scheduling and control of fms

8/7/2019 Scheduling and Control of Fms

1/22

Scheduling and Control of Flexible

Manufacturing Systems

Prepared by:Bopanna.K.D

Under the guidance ofDr. G.ThangamaniProfessor

Department Of Mechanical Engineering,Sir M.Visvesvaraya Institute Of Technology, Bangalore


2/22

INTRODUCTION

Flexible manufacturing systems (FMS) are

distinguished by the use of computer control

in place of the hard automation usually found

in transfer lines

FMS provide significant advantages, including

reduced work-in-process inventory, reduced

throughput time, improved quality, and

increased machine utilization


3/22


4/22

ONEOF THE MOST DIFFICULT PROBLEMS

ARISING IN FLEXIBLE MANUFACTURING

SYSTEMS IS SCHEDULING PROBLEM.

It May Be : Fabrication

Assembly

Machining


5/22

FMS PERFORMANCE STRONGLY

DEPENDS ON THE SCHEDULING

STRATEGIES USED.

FMS scheduling problems are known to be hard

and generally involve a large number of machines

and part types.

In addition, searching for an optimal schedule in a

dynamic system, such as an FMS, may not be

practical since it is too time-consuming to provide a

quick response to real-time events.


6/22

OVERALL FMS PROBLEM ARE

STRUCTURED AS :

Aggregate Scheduling Problem

Upper Level Problem

Real Time Scheduling

Lower Level Problem


7/22

OVERALL SCHEDULINGOBJECTIVES

OF THE FMS

meet due dates,

minimize work-in-process (WIP) inventory,

minimize the average flow time of orders throughthe system,

achieve high machine and worker time utilization,etc.

balance the assigned machine processing times. balance the workload per machine for a system of

groups of pooled machines of equal sizes.


8/22

DIFFERENT VIEWPOINTS OF

OPERATIONAL PROBLEMS

Methodology used in resolving the problem

Applications viewpoint

Time horizon considered

FMS factors considered


9/22

Methodology

MATHEMATICAL PROGRAMMING

APPROACH

To manage the complexity of the problem, the FMS

operation problem have divided into two sub problems:

preproduction setup

production operation.


10/22

AFTER THIS PREPARATORY PLANNING

PHASE,THE REMAINING PROBLEMS ARE

CALLED OPERATIONAL PROBLEMS

1) Part type selection problem.

2) Machine grouping problem.

3) Production ratio problem.

4) Resource allocation problem.

5) Loading problem.


11/22

MULTIPLE-CRITERIA DECISION

MAKINGAPPROACH

meeting production requirements,

balancing of machine utilization,

minimization of throughput time of parts.


12/22

HEURISTICS ORIENTED APPROACH

In the scheduling context, they report on three part

sequencing situations:

Initial entry of parts into an empty system,

General entry of parts into a loaded system,

Allocation of parts to machines within the system

(dispatching rules)


13/22

CONTROL THEORETIC APPROACH

A closed loop formulation of the FMS scheduling problem

The closed loop control policy is tailored for a dedicated FMS producing aparticular part mix.

The tooling of the FMS, buffer capacity and other constraints are notconsidered. It is assumed that the input of a part is a sufficient controldecision, and the (alternate) routing, possible deadlocks, blocking, etc. neednot be considered.

Further, the possible effect of long total processing times of parts in theFMS on the feedback loop is ignored


14/22

SIMULATIONBASED APPROACH

Recently some have presented discrete eventsimulation as a scheduling tool.

Basically, simulation is proposed as a tool to evaluatethe dispatching rules.

The simulation model is initialized to the exact

current state of the factory. The dispatching rules arethen tested on this model.


15/22

ARTIFICIAL INTELLIGENCEBASED

APPROACH

Artificial intelligence (ai) appears to be particularly suited to solving

operation problems ofFMS -problems involvingA large search

space, and where human expertise can find reasonable solutions

pretty fast. Many researchers have sought to utilize this similarity.

So far, two techniques of ai have found use in the fms literature:

Expert systems and planning.

A nonlinear planning algorithm


16/22

HIERARCHICAL APPROACH

Since an FMS has to achieve multiple performanceobjectives, a monolithic scheduling algorithm wouldbe complex even if it is capable of addressing all

objectives. It is achieved by the controllers at different levels of

the hierarchical architecture, namely the shop level, and

the cell level. The shop level controller employs a combined

priority rule to rank shop orders considering multiplescheduling objectives.


17/22

FMS SCHEDULING CONSIDERATIONS

SINGLE RULEVERSUS MIXED RULES

Since no single rule consistently outperforms all otherrules, they use the mixed dispatching rule (MDR)

approach in FMS scheduling by mixing four rules:next-in next out, shortest processing time (SPT),largest slack first, and first-in first-out

Due to the interchangeability of machines, a part can

have several alternative operations. To avoid longqueues, an alternative operation may be used, oftenperformed by machines of lesser capabilities


18/22

SCHEDULINGWITH MULTIPLETASKS AND

ALTERNATIVEOPERATIONS

Fixed priorities (FP) heuristic:

The pair with the highest priority value will be processedfirst

Least reduction in entropy heuristic:First, the processing operation of a part with the minimumflexibility is chosen because it has the fewest choices onmachines

M

inimum flow resistance heuristic:Priority should be given to simple heuristics that are easyto understand and implement, such as FP (for which SPTis a special case).


19/22

ECONOMIC FACTORS IN FMS

SCHEDULING

First-come-first-serve (FCFS): This rule does not consider duedates.

Earliest due date (EDD): This rule assumes all orders have thesame tardiness cost.

Slack per Remaining Processing Time (S/RPT): This rule isderived from minimum slack(MSLACK) rule.

Weighted Shortest Processing Time (WSPT):As is well known,the SPT rule provides for single machine scheduling the minimummean flow time, which is equivalent to minimizing mean lateness

Weighted Cost OVER Time (COVERT): With a look-ahead

dynamic feature, it considers the expected waiting time for aremaining operation.

Apparent Tardiness Cost (ATC): Also a dynamic rule, theATCfollows an exponential function of slack that involves a look-aheadparameter measured in units of average


20/22

COMPARISONOF HEURISTICS

The combined index rule with consideration of

multiple parameters of system outperforms SPT and

EDD rule in terms of objective function values per

order. Here in we can observe that SPT as a priorityindex does reduce mean flow time of the system.

However, since the FMS is not a single-objective

manufacturing system, reducing mean flow time does

not necessarily benefit the entire system. The EDDsystem does not consider the efficiency of the system


21/22

CONCLUSIONS

FMS scheduling is a difficult problem to solve

optimally due to the complex nature of the system.

By using well-designed heuristics, it can be rendered

tractable. In facing the scheduling problem ofFMS,we should notice that FMS is multi-objective,

hierarchical manufacturing system with a number of

manufacturing cells. Thus, it is important to consider

these features when designing an FMS schedule


22/22

scheduling and control of fms

Documents