an elaborate analysis of pr

An elaborate analysis of production systems inindustry what a consultant should know

Kostas S MetaxiotisElectrical amp Computer Engineer National Technical University of AthensInstitute of Communications amp Computer Systems Athens GreeceKostas ErgazakisElectrical amp Computer Engineer National Technical University of AthensInstitute of Communications amp Computer Systems Athens GreeceJohn E PsarrasNational Technical University of Athens Institute of Communications ampComputer Systems Athens Greece

Introduction

In recent years industrial markets have

undergone important changes which have

transformed the way in which

manufacturers must act and perform so as to

maintain competitiveness changes in

management process technology customer

expectations supplier attitudes competitive

behavior and many other aspects As a

consequence of these changes quality

reliability and speed with minimum costs

have become essential requirements to

compete with success

According to Spina et al (1996) ` many

industrial markets have turned into virtually

world-wide battlefields in which customers

are demanding ever wider ranges of

relatively low cost reliable and high quality

products and ever shorter and reliable

delivery timesrsquorsquo The word ` changersquorsquo is

nowadays a permanent feature of the general

business environment and companies which

are able to adapt to the new environment are

likely to gain significant competitive

advantage Adapting to the new

environments has meant for many

companies looking for new ways of

organizing and managing their production

In literature many authors have stressed

the need for ` new wave manufacturing

strategiesrsquorsquo (eg Hayes and Pisano 1994

Leong et al 1990) to ensure manufacturing

flexibility Others have considered the ability

of manufacturing companies to adapt to their

changing environment as a key to long-term

success (Spina et al 1996 Meredith et al

1994 Hum and Sim 1996 Price et al 1994)

Others have underlined the fact that higher

levels of computerization may result in

stronger ability to produce wider ranges of

products and greater flexibility in switching

among different products (Richter 1996)

In any case it is easily understood that the

needs of a modern company are liable to

dynamic changes The globalisation of the

economy the increase of the manufacturing

competition and the bursting evolution of the

information technology force many

manufacturing companies to proceed to

reengineering and restructuring of their

production systems and strategies Starting

from these considerations this paper aims at

exploring and presenting on an empirical

basis the production systems that are met in

industry In particular the paper analyses in

depth the key features of the production

systems and environments creating in this

way a comprehensive and solid knowledge

base to be exploited by individual

consultants production experts

practitioners software houses and of course

companies in the way of manufacturing

restructuring and improvement

Talking about software houses we should

stress that many sophisticated and complex

manufacturing information systems (ERP

scheduling systems etc) have unfortunately

shown unsatisfactory performance due to

developersrsquo superficial knowledge of the key

features of the production systems which

has eventually led to unsuccessful modeling

of the manufacturing environments The

importance of the complete knowledge of

production systems and of their peculiarities

behind the implementation of manufacturing

software should not be underestimated

(Karwowski and Salvendi 1994) For

example US software houses have

experienced an estimated 50 to 75 per cent

failure rate in implementing advanced

manufacturing technologies mostly due to

T he cu r re n t is su e an d fu ll t ex t a rch iv e o f th is jou rna l is ava i la b le a t

httpwwwemerald-librarycomft

[ 185 ]

Industrial Management ampData Systems1014 [2001] 185plusmn193

MCB University Press[ISSN 0263-5577]

KeywordsManufacturing industryProduction systems Scheduling

AbstractIt is common knowledge thatduring the last decade markets

have become extremelycompetitive with product variety

increasing continuousl y andproduct life cycles shorteningMany manufacturing companies

which hitherto satisfied theircustomers while operatingspecific production systems were

recently obliged to reconsiderbecause of the potentialsuperiority of other

` manufacturing philosophies rsquorsquo Inthe literature we meet a great

variety of production systems andmanufacturing philosophies while on the other side inindustry we usually find different

combinations of ` primaryrsquorsquoproductions systems In thispaper we present the existing

` state-of-the-artrsquo rsquo theoretical andexperientia l knowledge about

productions systems as well asdescribe their basiccharacteristic s in a useful exact

and comprehensive way forpractitioners and software houseswho want to have a knowledge

base for further research andpractical implementatio n in thewider field of production

management planning andscheduling

neglect of incomplete knowledge and

experience of production systems (Saraph

and Sebastian 1992)

Industrial environmentsclassification

Before proceeding to an in-depth description

of the production systems met in industry it

would be advisable to study firstly how it is

possible to classify an industrial

environment comprising all its

characteristics In the literature the most

popular method is the four-field notation

(A | B | C | D) of Conway et al (1967) A is the

number of jobs that must be processed by the

machines B is the number of machines C is

the flow pattern within the machine shop and

D is the performance measure by which the

schedule of production is evaluated

Although this descriptive technique is

suitable for basic environments when we are

in front of non-basic environments (with

characteristics more common in practice

such as preemption dependent jobs etc) then

the three-field notation (not | shy | reg) of Graham

et al (1979) is more appropriate (Pinedo 1995)

The not field describes the machine

environment and contains a single entry The

shy field may contain no entries a single entry

or multiple entries These entries provide

details of processing characteristics and

constraints The reg field contains the objective

to be minimized and usually contains a single

entry (Brucker 1997)

Figure 1 presents the symbols of most

common entries of not field accompanied with

a brief explanation We must note that The number of jobs to be processed and the

number of machines are assumed to be

finite The number of jobs is denoted by n

while the number of machines is denoted

by m We refer to a job with the subscript j In the literature we may find different

symbols for the same entries

In the shy field we specify the processing

restrictions and constraints which may

include multiple entries (Lawler et al 1993)

Some possible entries are Release dates (rj) If this entry appears in

shy field job j may not start its processing

before its release date rj Otherwise the

processing of job j may start at any time Sequence plusmn dependent setup times (sjk)

Machines often have to be reconfigured or

cleaned between jobs We state this

process by the term changeover or setup

If the length of the setup depends on the

job just completed and on the one about to

be started then the setup times are

sequence dependent

Preemptions (prmp) Preemptions imply

that is not necessary to keep a job on a

machine until completion It is allowed to

interrupt the processing of a job when a

high priority rush order arrives at the

machine When the processing already

done on the preempted job is not lost then

the preemption is referred as preemptive

resume If it is lost then we have

preemptive repeat Precedence constraints (prec) In a machine

environment it is possible that the

processing of a job can start only after the

completion of a given set of others jobs

Such constraints are referred to as

precedence constraints and can be

described by a precedence constraints

graph Blocking (block) Blocking is a

phenomenon that may occur in different

types of production systems Buffer in

between two successive machines may be

limited If this buffer is full the upstream

machine is not allowed to release a

completed job Breakdowns (brkdwn) Machines

breakdowns imply that machines are not

continuously available Permutation (prmu) In the flow shop

environment the queues in front of each

machine may operate according to the

FIFO discipline This implies that the

order in which the jobs go through the

first machine is maintained throughout

the system Recirculation (recrc) Recirculation may

occur in production systems of type job

shop when a job may visit a machine

more than once No wait (nwt) Jobs are not allowed to wait

between two successive machines In this

way the starting time of a job at the first

machine has to be delayed to be ensured

that the job can go through the shop

without having to wait for any machine Reentrance Jobs return to a workcenter

several times before completion This

practice is met very often in the

semiconductor industry (Graves et al

1983 Kubiak et al 1996) Machine eligibility constraints In a

parallel machine environment job j may

often not be processed on any of the

available machines but rather must be

processed on a machine belonging to a

specific subset Mj of the machines This

happens when the m machines in parallel

are not identical Tooling constraints Machines frequently

require one or more tools to process the

jobs they handle These tools may be of

[ 186]

Kostas S MetaxiotisKostas Ergazakis andJohn E PsarrasAn elaborate analysis ofproduction systems inindustry what a consultantshould know


various types some with only limited

availability Storage-Space constraints In many

production systems especially those that

produce bulky items the amount of space

available for WIP storage is limited This

constraint puts an upper bound on the

number of jobs waiting for a machine

(Pinedo and Chao 1999) Material-Handling constraints Modern

assembly systems (eg automobile

assembly facility) often have material-

handling systems that convey the jobs

from one workcenter to another Such

systems enforce strong dependencies

between starting times of operations and

the completion times of their

predecessors

Finally the reg field usually contains a single

entry that provides information about the

objective to be minimized In the following

Table I we present the most common entries

We must note that the time job j exits the

system is denoted by Cj The lateness of job j

is defined as

Lj ˆ Cjdj

where dj represents the committed shipping

or completion date of job j The tardiness of

job j is defined as

Tj ˆ maxhellipCjdj 0dagger ˆ maxhellipLj 0dagger

Description of production systems

Before analyzing the production systems

widely met in industry we need to identify

the various types of production activities

that may be encountered It has been found

convenient to identify three main types

(Baker 1974)

1 Continuous production where the demand

for a product requires production on a

continuous basis

2 Batch production where the rate of

demand for a product is well below the

rate at which it can be produced so that

production is carried out intermittently to

avoid excessive stockpiling

3 Job production where various jobs each

with its own array of processing

requirements need to be loaded in some

sequence on a given set of production

facilities

Figure 1Possible entries for a field

[ 187 ]



In modern industries there are many

different combinations of machine

configurations and consequently of

productions systems The most important

and mainly met in industry are

Flow shopIn many manufacturing or assembly

environments jobs have to undergo multiple

operations on a number of different

machines All jobs have the same routing so

they have to be processed first on machine 1

then on machine 2 and so on The machines

are set up in a series and whenever a job

completes its processing on one machine it

joins the queue at the next The sequence of

the jobs may vary from machine to machine

since jobs may be resequenced between

machines However the same job sequence is

maintained throughout the system if a

material handling system transports the jobs

from one machine to the next

In the general flow shop scheduling

problem we are given a set of m machines

M1 Mm and a set of n jobs J1 Jn

Each of the n jobs has to be processed on the

m machines M1 Mmin that order A job Jj

j = 1 n consists of a sequence of m

operations O1j Omj where Oij must be

processed on machine Mi for a given

uninterrupted processing time pij Each

machine Mi i = 1 m can process at most

one job at a time and each job Jj j = 1 n

can be processed by at most at one machine

at a time Let Cij be the completion time of

operation Oij The objective is to produce a

schedule that minimizes an objective

function

The problem above may be different if we

consider some basic variations of the flow

shop model in some flow shops if a job does

not need processing at a particular machine

it may bypass that machine and go ahead of

the jobs being processed or waiting for

processing there These systems are known

as non-permutation flow shops Other flow

shops allow that bypass Then we say that

they operate under the first in first out

(FIFO) discipline and the system is referred

to as a permutation flow shop

A generalization of the flow shop is the

flexible flow shop (or compoundhybrid flow

shop) which consists of a number of stages in

series with a number of machines in parallel

at each stage Jobs are processed at each

stage on any one of the parallel machines as

presented in Figure 2 The queues between

the various stages usually operate under the

FIFO discipline Applications of this system

can be found to cosmetics textile and food

industries

Many production systems may belong to

the batchflow shop category Here the

production process is divided in two parts

The first part is a batch shop where the

processing of raw materials takes place The

second part is a flexible flow shop system A

Table IPossible entries for reg field

Entries Brief explanation

Makespan (Cmax) The makespan is defined as the time the last job leaves thesystem It is defined as max(C1 Cn) A minimum makespanusually implies a high utilization of the machines

Maximum lateness (Lmax) It is defined as max(L1 Ln) It is a measure of the worstviolation of the due dates

Total weighted completion time (sect wjCj) The sum of the weighted completion times of n jobs gives us anindication of the total holding or inventory costs

Discounted total weighted completion time(sect wj(1 plusmn eplusmnrCj))

This is a more general cost function than the previous one wherecosts are discounted at a rate of r 0 lt r lt 1 per unit time

Total weighted tardiness (sect wjTj) This is also a more general cost function than the total weightedcompletion time

Work-in-process inventory costs WIP ties up capital and large amount of WIP can clog upoperations WIP increases handling and inventory costs

Setup costs It often pays to minimize the setup times when the throughput ratehas to be maximized

Figure 2Flexible flow shop

[ 188]



food company for example at a first stage

processes the products in large boilers

connected with pipes This system is not

usually totally linear The second half

comprises bottling canning and packing of

products in parallel lines

Finally we have a model known as

reentrant flow shop We mainly meet this

type of system in semiconductor wafer

fabrication facilities (Demirkol and Uzsoy

2000) The shop consists of m machines

organized as a flow shop Jobs move through

the shop as through a flow shop but may skip

certain machines Once the job has

completed a pass through the shop it may

reenter the shop for another set of

operations not necessarily in the same

machines it visited the previous time Jobs

incur sequence dependent setup times at

each machine Usually the objective in this

type of systems is to minimize the maximum

lateness over all jobs A number of authors

have studied scheduling reentrant flow

shops Graves et al 1983 solved the problem

of minimization of the average throughput

time subject to meeting a given production

rate Kubiak et al examined (1996) the

scheduling of reentrant shops to minimize

total completion time

Job shopIn multioperation shops jobs often have

different routes This environment is

referred to as a job shop which is a

generalization of a flow shop (A flow shop is

a job shop in which each and every job has

the same route)

In the job shop problem we are given n

jobs i = 1 n and m machines M1 Mm

Job i consists of a sequence of ni operations

Oi1 Oi2 Oin

which must be processed in this order ie we

have precedence constraints of the form Oij

Oij+1 (j = 1 ni plusmn 1) There is a machine

middotij M1 Mm and a processing time pij

associated with each operation Oij Oij must

be processed for pij time units on machine middotij

The problem is to find a feasible schedule

which minimizes some objective function

depending on the finishing times Ci of the last

operations of the Oinijobs If not stated

differently we assume that middotij 6ˆ middotij+1 for i =

1 ni 1 In case all jobs to be scheduled are

available at the beginning of the scheduling

process the problem is called static if the set

of jobs to be processed is continuously

changing over time the problem is called

dynamic In a deterministic problem all

parameters are known with certainty If at

least one parameter is probabilistic (for

instance the release times of the jobs) the

problem is called stochastic

The simplest job shop models assume that

a job may be processed on a particular

machine at most once on its route through

the system This system is known as classic

job shop Figure 3 represents the structure of

a classic job shop

A generalization of the classic job shop is

the flexible job shop with workcenters that

have multiple machines in parallel When a

job on its route arrives at the workcenter it

may be processed on anyone of the available

machines This environment is very common

in semiconductor industry

The simplest job shop problems assume

that a job may be processed on a particular


the system In others a job may visit a given

machine several times on its route through

the shop These shops are said to be subject to

recirculation which increases in a

significant degree the complexity of the

model The most complex machine

environment from a combinatorial point of

view is the flexible job shop with

recirculation

Flexible assembly systemsHere we have a limited number of different

product types and a given quantity of each

product type must be produced by the

system It is obvious that two units of the

same product type are identical A material

handling system is responsible for the

movement of jobs in a flexible assembly

system It imposes constraints on the starting

times of the jobs on the various machines

The completion time of a job on a machine

determines its starting time on the next

machine on its route On the other side the

material handling system limits the number

of jobs in the buffers between the machines

There exist three types of flexible assembly

systems In unpaced assembly systems we

have a flow line with a number of machines

in series Any job can spend a much time is

needed on any machine Blocking may be

caused on account of limited buffers between

successive machines The goal is the

production of different product types in given

Figure 3Classic job shop

[ 189 ]



quantities and the maximization of

throughput We meet this environment for

example in an assembly of copiers where

copiers with different characteristics are

assembled on the same line

In paced assembly systems the units that

have to be assembled are moved from one

workstation to the next by a conveyor

system at a fixed speed As before each

workstation has its own capacity and

constraints The goal is to prevent

workstations from being overloaded and to

minimize setup costs These systems are very

common in the automotive industry where

we have to assemble cars of different types on

one line

Finally there is a system in which there

are a number of machines in parallel at each

workcenter This system is known as flexible

flow system with buffers and bypass The

process of a job may take place on any of the

parallel machines while a workcenter may

be bypassed Manufactures of printed circuit

boards use this type of system The goal is the

maximization of throughput too

Open shopIn this system each job has to be processed

on each of the m machines However some of

these processing times may be zero The

route of each job through the machine

environment may be different

Batch shopIn a production system of this type the

production of identical finished or

unfinished products is massive and it is

preferable to have a batch processing in

order to achieve large economies of scale

Flow of jobs in these systems is not totally

linear but it is less complicated than in open

shops An example of a batch shop system is a

garment industry

Multiprocessor task systemsIn these systems tasks require processing by

one or more machines at a time We have m

different machines M1 Mmand n tasks i =

1 n Each task i requires a specific

processing time pi by all the machines

belonging to a given subset Misup3 M1 Mm

Some tasks that require the same machine

cannot be processed at the same time In this

case these tasks are called incompatible

Otherwise they are called compatible

Multipurpose machines shopIn this case we have a number of

multipurpose machines capable of

processing different jobs These machines

may be equipped with different tools A

machine can process a job only if it is

equipped with the appropriate tool The

multipurpose machine problem is described

as follows (Brucker 1997) we have n jobs

J1 Jn and each job Ji consists of a set of

operations Oi1 Oini In addition we have

m multipurpose machines Oi1 Oini

equipped with different tools An operation

Oij (i = 1 n j = 1 ni) must be processed

by a specific tool for pij time units and

consequently by a specific machine equipped

with this tool In this way there is a set Mij sup3M1 Mm which is related with each

operation Oij We have the following

restrictions A machine cannot process more than one

operation at the same time An operation cannot be processed by more

than one machine at the same time

In Figure 4 we can see an example of a

scheduling problem with multiprocessor

machines with two jobs three operations

and three machines Operations O11 O12 and

O21 must be processed by the tool amp and 5respectively Operation O11 can be processed

by machine M1 or M2 O12 by M2 or M3 and O21

by M3 too

Lot sizingIn some systems (eg in flexible assembly

systems) we may have a schedule that

alternates frequently between different job

types because of the fact that setup times and

costs are not important (Pinedo and Chao

1999) In these cases it is preferable to have

an alternating schedule because it is usually

more efficient that one with long runs of

identical jobs In other systems setup times

and setup costs may be significant If the

processing of a job in a machine requires a

major setup then it may be beneficial to let

this job be followed by similar jobs This

uninterrupted processing of a series of

identical items is called a run The run

lengths are referred as lot sizes The form of

production known as continuous

manufacturing has the main characteristic

that we have processing of identical items in

long runs This situation inevitably involves

inventory holding costs The scheduling

Figure 4Scheduling problem with multiprocessormachines

[ 190]



problem consists in the determination of lot

sizes and their sequencing in a way that

minimizes the setup costs and the setup

times This problem is known as economic lot

scheduling problem (ELSP) and in practice

it has many applications in case that

inventory holding costs are substantial such

as in the pharmaceutical paper chemical

aluminum and steel industries

Just-in-timeIn recent years many manufacturing

companies have been challenged to increase

their focus on customer satisfaction and

quality of products Confronting the challenges

of global competition companies world-wide

are forced to find ways to reduce costs

improve quality and meet the ever-changing

needs of their customers One successful

solution has been the adoption of just-in-time

(JIT) manufacturing systems which involve

many functional areas of a company such as

manufacturing engineering marketing and

purchasing JIT was developed in Japan in the

1950rsquos and it achieved considerable success in

Toyota The basis for JIT was the production

system of Toyota after the Second World War

Until the early 1980rsquos the thrust of much of the

analysis of Japanese production systems had

focused on cultural differences and concluded

that there was a particular Japanese ` mindsetrsquorsquo

that facilitated their success Schonberger

championed the notion (1982) that these

systems were based on a set of procedures and

techniques that could be implemented

independent of any particular cultural

conditions He provided the following

definition of the just-in-time manufacturing

systemThe JIT idea is simple produce and deliver

finished goods just-in-time to be sold sub

assemblies just-in-time to be assembled into

finished goods fabricated parts just-in-time to

go into sub-assemblies and purchased

materials just-in-time to be transformed into

fabricated partsrsquorsquo (Schonberger 1982)

The ultimate goal of JIT is to eliminate all

forms of waste This goal is approached by

testing each step in a process to determine if

it adds value to the product If the step does

not add value then it is examined closely to

determine possible alternatives In this way

each process gradually improves In general

companies try to realize the following

benefits (Chase et al 1998) lower raw material work in process and

finished goods inventories higher levels of product quality increased flexibility and ability to meet

customer demands lower overall manufacturing costs and increased employee involvement

It is easily understood that the philosophy of

JIT can bring impressive advances in

productivity and quality in manufacturing

industries Being a strategic weapon for

process improvement JIT has been subjected

to numerous studies in the literature The

conclusion is that JIT perspectives are

excellent although there are areas of greatest

potential for improving performance such as

technology training of workers quality and

so on

In addition we should mention also the

Group Technology (GT) philosophy of

cellular manufacturing systems Group

Technology has emerged as a significant

philosophy in improving the productivity of

production systems (Onwubolu 1998) This

philosophy offers a systems approach to the

reorganization of the traditional complex job

shop and flow shop production systems into

cellular or flexible manufacturing systems

The main objective of this philosophy is to

achieve the following benefits for production

systems (Ang 2000) simplification of flow of parts and tools reduction of set-up times reduction of average material handling

time lowering work-in-process and reduction of throughput time

In this category of modern manufacturing

improvement philosophies we could include

also total quality management (TQM)

business process re-engineering (BPR) and

time-based competition (TBC) which use the

principles of the cellular manufacturing

systems

Finally we must note that in literature

some other production systems such as

single machine shop or parallel machine

shop are studied (Moore 1968 Morton and

Pentico 1997 Brucker 1997 Blazewicz et al

1991) In practice in industry we can find

some combinations of these ` primaryrsquorsquo

production systems However their

theoretical study is important in order to

acquire a better insight on real systems

Conclusions

This paper recognizes the problem of the

lack of a comprehensive ` knowledge basersquorsquo

for production systems in industry although

the academic contribution to this area is

abundant The paper presents an elaborate

analysis of the production systems which are

met in industry giving the opportunity to

practitioners managers consultants and

software houses to acquire very easily

specific knowledge that they could ignore

This knowledge has been proved to be

[ 191 ]



crucial in many research and practical

activities in the wider field of production

management

The analysis described in this paper

creates a strong theoretical and empirical

basis on which further research and

practical applications can rely Production

scheduling problems are one of the main

fields on which future research can andor

should be focused due to the big number of

its peculiarities restrictions or constraints

On the other hand ERP systems have already

become the new fashion Although many

projects in the past have failed because of

loss of focus lack of expertise lack of funding

and minimum business participation ERP

vendors try to expand their systems with new

capabilities (eg scheduling) We should keep

always in mind that all the well-known

commercial packages dealing with the

enterprise resources planning such as SAP

BAAN Oracle PeopleSoft do not support

production scheduling In any case

implementing ERP is only part of the

solution to creating a seamless operational

environment The complete solution involves

making a business commitment

implementing truly global systems (Gupta

2000)

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exceptional elements in cellular

manufacturing systemsrsquorsquo Industrial

Management amp Data Systems Vol 100 No 6

pp 251-54

Baker KR (1974) Introduction to Sequencing

and Scheduling John Wiley New York

NY

Blazewicz J Dror Mm and Weglarz J (1991)

` Mathematical programming formulations

for machine scheduling a surveyrsquorsquo European

Journal Operational Research Vol 51 No 3

pp 283-300

Brucker P (1997) Scheduling Algorithms

Springer Osnabruck

Chase R and Aquilano N and Jacobs F (1998)

Production and Operations Management

Manufacturing and Services McGraw-Hill

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Conway RW Maxwell WL and Miller LW

(1967) Theory of Scheduling Addison-Wesley

Reading MA

Demirkol E and Uzsoy R (2000) ` Decomposition

methods for reentrant flow shops with

sequence-dependent setup timesrsquorsquo Journal of

Scheduling Vol 3 No 3 pp 155-77

Graham RL Lawler EL Lenstra JK and

Rinnooy Kan AHG (1979) ` Optimisation

and approximation in deterministic

sequencing and scheduling a surveyrsquorsquo

Annals of Discrete Mathematics Vol 5

pp 287-326

Graves SC Meal HC Stefek D Zeeghmi AH

(1983) ` Scheduling of re-entrant flow shopsrsquorsquo

Journal of Operations Management Vol 3

pp 197-207

Gupta A (2000) ` Enterprise resource planning

the emerging organizational value systemsrsquorsquo

Industrial Management amp Data Systems

Vol 100 No 3 pp 114-18

Hayes RH and Pisano GP (1994) ` Beyond

world-class the new manufacturing

strategyrsquorsquo Harvard Business Review January-

February

Hum S-H and Sim H-H (1996) ` Time based

competition literature review and

implications for modelingrsquorsquo International

Journal of Operations amp Production

Management Vol 16 No 1 pp 75-90

Karwowski W and Salvendi G (1994)

` Integrating people organization and

technology in advanced manufacturing a

position paper based on the joint view of

industrial managers engineers consultants

and researchersrsquorsquo International Journal of

Human Factors in Manufacturing Vol 4

No 1 pp 1-19

Kubiak W Lou SX and Wang YM (1996)

` Mean flow time minimizations in re-entrant

job shops with hubrsquorsquo Operations Research

Vol 44 pp 764-76

Lawler EL Lenstra JK Rinnooy Kan AHG

and Shmoys DB (1993) ` Sequencing and

scheduling algorithms and complexityrsquorsquo

Handbook in Operations Research and

Management Science 4 Logistics of Production

and Inventory

Leong GK Snyder DL and Ward PT (1990)

` Research in the process and content of

manufacturing strategyrsquorsquo Omega Vol 18

No 2

Meredith JR McCutcheon DM and Hartley J

(1994) ` Enhancing competitiveness through

the new market value equationrsquorsquo

International Journal of Operations amp

Production Management Vol 14 No 11

pp 7-22

Moore JM (1968) ` An n job one machine

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number of late jobsrsquorsquo Management Science

Vol 15 pp 102-109

Morton T and Pentico D (1997) Heuristic

Scheduling Systems With Applications to

Production Systems and Project Management

J Wiley amp Sons New York NY

Onwubolu G (1998) ` Redesigning job-shops to

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Manufacturing Systems Vol 9 No 6

pp 377-83

Pinedo M (1995) Scheduling Theory Algorithms

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Pinedo M and Chao X (1999) Operations

Scheduling with Applications in


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process for developing a methodology for

[ 192]



enhancing the flexibility of manufacturing

information systemsrsquorsquo in Platts KW

Gregory MJ and Neely A (Eds) Operations

Strategy and Performance University of

Cambridge Cambridge pp 107-12

Richter A (1996) ` Integration builds factory of

the futurersquorsquo Electronic Business Today Vol 22

No 3

Saraph JV and Sebastian RJ (1992) ` Human

resource strategies for effective introduction

of advanced manufacturing technologies

(AMT)rsquorsquo Production and Inventory

Management Journal pp 64-70

Schonberger RJ (1982) Japanese Manufacturing

Techniques Nine Hidden Lessons in

Simplicity The Free Press New York

NY

Spina G Bartezzaghi E Bert A Cagliano R

Draaijer D and Boer H (1996)

` Strategically flexible production the

multi-focused manufacturing paradigmrsquorsquo



pp 20-41

Further readingArtiba A and SE Elmaghraby (1997) The

Planning and Scheduling of Production

Systems Chapman amp Hall London

Bedworth D and Bailey J (1987) Integrated

Production Control Systems Management

Analysis Design Wiley amp Sons New York

NY

Buffa ES (1976) Operations Management The

Management of Production Systems Wiley

New York NY

Kussiak A (1990) Intelligent Manufacturing

Systems Prentice-Hall Englewood Cliffs NJ

Lozinsky S and Wahl P (1998) Enterprise-Wide

Software Solutions Integration Strategies and

Practices Addison-Wesley Publication

Readubgm NA

Martinich J (1997) Production and Operations

Management J Wiley amp Sons New York NY

Shtub A (1999) Enterprise Resource Planning

(ERP) The Dynamics of Operations

Management Kluwer Academic Publishers

Dordrecht

[ 193 ]



neglect of incomplete knowledge and

experience of production systems (Saraph

and Sebastian 1992)

Industrial environmentsclassification

Before proceeding to an in-depth description

of the production systems met in industry it

would be advisable to study firstly how it is

possible to classify an industrial

environment comprising all its

characteristics In the literature the most

popular method is the four-field notation

(A | B | C | D) of Conway et al (1967) A is the

number of jobs that must be processed by the

machines B is the number of machines C is

the flow pattern within the machine shop and

D is the performance measure by which the

schedule of production is evaluated

Although this descriptive technique is

suitable for basic environments when we are

in front of non-basic environments (with

characteristics more common in practice

such as preemption dependent jobs etc) then

the three-field notation (not | shy | reg) of Graham

et al (1979) is more appropriate (Pinedo 1995)

The not field describes the machine

environment and contains a single entry The

shy field may contain no entries a single entry

or multiple entries These entries provide

details of processing characteristics and

constraints The reg field contains the objective

to be minimized and usually contains a single

entry (Brucker 1997)

Figure 1 presents the symbols of most

common entries of not field accompanied with

a brief explanation We must note that The number of jobs to be processed and the

number of machines are assumed to be

finite The number of jobs is denoted by n

while the number of machines is denoted

by m We refer to a job with the subscript j In the literature we may find different

symbols for the same entries

In the shy field we specify the processing

restrictions and constraints which may

include multiple entries (Lawler et al 1993)

Some possible entries are Release dates (rj) If this entry appears in

shy field job j may not start its processing

before its release date rj Otherwise the

processing of job j may start at any time Sequence plusmn dependent setup times (sjk)

Machines often have to be reconfigured or

cleaned between jobs We state this

process by the term changeover or setup

If the length of the setup depends on the

job just completed and on the one about to

be started then the setup times are

sequence dependent

Preemptions (prmp) Preemptions imply

that is not necessary to keep a job on a

machine until completion It is allowed to

interrupt the processing of a job when a

high priority rush order arrives at the

machine When the processing already

done on the preempted job is not lost then

the preemption is referred as preemptive

resume If it is lost then we have

preemptive repeat Precedence constraints (prec) In a machine

environment it is possible that the

processing of a job can start only after the

completion of a given set of others jobs

Such constraints are referred to as

precedence constraints and can be

described by a precedence constraints

graph Blocking (block) Blocking is a

phenomenon that may occur in different

types of production systems Buffer in

between two successive machines may be

limited If this buffer is full the upstream

machine is not allowed to release a

completed job Breakdowns (brkdwn) Machines

breakdowns imply that machines are not

continuously available Permutation (prmu) In the flow shop

environment the queues in front of each

machine may operate according to the

FIFO discipline This implies that the

order in which the jobs go through the

first machine is maintained throughout

the system Recirculation (recrc) Recirculation may

occur in production systems of type job

shop when a job may visit a machine

more than once No wait (nwt) Jobs are not allowed to wait

between two successive machines In this

way the starting time of a job at the first

machine has to be delayed to be ensured

that the job can go through the shop

without having to wait for any machine Reentrance Jobs return to a workcenter

several times before completion This

practice is met very often in the

semiconductor industry (Graves et al

1983 Kubiak et al 1996) Machine eligibility constraints In a

parallel machine environment job j may

often not be processed on any of the

available machines but rather must be

processed on a machine belonging to a

specific subset Mj of the machines This

happens when the m machines in parallel

are not identical Tooling constraints Machines frequently

require one or more tools to process the

jobs they handle These tools may be of

[ 186]


















predecessors







is defined as

Lj ˆ Cjdj



job j is defined as








(Baker 1974)



continuous basis










facilities


[ 187 ]






































function























industries


















[ 188]





































the same route)




Oi1 Oi2 Oin



























a classic job shop



















recirculation
























[ 189 ]


















one line























garment industry





































by M3 too























[ 190]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]


















predecessors







is defined as

Lj ˆ Cjdj



job j is defined as








(Baker 1974)



continuous basis










facilities


[ 187 ]






































function























industries


















[ 188]





































the same route)




Oi1 Oi2 Oin



























a classic job shop



















recirculation
























[ 189 ]


















one line























garment industry





































by M3 too























[ 190]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]






































function























industries


















[ 188]





































the same route)




Oi1 Oi2 Oin



























a classic job shop



















recirculation
























[ 189 ]


















one line























garment industry





































by M3 too























[ 190]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]





































the same route)




Oi1 Oi2 Oin



























a classic job shop



















recirculation
























[ 189 ]


















one line























garment industry





































by M3 too























[ 190]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]


















one line























garment industry





































by M3 too























[ 190]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]



































































so on





















systems











Conclusions












[ 191 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]





management


























2000)





pp 251-54



NY





pp 283-300


Springer Osnabruck




New York NY



Reading MA










pp 287-326




pp 197-207




Vol 100 No 3 pp 114-18




February













No 1 pp 1-19




Vol 44 pp 764-76






and Inventory




No 2






pp 7-22




Vol 15 pp 102-109








pp 377-83



Cliffs NJ






[ 192]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]










No 3









NY







pp 20-41







NY



New York NY






Readubgm NA






Dordrecht

[ 193 ]



an elaborate analysis of pr

Documents