Routes 5 5 3
Processing Times ⌊(10 + |z| ∗ 3)⌋ ⌊(10 + |z| ∗ 3)⌋ ⌊(10 + |z| ∗ 3)⌋ # of op. per job 10 10 10
We have three sized shop floor. First is small shop
floor with 50 jobs and 20 machines, second is medium
sized shop floor with 100 jobs 30 machines and third is
big sized shop floor with 200 jobs and 40 machines. At
small and medium sized shop floors, jobs have five
alternative routes and at the big sized shop floor, jobs
have three alternative routes. In every case we have 10
operations for each job. Processing times practically
change in between 10 and 19 according to the given
formula (Processing times = ⌊(10 + |z| ∗ 3)⌋ = nearest
small integer in between 10 and 19. |z| is absolute value
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of standard normal numbers). Processing times are
randomly produced. Characteristics of the shop floors are
given at Table I.
In this study we penalized earliness and tardiness
symmetrically. We accepted one day as one shift and 8
hours. 8 hours make 8*60 = 480 minutes so one day is
480 minutes. If jobs are early and late within one day we
used linear punishment (Since quadratic punishment is
very small). If jobs are early and late for more than one
day then we used quadratic punishment. In case of due
date we penalized length of due date linearly.
Punishment functions are given below where PD is
penalty for due-date, PE is penalty for earliness and PT is
penalty for tardiness;
IV. USED TECHNIQUES
In this study three solution techniques are compared.
We compared genetic search (genetic algorithm)
technique with random search technique and with
ordinary solution.
Genetic Search: At genetic search we used crossover
and mutation operator and produce two population,
crossover population and mutation population and with
the final-old population we compare three populations
and select n best chromosome (solution) as the new
population. This makes one iteration. We repeat
iterations until a predetermined value and it changes
according to the size of the shop.
Random Search: At random search, at each iteration
we produced new random solutions as big as crossover
population and mutation population. Among these three
populations (final main population and two new
produced populations) we selected best n solutions for
the new main population and we finish one iteration and
pass to the next iteration. At random search we produce
two new populations equal sized with crossover and
mutation population so that comparison of random
search and genetic search becomes fairer.
Figure 1. Sample chromosome.
Ordinary solution: At ordinary solution we randomly
produced three population. One is main population and
two populations equally sized with crossover population
and mutation population. Later we chose n best solutions
for the initial main population. This is like one iteration
in genetic search and random search. We used these
solutions as ordinary solutions. Later we compared these
solutions and made comment on these solutions.
We represented solutions (individuals) as chromosome
which consists of number of jobs plus two genes for
SLK due-date assignment and dispatching rules as
illustrated in Fig. 1.
Due dates were assigned using mainly two different
types of rules. Considering different constants the first
gene took one of four values. These rules are explained
below at Table II and at Appendix A:
TABLE II. DUE-DATE ASSIGNMENT RULES
Method Multiplier k Constant qx Rule no:
SLK qx = q1,q2,q3 1,2,3
RDM 4
In order to dispatch eighth different methods were
used. Considering different multipliers, the second gene
took one of ten different values. Dispatching rules are
given and explained at Table III and at Appendix B.
TABLE III. DISPATCHING RULES
Method Multiplier Rule no
ATC k x =1,2,3 1,2,3
MS 4
SPT 5
LPT 6
SOT 7
EDD 8
ERD 9
SIRO 10
V. COMPARED SOLUTIONS
SCH-SLK (Ordinary): Process planning, Scheduling
and SLK due date assignment are integrated and ordinary
solution is taken. Ordinary means just one iteration
applied and initial population is taken without making
any directed and undirected search. SLK means due-
dates are not externally (randomly and predetermined)
determined rather it is internally and integrally
determined with dispatching rules and route selection.
SCH-RDM (Ordinary): Process planning and
scheduling integrated, due dates are randomly
determined that represent external due-dates. Ordinary
solution is taken. No directed or undirected search is
applied.
SIRO-RDM (Ordinary): In this solution scheduling
and due-date determinations are unintegrated. One of
alternative routes is selected and SIRO (Service in
random order) dispatching rule is used to represent
unintegrated scheduling and RDM (Random) due-date
determination is used to represent unintegrated due-date
assignment.
SCH-SLK (Random): Above solutions were first
iteration solutions (No directed or undirected search are
applied) but this solutions apply undirected search. For
small shop floor we apply 200 random iterations, for
PE= 8*(E/480) If E <= 480 8*(E/480)* (E/480) If E > 480
PT= 8*(T/480) If T <= 480 8*(T/480)* (T/480) If T > 480
P.D= 8*(Due-date/480) (1)
(2)
(3)
DD DR R1j R2j
. . . R nj
Where
DD : Due date assignment gene DR : Dispatching rule gene
R nj : j’th route of job n
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medium sized shop floor we apply 100 iterations and for
large shop floor we apply 50 random iterations. Here
scheduling and SLK due-date assignment are integrated
with process planning.
SCH-SLK (Genetic): Here scheduling and SLK due-
date assignment are integrated with process planning.
Genetic (Directed) search is applied to the problem. For
small shop we apply 200 genetic iterations, for mid-size
shopfloor we apply 100 genetic iterations and for large
shop floor we apply 50 genetic iterations. Since genetic
(directed) search is better than random (undirected)
search we used genetic iterations for the solutions below.
SCH-RDM (Genetic): Here scheduling is integrated
with process planning but due-dates are determined
randomly and genetic search is applied.
SIRO-RDM (Genetic): In this solution scheduling and
due-date determination are unintegrated with process
planning. SIRO dispatching rule is used and RDM due-
date assignment is used and genetic search is applied.
We compared above seven solutions with each other
to determine whether integration of scheduling with
process planning is beneficial and whether integration of
due-date assignment with process planning and
scheduling is beneficial. We also tested directed and
undirected search for three shop floors and genetic search
is found better. Results are given at experimentation part
and interpreted at conclusion part.
VI. EXPERIMENTATION
We coded shop floor at C++ language and run the
program on a Laptop with 2 GHz processor. We
produced data for three shop floors and coded integrated
process planning, scheduling and due-date assignment
problem and coded random and genetic search. For
different shop floors we applied given number of random
and genetic iterations. For small shop floor we used 200
iterations and it took approximately 100 seconds to finish
200 iterations. For medium sized shop floor we applied
100 iterations and it took approximately 300 seconds to
finish 100 iterations. For large shop floor we applied 50
iterations and it took 600 seconds to finish 50 iterations.
We tested three shop floors for seven types of
solutions. We first looked at unintegrated process
planning scheduling and due-date assignment as SIRO-
RDM (Genetic) and SIRO-RDM (Ordinary). Later we
integrated scheduling with process planning and used
Random due-date assignment. At these solutions we
looked at SCH-RDM (Genetic) and SCH-RDM
(Ordinary) solutions. Finally we integrated process
planning, scheduling and SLK Due-date assignment and
looked at solutions SCH-SLK (Genetic), SCH-SLK
(Random), SCH-SLK (ordinary). Explanations of these
solutions are given at section 5.
We tested three shop floors for seven types of
solutions. First shop floor is small shop floor and there
are 20 machines, 50 jobs with 10 operations each and
each job has 5 alternative process plans and processing
times of each operation changes in between 10 and 19
according to following formula ⌊(10 + |z| ∗ 3)⌋ . We
compared seven solutions and three of them are ordinary
solutions and use first step (initial step, can be considered
as first iteration) solutions. One of them use random
search and we make 200 random iterations. Three of the
solutions use genetic search and we apply 200 genetic
search iterations. Results are given below for the small
shop floor. Getting ordinary solution occurs at the very
beginning of the iterations so negligible time required for
ordinary solutions. Applying 200 random or genetic
iterations require approximately 100 seconds (cpu time).
Times are given at Table IV at the last column. Results
of small shop floor are given at Table IV and at Fig. 2.
According to results the more the solutions are integrated
the results are better and the more search iteration
applied the solution is better and directed search
outperform undirected search.
TABLE IV: COMPARISON OF SEVEN TYPES OF SOLUTIONS FOR SMALL
SHOP FLOOR
Worst Avarage Best Cpu time
SCH-SLK (O) 424,24 308,82 238,54
SCH-RDM(O) 448,56 419,08 390,68
SIRO-RDM(O) 430,92 423,04 413,05
SCH-SLK (R) 237,34 236,05 233,82 115sec
SCH-SLK (G) 227,9 226,38 225,33 92sec
SCH-RDM(G) 376,16 375,69 374,93 111sec
SIRO-RDM(G) 399,12 397,64 394,82 89sec
TABLE V: COMPARISON OF SEVEN TYPES OF SOLUTIONS FOR MEDIUM
SHOP FLOOR
Worst Avarage Best Cpu time
SCH-SLK (O) 840,27 676,03 598,33
SCH-RDM(O) 893,2 827,32 755,19
SIRO-RDM(O) 830,59 806,89 784,22
SCH-SLK (R) 574,6 569,63 562,73 326sec
SCH-SLK (G) 541,55 540,3 539,03 261sec
SCH-RDM(G) 732,89 732,35 731,85 303sec
SIRO-RDM(G) 774,45 772,71 768,9 250sec
Figure 2. Small shop floor results
200
250
300
350
400
450
500
W O R S T A V A R A G E B E S T
S M A L L S H O P F L O O R
SCH-SLK (O) SCH-RDM(O) SIRO-RDM(O)
SCH-SLK (R) SCH-SLK (G) SCH-RDM(G)
SIRO-RDM(G)
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Similar results are found for the second mid-size shop
floor and these results are given at the following Table V
and Fig. 3.
Figure 3. Medium shop floor results
Third shop floor is the biggest shop floor and similar
results are found in this shop floor. Results are
summarized at the following Table VI and Fig. 4.
TABLE VI: COMPARISON OF SEVEN TYPES OF SOLUTIONS FOR LARGE
SHOP FLOOR
WORST AVG. BEST CPU TIME
SCH-SLK (O) 2069,26 1837,38 1664,24
SCH-RDM(O) 2061,26 1896,34 1770,73
SIRO-RDM(O) 1936,56 1910,79 1886,14
SCH-SLK (R) 1559,32 1547,93 1531,39 628sn
SCH-SLK (G) 1475,92 1473,64 1467,52 507sn
SCH-RDM(G) 1706,57 1704,1 1701,61 814sn
SIRO-RDM(G) 1813,14 1808,31 1797,27 515sn
Figure 4. Large shop floor results
VII. CONCLUSION
In this study we tried to integrate process planning,
scheduling and SLK due-date assignment. We compared
different integration level. At first we took scheduling
and due-date assignment as unintegrated we solved
problem for SIRO-RDM (Genetic) and SIRO-RDM
(Ordinary). Here we assumed that scheduling is
unintegrated and we used SIRO (Service in random order)
dispatching. We also assumed due-date determination is
unintegrated and we used RDM (Random) due-date
assignment in place of external, unintegrated due-date
determination.
Later we integrated scheduling with process plan
selection. At solution (chromosome), at dispatching rule
gene we used with multipliers ten different dispatching
rules. These dispatching rules are given at section 4.
Here still we determined due-date randomly (externally)
and we didn’t integrate due date determination with
process plan selection and scheduling. We solved
problem for SCH-RDM (Genetic) and for SCH-RDM
(Ordinary).
Later we integrated three functions (process planning,
scheduling and due-date assignment). At solution
(chromosome), at scheduling gene we used ten
dispatching rules and at due-date assignment gene we
used 4 types of due-date assignment, with three different
slack constant we used three different SLK due date
assignment and we used one RDM (Random) due-date
assignment method. Here we solved problem for SCH-
SLK (Genetic), SCH-SLK (Random), and SCH-SLK
(Ordinary) cases. At genetic search we repeated genetic
iterations up to 200, 100 and 50 iterations for small,
medium and large sized shop floors. At Random search
we applied these many random iterations for three
different shop floors. Totally these seven types of
solutions and their explanations are given at section 5.
If these three functions are performed sequentially and
separately then they can become very poor input to the
next stage. Increasing competitiveness force firms to
determine more reliable due-dates and keep their
promises and force them to be flexible and to be
successful at shop floor utilization and scheduling. If
process plans are not integrated with scheduling and due-
date determination then poor inputs can be produced for
the downstream. Repeatedly most desired machines can
be selected and these cause unbalanced machine load and
some machines become bottleneck while others starving.
Also unintegrated case do not consider machine
breakdown, unbalanced workload and other unexpected
and unwanted occurrences. If we integrate these three
functions then we can reduce the effect of these troubles,
we can get better loaded shop floor, we can assign better
due date, keep our promise, and satisfy customers.
When we look at the seven types of solution; first
observation is the more integration we succeeded the
better the solution we got. Second observation is directed
and undirected searches give better performance than
ordinary solutions. Final observation we get, genetic
(directed) search outperforms random (undirected) search.
500
600
700
800
900
W O R S T A V A R A G E B E S T
M E D I U M S H O P F L O O R
SCH-SLK (O) SCH-RDM(O)SIRO-RDM(O) SCH-SLK (R)SCH-SLK (G) SCH-RDM(G)SIRO-RDM(G)
1400
1600
1800
2000
W O R S T A V A R A G E B E S T
L A R G E S H O P F L O O R
SCH-SLK (O) SCH-RDM(O) SIRO-RDM(O)
SCH-SLK (R) SCH-SLK (G) SCH-RDM(G)
SIRO-RDM(G)
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APPENDIX A: DUE-DATE ASSIGNMENT RULES
SLK (Slack) Due = TPT + qx
RDM (Random due assign.) Due = N ~
(3*Pav,(Pavg/2 )2)
TPT= total processing time
Pavg= mean processing time of all job waiting
APPENDIX B: DISPATCHING RULES
ATC (Apparent Tardiness Cost): This is composite
dispatching rule, and it is a hybrid of MS and SPT.
MS: Minimum Slack First
SPT: Shortest Processing Time First
LPT: Longest Processing Time First
SOT: Shortest Operation Time First
EDD: Earliest Due-Date First
ERD: Earliest Release Date First
SIRO (Service in Random order): A job among
waiting jobs is selected randomly to be processed.
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Halil Ibrahim Demir was born in Sivas, Turkey in 1971. In 1988 he
got full scholarship and entered Bilkent University, Ankara, Turkey to Industrial Engineering Department. He got his Bachelor of Science
degree in Industrial Engineering in 1993.
In 1993 he went to Germany for graduate study. He studied German up to intermediate level. In 1994 he got full scholarship for graduate study
in USA from ministry of Education of Turkey. In 1997 he got Master of
Science degree in Industrial Engineering from Lehigh University, Bethlehem, Pennsylvania, USA. He is accepted to Northeastern
University, Boston, Massachusetts for Ph.D. study. He finished Ph.D.
courses at this university and completed Ph.D. thesis at Sakarya University, Turkey in 2005 and got Ph.D. degree in Industrial
Engineering.
He started to his academic position at Sakarya University and works as an Assistant Professor at this university.
Ozer Uygun is an Assistant professor at Sakarya University
Ibrahim Cil is a Professor at Sakarya University
Mumtaz Ipek is an Assistant professor at Sakarya University
Meral Sari is Master of Science student at Sakarya University
2015 Engineering and Technology Publishing 180
Journal of Industrial and Intelligent Information Vol. 3, No. 3, September 2015