Rescheduling -review

1
Applications of Dynamic Re-scheduling
Methodologies
t
Gerry Kelleher and Abdennour el-Rhalibi
2
Introduction

• Preparing predictive schedule is not enough.
• there are many events that require the revision
  of the predictive schedule.
• a frequent comment in many scheduling contexts is
  that scheduling is not a problem but rescheduling
  is

3
Terminology

• Rescheduling, process of updating an existing
  production schedule in response to disruptions
• Disruptions (Rescheduling Factors)

• Machine Failure
• Urgent Job Arrival
• Job cancellation
• Due date change
• Operator Absenteeism

• Change in Job Priority
• Delay in Arrival
• Rework or Quality Problems
• Over or under estimation of processing times

4
Terminology

• Scheduling, creating production schedules and
• Rescheduling framework, consists of rescheduling
  environment, rescheduling strategies,
  rescheduling policy and rescheduling methods

5
Triggering Events

• The current schedule has become infeasible
• The current schedule is likely to fail based on
  some performance measures
• Detection of opportunities to improve the
  schedule while the current schedule is still
  acceptable
• Rescheduling is done with fixed frequency

6
Rescheduling Framework
7
Rescheduling Strategies

• Dynamic Scheduling
• do not use scheduling policies, uses current
  information to dispatch the jobs (eg FIFO, EDD,
  SPT)
• tradeoff utility, measure of improvement, against
  stability, measure of nervousness
• three types of actions upon information arrival
  no move, repair and reschedule.

8
Dynamic Scheduling

• Utility, a measure of improvement such as,
  decrease in total completion time

• Stability, a measure of nervousness, such as,
  total change in start times and completion times

• Utility Stability vs. time of arrival
  information and/or change in the current system
• Decide on repair or reschedule

9
Predictive-Reactive Scheduling

• Evaluation step, generate a robust schedule,
  evaluating the impact that a disruption causes
• Solution step, determine rescheduling solutions
  enhancing the current performance
• Revision step, update the existing schedule or
  generate a new one

10
Rescheduling Methods

• Right shift rescheduling, postpones each
  remaining operations

• Partial rescheduling, schedules only operations
  affected by the disruption
• Matchup scheduling, reschedule all the jobs
  before a matchup point
• If point too large, use integer programming or
  dispatching rules

Regeneration, reschedule the entire jobs before
the rescheduling point
11
P I S C E S (Pipeline Intermodal System to
Support Control Expedition and Scheduling) IN-96-
SC.1204 Partners Fraser Williams Logistics
Ltd. Van Ommeren Agencies Rotterdam
BV Liverpool John Moores University
PROJECT FUNDED BY THE EUROPEAN
COMMISSION UNDER THE TRANSPORT RTD PROGRAMME
OF THE
4TH FRAMEWORK PROGRAMME
12
PISCES and Logistics Evolution
Total Integration 2000s
Fragmentation 1960s
Evolving Integration 1980s
Demand Forecasting
Purchasing
Warehousing
Requirements Planning
Production Planning
Materials Management
Manufacturing Inventory
Warehousing
Logistics
Materials Handling
Industrial Packaging
PISCES
Physical Distribution
Inventory
Distribution Planning
Transportation
Order Processing
Transportation
Customer Service
13
Pipeline Intermodal System to Support Control
Expedition and Scheduling
Freight Forwarder
Bookings Cargo receipts Packing lists Shipping
advice
Cargo Manifests Shipment Status
Pre advice of container contents
PISCES Database
Customs documentation
Customs clearance
Check commodity availability/location
Accept shipments into inventory
PO
Delivery Details
Wholesaler/Retailer
14
Pipeline Intermodal System to Support Control
Expedition and Scheduling
VELOCITY CRITICAL PATH

• Speed of information transfer related to need
• Neutral database to maintain relationships
• No need to publicise actual parties or cargo
  details
• Integrate info/services/equipment to flex
  critical path
• Provide adaptive algorithms for
  scheduling/optimisation
• Focus on operational milestones
• Easy access via Internet

15
Support to Logistics Management Decisions
Location Choice Transport Mode Selection
Vendor Choice
STRATEGIC
Uncertainty
Throughput levels Employment levels Distribution
routes
Time frame
TACTICAL
Scope
Vehicle scheduling Order tracking
Inventory replenishment
OPERATIONAL
16
  Figure 1 The Vehicle Routing Problem
17
ContainerTransport
Delivery
Empty Running
Delivery
DEPOT
CUSTOMER
Delivery
Collection
Positioning
Delivery
PORT
Collection
CUSTOMER
Delivery
DEPOT
Collection
Positioning
CUSTOMER
18
ContainerTransport
Delivery
Empty Running
Delivery
DEPOT
CUSTOMER
Delivery
3
Positioning
4
Empty
Collection
PORT
1
Delivery
CUSTOMER
Delivery
Collection
2
DEPOT
5
6
Collection
Positioning
CUSTOMER
Collection
19
Rotterdam
Amsterdam
Antwerp
Hannover
Cologne
Trier
Duisberg
Bonn
Dortmund
Metz
Munich
Strasbourg
Stuttgart
Basel
Transport Scheduling
20
Rotterdam
Amsterdam
Antwerp
Hannover
Cologne
Trier
Duisberg
Bonn
Dortmund
Metz
Munich
Strasbourg
Stuttgart
Basel
Transport Scheduling
21
Transport Scheduling
Constraints on Length/Durationof Tour
Time Window
Pickup andDelivery
CONTAINERS TRANSPORT TRIANGULATION CONSTRAINTS
DynamicChanges
Multiple Types of Vehicles
Multiple Ports/Depots
Capacited Vehicles
Containers/GoodsCompatibility
Intermodality
22
Transport Scheduling
Total Traveled Distance
Empty Running
Cost
SCHEDULING OptimisationCriteria RESCHEDULING
Maximise Length of Triangulated Legs
Maximise Use of Intermodal AlternativeBarge/Trai
n
Minimise Changes from the Initial Routing
Minimise Delays
Minimise Introduction of New Resources
23

• Design of a Software Package to Produce Routing
  Scheduler
• Application to the Triangulation Problem, for the
  Transport of Containers.
• Application to Classical Vehicle Routing Problems.

• We take advantage of two techniques by using an
  hybrid approach
• a CSP program to compute feasible solutions on a
  subspace of the search space.
• a GA to explore the space formed by the
  solutions provided by the CSP, and perform the
  optimisation

24
Routing Scheduler
Reasoning Module
Optimisation Module
Selection
CSP Solver
Feasible solutions
Parent
Infeasible solutions
Recombination Mutation
CSP Generator Variables Domains Constraints
Population
Repaired solutions
Offspring
Forward Checking/ Orderings
Replacement
Constraint Satisfaction and Genetic Algorithm
25
Performance on Van Ommerens Problems
Triangulation of Containers Transport
26

• Tyre Manufacturing (Pirelli)
• Problem - add rescheduling capability to an
  existing system - BIS (Banbury Information System
• Scheduling of the Banbury Area is a job-shop
  scheduling task input to the system is a
  production plan containing customers and
  production orders (denoted requirements
  typically 50 per day).
• Scheduling horizon varies with the due-date of
  the orders, ( typically 2 days).

27

• Scheduling difficult, complex in itself but also
  requires reaction in real-time to change
• small revisions because of short stops of
  machines.
• major revisions because of breakdown
• customer order changes
• feedback from quality control on finished tyres
• breakdowns in semi-manufacturing, building and
  curing areas.

28
The objectives to be optimised include 1.
Minimise the tardiness of the requirements 2.
Keep stock-levels within a defined
minimum/maximum 3. Optimise the standing times
of compounds 4. Maximise machine utilisation 5.
Minimise set-up-time for the machines 6. Use
prioritised machines
29
     
     
 Table 1 Causes of Rescheduling
30
 System Architecture Schematic
31
Rescheduling Time
DAY 1
DAY 2
Shift3
Shift2
Shift1
Reaction Time
Dosage Time
Dosage Horizon
Zone 4
Zone 3
Time Zones for Rescheduling
32
Disruption Function Weighting
33
 
 
 
 
34
(No Transcript)
35
Notes and Conclusion

• Cost of rescheduling policies depends on
  frequency of rescheduling
• Implementation of rescheduling policy depends on
  information acquisition
• More research on the interaction of rescheduling
  policies with other production planning decisions
  is needed

36
 
 
 
 
 
 
37
Levels of experiment
38
Table 29 Classification JSSP Benchmarks
Table 29 Classification JSSP Benchmarks
39
Efficiency defined as the percentage change in
makespan of the repaired schedule compared to the
preschedule
where, h Efficiency Mnew Makespan of the
rescheduled schedule Mo Makespan of the
preschedule
40
Stability is the absolute sum of difference in
starting times of the job operations between the
initial and the rescheduled schedules. It is then
normalized as a ratio of total number of
operations in the schedule. A schedule will be
stable if it deviates minimally from the
preschedule.
where, x Normalized deviation. pj number of
operations of job j. k number of jobs. Sji
Starting time of ith operation of job j in
repaired schedule. Sji Starting time of ith
operation of job j in original schedule.
41
  Table 31 Efficiency and Stability of
rescheduling (machine breakdown)
 
 
   
 
 
   
 
 
42
 
43
importance
optimisation criteria
Time/number of actions
t0 Disruption incidents(s)