scheduling and control of fms
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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
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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
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ONEOF THE MOST DIFFICULT PROBLEMS
ARISING IN FLEXIBLE MANUFACTURING
SYSTEMS IS SCHEDULING PROBLEM.
It May Be : Fabrication
Assembly
Machining
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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.
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OVERALL FMS PROBLEM ARE
STRUCTURED AS :
Aggregate Scheduling Problem
Upper Level Problem
Real Time Scheduling
Lower Level Problem
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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.
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DIFFERENT VIEWPOINTS OF
OPERATIONAL PROBLEMS
Methodology used in resolving the problem
Applications viewpoint
Time horizon considered
FMS factors considered
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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.
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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.
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MULTIPLE-CRITERIA DECISION
MAKINGAPPROACH
meeting production requirements,
balancing of machine utilization,
minimization of throughput time of parts.
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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)
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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
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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.
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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
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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.
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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
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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).
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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
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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
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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
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