process planning and scheduling with slk due- date assignment … · 2014. 12. 16. · due-date...

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

mailto:[email protected]

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

2015 Engineering and Technology Publishing 174


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



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



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)



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


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


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)



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.

REFERENCES

[1] H. C. Zhang and S. Mallur, “An integrated model of process planning and production scheduling,” International Journal of

Computer Integrated Manufacturing, vol. 7, no. 6, pp. 356-364,

November 1994. [2] H. I. Demir, H. Taskin, and T. Cakar, “Integrated process

planning, scheduling and due-date assignment,” in Proc. 4th International Symposium on Intelligent Manufacturing Systems,

Sakarya, 2004, pp. 1165-1175.

[3] N. Nasr and E. A. Elsayed, “Job shop scheduling with alternative machines,” International Journal of Production Research, vol. 28,

no. 9, pp. 1595-1609, September 1990.

[4] B. Khoshnevis and Q. M. Chen, “Integration of process planning and scheduling functions,” Journal of Intelligent Manufacturing,

vol. 2, no. 3, pp. 165-175, June 1991.

[5] J. Hutchinson, K. Leong, D. Snyder, and P. Ward, “Scheduling approaches for random job shop flexible manufacturing systems,”

International Journal of Production Research, vol. 29, no. 5, pp.

1053-1067, May 1991. [6] J. Jiang and M. Y. Chen, “The influence of alternative process

planning in job shop scheduling,” Computers and Industrial

Engineering, vol. 25, no. 1-4, pp. 263-267, January 1993. [7] Q. Chen and B. Khoshnevis, “Scheduling with flexible process

plans,” Production Planning & Control, vol. 4, no. 4, pp. 333-343,

1993. [8] P. Brandimart, “Exploiting process plan flexibility in production

scheduling: A multi-objective approach,” European Journal of

Operational Research, vol. 114, no. 1, pp. 59-71, April 1999. [9] K. H. Kim and P. J. Egbelu, “Scheduling in a production

environment with multiple process plans per job,” International

Journal of Production Research, vol. 37, no. 12, pp. 2725-2753, August 1999.

[10] N. Morad and A. Zalzala, “Genetic algorithms in integrated

process planning and scheduling,” Journal of Intelligent Manufacturing, vol. 10, no. 2, pp. 169-179, April 1999.

[11] X. G. Ming and K. L. Mak, “A hybrid Hopfield-genetic algorithm

approach to optimal process plan selection,” International Journal of Production Research, vol. 38, no. 8, pp. 1823-1839, May 2000.

[12] W. Tan and B. Khoshnevis, “Integration of process planning and

scheduling–a review,” Journal of Intelligent Manufacturing, vol. 11, no. 1, pp. 51-63, February 2000.

[13] H. Lee and S. S. Kim, “Integration of process planning and

scheduling using simulation based genetic algorithms,” International Journal of Advanced Manufacturing Technology,

vol. 18, no. 8, pp. 586-590, October 2001.

[14] Y. K. Kim, K. Park, and J. Ko, “A symbiotic evolutionary algorithm for the integration of process planning and job shop

scheduling,” Computers and Operations Research, vol. 30, no. 8,

pp. 1151-1171, July 2003.

[15] M. Kumar and S. Rajotia, “Integration of scheduling with

computer aided process planning,” Journal of Materials

Processing Technology, vol. 138, no. 1-3, pp. 297-300, July 2003.

[16] J. M. Usher, “Evaluating the impact of alternative plans on manufacturing performance,” Computers and Industrial

Engineering, vol. 45, no. 4, pp. 585-596, December 2003.

[17] M. K. Lim and D. Z. Zhang, “An integrated agent-based approach for responsive control of manufacturing resources” Computers

and Industrial Engineering, vol. 46, no. 2, pp. 221-232, April

2004. [18] W. Tan and B. Khoshnevis, “A linearized polynomial mixed

integer programming model for the integration of process

planning and scheduling” Journal of Intelligent Manufacturing, vol. 15, no. 5, pp. 593-605, October 2004.

[19] M. Kumar and S. Rajotia, “Integration of process planning and

scheduling in a job shop environment,” The International Journal of Advanced Manufacturing Technology, vol. 28, no. 1-2, pp. 109-

116, February 2006.

[20] K. Ueda, N. Fujii, and R. Inoue, “An emergent synthesis approach to simultaneous process planning and scheduling,” CIRP Annals-

Manufacturing Technology, vol. 56, no. 1, pp. 463-466, 2007.

[21] C. Moon, Y. H. Lee, C. S. Jeong, and Y. Yun, “Integrated process planning and scheduling in a supply chain,” Computers and

Industrial Engineering, vol. 54, no. 4, pp. 1048-1061, May 2008.

[22] Y. W. Guo, W. D. Li, A. R. Mileham, and G. W. Owen, “Optimization of integrated process planning and scheduling

using a particle swarm optimization approach,” International

Journal of Production Research, vol. 47, no. 4, pp. 3775-3796, July 2009.

[23] X. Li, L. Gao, C. Zhang, and X. Shao, “A review on integrated

process planning and scheduling,” International Journal of Manufacturing Research, vol. 5, no. 2, pp. 161-180, 2010.

[24] C. W. Leung, T. N. Wong, K. L. Mak, and R. Y. K. Fung,

“Integrated process planning and scheduling by an agent-based ant colony optimization,” Computers and Industrial Engineering,

vol. 59, no. 1, pp. 166-180, August 2010.

[25] R. K. Phanden, A. Jaina, and R. Verma, “Integration of process planning and scheduling: A state-of-the-art review,” International

Journal of Computer Integrated Manufacturing, vol. 24, no. 6, pp. 517-534, June 2011.

[26] V. Gordon, J. M. Proth, and C. Chu, “A survey of the state-of-the-

art of common due-date assignment and scheduling research,”

European Journal of Operational Research, vol. 139, no. 1, pp. 1-

25, May 2000.

[27] T. Hill, Production/Operations Management, Text and Cases, New York: Prentice Hall, 1991.

[28] P. R. Philipoom, “The choice of dispatching rules in a shop using

internally set due-dates with quoted leadtime and tardiness costs,” International Journal of Production Research, vol. 38, no. 7, pp.

1641-1655, 2000.

[29] K.R. Baker, “Sequencing rules and due-date assignments in a job shop,” Management Science, vol. 30, no. 9, pp. 1093-1104,

September 1984.

[30] T. C. E. Cheng, “Optimal due-date assignment in a job shop” International Journal of Production Research, vol. 24, no. 3, pp.

503-5015, May 1986.

[31] T. C. E. Cheng, “Simulation study of job shop scheduling with due dates” International journal of systems science, vol. 19, no. 3,

pp. 383-390, January 1988.

[32] H. Luss and M. B. Rosenwein, “A due date assignment algorithm for multiproduct manufacturing facilities,” European Journal of

Operational Research, vol. 65, no. 2, pp. 187-198, March 1993.

[33] S. R. Lawrance, “Negotiating due-dates between customer and producers,” International Journal of Production Economics, vol.

37, no. 1, pp. 127-138, November 1994.

[34] T. Yang, Z. He, and K. K. Cho, “An effective heuristic method for generalized job shop scheduling with due dates,” Computers

and Industrial Engineering, vol. 26, no. 4, pp. 647-660, October

1994. [35] H. G. Kahlbacher and T. C. E. Cheng, “Processing-plus-wait due

dates in single-machine scheduling,” Journal of Optimization

Theory and Applications, vol. 85, no. 1, pp. 163-186, April 1995. [36] X. Qi, G. Yu, and J. F. Bard, “Single machine scheduling with

assignable due dates,” Discrete Applied Mathematics, vol. 122, no.

1-3, pp. 211-233, October 2002.



http://www.tandfonline.com/loi/tprs20?open=28#vol_28

[37] V. Lauff and F. Werner, “Scheduling with common due date,

earliness and tardiness penalties for multimachine problems: A

survey,” Mathematical and Computer Modelling, vol. 40, no. 5-6,

pp. 637-655, September 2004. [38] T. C. E. Cheng, L. Y. Kang, and C. T. Ng, “Single machine due-

date scheduling of jobs with decreasing start-time dependent

processing times,” International Transactions in Operational Research, vol. 12, no. 3, pp. 355-366, May 2005.

[39] J. B. Wang, “Single machine scheduling with common due date

and controllable processing times,” Applied Mathematics and Computation, vol. 174, no. 2, pp. 1245-1254, March 2006.

[40] Y. Xia, B. Chen, and J. Yue, “Job sequencing and due date

assignment in a single machine shop with uncertain processing times,” European Journal of Operational Research, vol. 184, no.

1, pp. 63-75, January 2008.

[41] D. Shabtay and G. Steiner, “Optimal due date assignment in multi-machine scheduling environments,” Journal of Scheduling,

vol. 11, no. 3, pp. 217-228, June 2008.

[42] A. Baykasoğlu, M. Göçken, and Z. D. Unutmaz, “New approaches to due date assignment in job shops,” European

Journal of Operational Research, vol. 187, no. 1, pp. 31-45, May

2008. [43] V. S. Gordon and V. A. Strusevich, “Single machine scheduling

and due date assignment with positionally dependent processing

times,” European Journal of Operational Research, vol. 198, no. 1, pp. 57-62, October 2009.

[44] N. H. Tuong and A. Soukhal, “Due dates assignment and JIT

scheduling with equal-size jobs,” European Journal of Operational Research, vol. 205, no. 2, pp. 280-289, September

2010.

[45] V. Vinoda and R. Sridharan, “Simulation modeling and analysis of due-date assignment methods and scheduling decision rules in

a dynamic job shop production system,” International Journal of

Production Economics, vol. 129, no. 1, pp. 127-146, January 2011. [46] Y. Yinab, W H Wu, T. C. E. Cheng, and C. C. Wu, “Due-date

assignment and single-machine scheduling with generalised

position-dependent deteriorating jobs and deteriorating multi-

maintenance activities” International Journal of Production Research vol. 52, no. 8, pp. 2311-2326, 2014.

[47] Y. Y. Lu, G. Li, Y. B. Wu, and P. Ji, “Optimal due-date

assignment problem with learning effect and resource-dependent processing times,” Optimization Letters, vol. 8, no. 1, pp. 113-127,

January 2014. [48] O. Hazır and S. Kedad-Sidhoum, “Batch sizing and just-in-time

scheduling with common due date,” Annals of Operations

Research, vol. 213, no. 1, pp. 187-202, February 2014.

[49] Y. Yin, T. C. E. Cheng, C. C. Wu, and S. R. Cheng, “Single-machine batch delivery scheduling and common due-date

assignment with a rate-modifying activity,” International Journal

of Production Research, February 2014.

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



http://www.tandfonline.com/loi/tprs20?open=52#vol_52

http://www.tandfonline.com/toc/tprs20/52/8

http://link.springer.com/journal/10479

http://link.springer.com/journal/10479

process planning and scheduling with slk due- date assignment … · 2014. 12. 16. · due-date...

Documents