Rescheduling Manufacturing Systems: a framework of strategies, policies, and methods

1
Rescheduling Manufacturing Systems a framework
of strategies, policies, and methods

• By
• Guilherme E. Vieria Jeffrey W. Herrmann, Edward
  Lin
• Represented by
• Nuriye Kaptanlar

2
Introduction ...
Production Schedules ?state when certain
controllable activities should take place
?help in coordinating activities to
increase productivity
reduce operating costs !!!BUT not
ENOUGH. Rescheduling ? updating a production
schedule when it becomes INFEASIBLE.
3
Introduction ...

• Aim of the paper
• To standardize the terms
• To classify
• the strategies
• the policies framework
• the methods
• in rescheduling
  literature.
• To show how rescheduling affects the performance
  criteria.
• To bring practice and theory closer.

4
Introduction ...

• Objectives in scheduling
• Generate high quality schedules
• React quickly to disruptions
• Revise schedules in a cost effective way
• Notation in static scheduling
• ?/?/ ?
• ? scheduling environment
• ? characteristics of the jobs to be scheduled
• ? objective function
• Classifications DO NOT consider rescheduling
  context.

5
Introduction ...

• Types of studies in rescheduling literature
• for repairing a schedule that has been
  disrupted
• for creating a schedule that is robust
  with respect to disruptions (robustness will be
  considered later)
• how rescheduling policies affect the
  performance criteria.

6
Introduction ...

• Framework
• Rescheduling environment identifies
  the set of jobs that need to be scheduled
• Rescheduling strategy describes
  whether or not production schedules are generated
• Rescheduling policy specifies when
  rescheduling should occur

7
Introduction ...

• Paper Organization
• Rescheduling in general
• Scheduling terms, rescheduling framework
• Performance measures
• Rescheduling strategies policies
• Robust schedules updating them
• Effect of rescheduling policies on system
• Performance
• Gap between theory and practice

8
Rescheduling in general

• A production schedule CAN
• Identify resource conflicts
• Control the release of jobs to the shop
• Ensure that required raw material are ordered in
  time
• Determine whether delivery promises can be met
• Identify time periods available for prev. maint.

9
Rescheduling in general

• Disruptions
• Arrival of new jobs
• M/C failures
• M/C repairs
• Types of manufacturing systems considered
• Single M/C
• Parallel M/C
• Flow shop
• Job Shop
• Flexible Manufacturing cells and systems

10
Rescheduling in general

• Factors triggered actions
• M/C failure
• Urgent job arrival
• Job cancellation
• Due date change
• Delay in the arrival
• Shortage of materials
• Change in job priority
• Rework or quality problems
• Over or underestimation of
• process time
• Operator absenteeism

Overtime In-process subcontracting Process change
or re-routing M/C substitution Limited
manpower Setup times Equipment release
Cause
11
Terminology framework

• Definitions
• Manufacturing system organizes equipment, people
  and information to fabricate and assemble
  finished goods.
• Order release controls a manufacturing systems
  input by determining which orders should be moved
  into production.
• Shop floor control determines which operation
  each person and piece of equipment should do and
  when they should do it.

12
Terminology framework

• Definitions
• Production schedule specifies for each resource
  required for production, the planned start and
  end time of each job assigned to that resource.
• Scheduling process of creating a production
  schedule for a given set of jobs and resources.
• Rescheduling process of updating an existing
  production schedule in response to disruptions or
  other changes.

13
Terminology framework

• Definitions
• Rescheduling environment identifies the set of
  jobs that the schedule should include.
• Rescheduling strategy describes whether or not
  production schedules are generated.
• Rescheduling policy specifies when and how
  rescheduling is done.
• Rescheduling methods generate and update
  production schedules.

14
Terminology framework

• The focus of studies in Rescheduling
• on one or more well-defined scheduling problems
• Establishes the problems computational
  complexity
• Identifies properties of optimal schedules
• Proves the optimality of an exact solution
  approach
• Compares the performance of heuristic solution
  approaches experimentally.

15
Terminology framework
16
Terminology framework

• Rescheduling Environment
• Static rescheduling environment have a finite
  set of jobs
• Dynamic rescheduling have an infinite set of
  jobs

17
Terminology framework

• Deterministic static finite set of jobs and no
  uncertainty.
• Stochastic static finite set of jobs but some
  variables are uncertain
• e.g. processing times are random variable
• modify the schedule during
  execution to react additional information.
• construct a solution that only
  partially specifies the schedule.
• uncertainty is not modeled as a
  probability distribution.

18
Terminology framework

• Dynamic rescheduling
• No uncertainty or variability in the arrival
  process?cyclic scheduling
• Uncertainty in job arrivals, same route for all
  jobs, arrival rate steady?significant set ups
• Variability in process flow and job arrival
• Presence of additional capacity (subcontracting
  overtime?relax in capacity versus overtime costs)

19
Terminology framework

• Definitions (continued)
• Scheduling point point in time when a scheduling
  decision is made
• Rescheduling period time between two consecutive
  scheduling points
• Rescheduling frequency measures how often
  rescheduling is performed
• Scheduling stability measures the number of
  revisions or changes that a schedule undergoes
  during execution.(Cowling Johansson)

20
Cowling Utility versus stability

• Utility will measure the benefit which may be
  gained by using a particular rescheduling
  strategy.
• There is a continuum of different strategic
  approaches to dealing with real time information
  between do nothing and always reschedule to
  maximize utility without considering stability
  where we may trade-off Cbar and stability.

21
Cowling Utility versus stability
22
Cowling Utility versus stability
23
Terminology framework

• Definitions (continued)
• Scheduling nervousness significant changes in
  the schedule (opposite of schedule stability)
• Schedule robustness measures how much
  disruptions would degrade the performance of the
  system as it executes the schedule
• !!! NOTE Stability nervousness measure the
  changes to a schedule BUT robustness measures the
  changes to system-level performance.

24
Performance Measures

• Measures of schedule
• efficiency
• stability
• cost.
• Schedule efficiency used when generating a
  schedule.
• e.g. makespan, mean tardiness, mean flow time,
  average resource utilization, maximum lateness.

25
Performance Measures

• Schedule stability
• static, deterministic not considered
• Impact of schedule change defined as
• ?the starting time deviations btw the new
  schedule and the original one
• ?a measure of the sequence difference btw the
  two schedules Wu, Storer Chang
• M/C failure major concern (stable
  robust)

26
Performance Measures

• Schedule Robustness
• as the level of uncertainty ,
  frequent scheduling becomes more effective in
  improving the robustness.
• Economic Performance of the system
• due to lack of efficient time-based
  performance measure
• Min COST (starting jobs too early,
  WIP, Tardiness)

27
Performance Measures

• Economic Performance of the system conted
• Computational costs
• Setup costs
• Transportation costs
• Computational costs
• computational burden on the computer
  running the scheduling system
• non-recurring costs of investment
• recurring costs of administration,
  maintenance and upgrades
• time sent for generating and updating
  the schedule

28
Performance Measures

• Setup Costs
• occur when tooling fixtures are
  created or allocated
• Transportation Costs (material handling)
• delivering materials earlier than
  required
• additional material handling work
  to transport jobs form one scheduled M/C to other
  points

29
Performance Measures

• The relative values of the rescheduling period
  and the total mean processing time will affect
  the performance measures used in
  predictive-reactive rescheduling.
• Large rescheduling period versus small ones
• In job shop, scheduling objectives are complex.

30
Rescheduling strategies

• Comparison of two strategies in dynamic
  rescheduling environment that have uncertain job
  arrivals
• Dynamic
• versus
• Predictive-Reactive
• ?periodic
• ?event-driven
• ?hybrid

31
Rescheduling strategies

• Dynamic Scheduling (reactive, online)
• Does not create schedules
• Decentralized production control methods dispatch
  jobs
• when necessary
• use information at the time of
  dispatching
• Dispatching rules pull mechanisms
• when a M/C becomes available it chooses
  from among the jobs in its queue by using a
  dispatching rule that sorts the jobs by some
  criteria

32
Rescheduling strategies

• Dynamic Scheduling (reactive, online)
• Dispatching rules are classified as
• simple dispatching rules
• combinations of simple rules
• weighted priority indexes
• heuristic scheduling rules
• Panwalkar
  Iskander
• Coalition rule highly valued, individual rule
  rejected, traditional and theoretical not highly
  valued. Green Appel

33
Rescheduling strategies

• Dynamic Scheduling (reactive, online)
• Control Theoretic Models are used to develop
  rules for deciding which action to take an when
  to take it in response to random disruptions.
• M/Cs without setup least slack policy (due
  date-E(amount of time until the job is
  completed))
• M/Cs with setups focus on completing all waiting
  jobs of one type before performing a setup and
  processing of another type. Kumar

34
Rescheduling strategies

• Predictive- Reactive Scheduling
• generate a schedule
• updates the schedule in response to a
  disruption or other event to minimize its impact
  on system performance
• Iterative process Wu Li
• Evaluation step evaluates the impact of a
  disruption
• Solution step determines the rescheduling
  solutions that can enhance the performance of the
  existing schedule
• Revision step updates the existing
  production schedule or generates a new one

35
Rescheduling strategies

• Predictive- Reactive Scheduling
• Rescheduling approach Yamamoto Nof
• Planning Phase constructs an initial schedule
  just prior to the start of a new period
• Control Phase compares the actual progress of
  operations during a given period.
• Rescheduling Phase constructs a revised
  schedule considering the operational changes that
  have triggered rescheduling.

36
Rescheduling strategies

• Predictive- Reactive Scheduling
• Policies studied
• periodic
• hybrid rolling time horizon
• event-driven
• Rolling time horizon the overall scheduling
  problem is decomposed into smaller and static
  scheduling problems.

37
Rescheduling strategies

• Predictive- Reactive Scheduling
• Periodic Policy reschedules the facility
  periodically.
• used in environments where there is
  online data acquisition from the shop floor
• yields more schedule stability than
  constant rescheduling
• significant changes compromise
  performance
• determination of the optimal
  rescheduling period is not easy

38
Rescheduling strategies

• Predictive- Reactive Scheduling
• Event Driven Policy rescheduling can happen
  repeatedly in dynamic environments or a single
  event can revise a schedule in a static system.
• popular in static environment and used when
  M/C failure occurs.
• in dynamic environments used when total of
  job arrivals reaches a threshold. Viera
• require fast reliable electronic data
  collection.

39
Rescheduling strategies

• Predictive- Reactive Scheduling
• Hybrid Policy reschedules the system
  periodically and also when special (or major)
  events take place.
• e.g. major events M/C failures, arrival of
  urgent jobs, job cancellation or job priority
  changes

40
Rescheduling methods

• Methods used as a part of reactive-predictive
  scheduling is discussed.
• Concentrate on methods that generate robust
  schedules and methods that update schedules.
• Approaches for static and stochastic environments
  are considered.

41
Rescheduling methods

• Generating Robust Schedules
• Attempt to maintain good system performance with
  simple schedule adjustments.
• Some studies and results
• As the amount of processing time variability
  increases, dispatching rules led to better
  performance. Wu (branch bound)
• Schedules that are robust to stochastic
  disturbances could be generated without much
  degradation. Mehta Uzsoy (inserted idle time,
  shifting bottleneck algorithm)

42
Rescheduling methods

• Generating Robust Schedules
• When probability distributions are not
  available, worst case scenario is key objective.
  Daniels Kouvelis
• ODonovan uses first a dispatching rule and
  then a simple policy to insert idle time btw jobs
  based on E(downtime) in his method aiming at
  minimizing E(deviation in completion times).
• As the level of uncertainty increases, frequent
  scheduling becomes more effective in improving
  the robustness. Shafaei Brunn

43
Rescheduling methods

• Repairing Schedules
• Even if the managers and supervisors do not
  explicitly update the schedule, schedule repair
  occurs as the operators react to disruptions,
  delaying tasks or performing tasks out of order.
• Methods used to repair a schedule
• Right shift scheduling
• Regeneration
• Partial scheduling

44
Rescheduling methods

• Repairing Schedules
• Right shift scheduling postpones each remaining
  operation by the amount of time needed to make
  the schedule feasible

45
Rescheduling methods

• Repairing Schedules
• Partial rescheduling reschedules only the
  operations affected directly or indirectly by the
  disruptions.
• tends to maintain schedule stability by
  preserving the initial schedule
• Studies
• Comparison of performance under the proposed
  affected operations method to the total
  rescheduling and right shift scheduling methods.
  Abunaizar Svestka

46
Rescheduling methods

• Repairing Schedules
• Studies Continued
• Matchup scheduling procedure uses heuristic
  ordering rules to resequence all jobs scheduled
  before a matchup point. Optimal, when the
  disruptions are infrequent. Bean
• Matchup scheduling that partially reschedules a
  modified flow shop when a M/C failure occurs.
  Akturk Gorgulu
• Constraint based schedule repair Smith
• Repair tactics adjusting start times, swapping
  operations and switching to alternative
  resources. Miyashita Sycara

47
Rescheduling methods

• Repairing Schedules
• Regeneration reschedules the entire set of
• operations not processed before the
• rescheduling point, including those not affected
• by the disruption.
• Disadvantage, excessive computational effort and
  unsatisfactory response time.
• Genetic algorithm that reuses the previous
  solution to solve a job shop scheduling problem
  every time a new job arrives. Bierwirth
  Mattfeld

48
The impact of rescheduling policies

• The impact of
• Type of events that trigger rescheduling
• Rescheduling frequency
• on performance is studied.

49
The impact of rescheduling policies

• Church Uzsoy
• Hybrid, event-driven, for single and parallel
  M/Cs with dynamic job arrivals
• EDD rule is used
• Analytical models to bound the max(completion
  time)
• Periodic rescheduling lead to near opt. (min
  max lateness) when order release is periodic.
• Rescheduling at the arrival of a rush job is
  useful but more frequent rescheduling does not
  improve the performance.

50
The impact of rescheduling policies

• Vieira
• Single M/C, periodic event-driven based on
  queue size
• Analytical models can accurately predict the
  performance
• Extend to Parallel M/Cs
• Rescheduling frequency can significantly affect
  the average flow time.

51
The impact of rescheduling policies

• Vieira (continued)
• Lower freq. lowers the of setups, increases
  cycle time and WIP.
• Higher freq. Allows the system to react more
  quickly to disruptions but may increase the of
  setups.
• Event-driven and periodic strategies give
  similar performance.
• Rescheduling when a M/C fails after a repair
  decreases cycle time but increases the freq.

52
The impact of rescheduling policies

• Farn Muhlemann
• Via simulation
• Single M/C with sequence dep. setup times
• FIFO
• Often rescheduling leads to lower setup costs.
• Muhlemann
• Dynamic job shop
• Compare heuristics across scenarios
• scheduling period length, of jobs in the
  backlog, the amount of certainty in processing
  times and M/C failures

53
The impact of rescheduling policies

• Bean
• Matchup algorithm leads to better less total
  tardiness
• Wu
• Robust, partial schedule leads to better less
  weighted tardiness
• Processing time variability increases,
  dispatching rules lead better performance
• Mehta Uzsoy
• Predictive schedules (idle time) increase
  predictability but do not significantly degrade
  max lateness

54
The impact of rescheduling policies

• Kim Kim and Sabuncuoglu Karabuk
• Advantage to check the performance periodically
  
• Too-long monitoring periods and too-frequent
  monitoring negatively affect performance
• Shafaei Brunn
• Under loose due date conditions, the
  performance is not sensitive to changes in
  rescheduling interval.
• Under tight due date conditions, the
  rescheduling interval had a much more significant
  effect on performance.

55
The impact of rescheduling policies

• Through better coordination a longer rescheduling
  period can improve performance
• Herrmann Delaio
• Material inexpensive?decrease freq. Reduce costs
• Material expensive?changing freq. Does not affect
  costs much.

56
Scheduling theory practice

• Understanding the rescheduling can address the
  gap btw theory and practice.
• Theory has had limited impact on practice since
• results do not consider important
  characteristics of the environment
• the dynamic aspects of the
  manufacturing system has not been considered
  fully
• !!! Solving scheduling problems is IMPORTANT for
  controlling dynamic, stochastic systems.

57
Scheduling theory practice

• Importance of planning period
• Job cycle timesgtplanning period?careful
  scheduling
• Job cycle timesltplanning period?scheduling is
  seldom important satisfying the production
  target should set the constraints and objectives
  
• Portougal
  Robb

58
Scheduling theory practice

• Principles that explain practical scheduling
  processes
• generates partial solutions for partial
  problems
• anticipates, reacts to and adjusts for
  disturbances
• Is sensitive to and adjusts to the meaning of
  time in the production situation

59
Summary conclusion

• Papers formulate scheduling as a combinatorial
  optimization problem.
• Describe
• algorithms for generating or updating
  schedules
• new rescheduling policies that specify
  when schedules are generated and updated
• studies on dispatching rules, optimal
  control policies, rescheduling strategies

60
Summary conclusion

• This paper standardizes the rescheduling concepts
  and shows the gap btw theory and practice.
• Key results
• Rescheduling policy needs to be considered in
  system design. However existing models provide
  little support for rescheduling.

61
Summary conclusion

• More research is needed to
• ? compare the performance of systems under
  predictive-reactive policies to their performance
  under dynamic scheduling.
• ? understand how interactions btw rescheduling
  policies and other production planning functions
  affect performance.