Vehicle Routing and Job Shop Scheduling: - PowerPoint PPT Presentation

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Vehicle Routing and Job Shop Scheduling:

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As VRP becomes less pure it looks more like JSSP ... people, tools, cranes, etc. etc. JSSP. Specialised toolkit. ILOG Scheduler. Depth 1st search or LDS ... – PowerPoint PPT presentation

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Title: Vehicle Routing and Job Shop Scheduling:


1
Vehicle Routing and Job Shop Scheduling Whats
the difference?
J. Christopher Beck, Patrick Prosser, Evgeny
Selensky
2
What this talk is about
  • VRP JSSP are essentially the same (NPC)
  • There are specialised toolkits for VRP and for
    JSSP
  • e.g. Dispatcher Scheduler
  • As VRP becomes less pure it looks more like JSSP
  • As JSSP becomes less pure it looks more like VRP
  • When should we treat a VRP as if it were a JSSP?
  • When should we treat a JSSP as if it were a VRP

3
VRP
Objective minimise travel minimise vehicles
used
Like a TSP but with many salesmen
4
VRP
  • Can be richer
  • time windows on visits
  • capacity of vehicles
  • type of vehicle
  • type of visit
  • sequencing between visits
  • minimise time of last visit

Like a TSP but with many salesmen
5
VRP
Specialised Tool Kit ILOG Dispatcher
Local Search GLS TS Construction Techniques
Savings etc Weak propagation Typically
Like a TSP but with many salesmen
6
JSSP
7
  • We have
  • a set of resources
  • a set of jobs
  • a job is a sequence of operations/activities
  • sequence the activities on the resources

8
An example 3 x 4
  • We have 4 resources green, yellow, red and blue
  • a job is a sequence of operations
  • each operation is executed on a resource
  • each resource can do one operation at a time
  • the duration of an operation is the length of
    its box

9
An example 3 x 4
Op1.2 Op2.1 Op3.4
Op1.1 Op2.3 Op3.1
Op1.3 Op2.2 Op3.3
Op1.4 Op2.4 Op3.2
And so on
10
  • Why bother?
  • Minimise makespan
  • Maximise start
  • JIT, minimise inventory levels
  • minimise idle time on resources
  • maximise ROI
  • ...

11
Variants of jsp
  • openness
  • variety of resources can perform an operation
  • processing time dependant on resource used
  • set up costs, between jobs (transition cost)
  • consumable resources
  • such as gas, oil, etc
  • pre-emption
  • can stop and restart an operation
  • resource can perform multiple operations
    simultaneously
  • batch processing
  • secondary resources
  • people, tools, cranes, etc
  • etc

12
JSSP
Specialised toolkit ILOG Scheduler
Depth 1st search or LDS powerful
propagation texture based heuristics
13
JSSP lt-gt VRP
  • We can model a jssp as a vrp
  • resources are vehicles
  • activities are visits
  • set up costs between activities are travel
    between visits
  • sequence within a job is sequence between visits
  • but these visits are on different vehicles
  • minimise completion of latest visit
  • and then solve with Dispatcher
  • NOTE pure jssp is a weird vrp
  • We can model a vrp as a jssp
  • vehicles are resources
  • visits are activities
  • distances are set up costs
  • a job has a single activity/visit
  • minimise transition times (set ups)
  • then solve with Scheduler
  • NOTE pure vrp is weird jssp

14
JSSP lt-gt VRP
VRP
JSSP
Scheduler
Dispatcher
?
There is a spectrum of problems As we vary
characteristics of problems do we move across the
spectrum and make better use other toolkits?
15
Features we can change for VRP JSSP
  • alternative resources
  • VRP, specialised fleet
  • JSSP, openness
  • temporal constraints
  • VRP, add sequencing constraints between visits
  • JSSP, remove sequencing constraints between
    activities
  • duration to transition time
  • VRP, increase time at visit and decrease travel
    between visits
  • optimisation criterion
  • VRP, minimise makespan (normally minimise travel
    vehicles used)
  • JSSP, minimise transition times (normally
    minimise tardiness)
  • temporal slack
  • how will this affect technology used?
  • resource capacity
  • the number of activities/visits a
    resource/vehicle can do/make in a solution

How do these 6 parameters affect solution
technology?
16
The experiments
17
Pure vrp pure jssp
  • VRP
  • Dispatcher is far better at pure vrp than
    Scheduler
  • JSSP
  • Scheduler is far better at pure jssp than
    Dispatcher

18
Alternative resources
  • VRP
  • with high vehicle specialisation Dispatcher
    fails to find solutions
  • we suspect culprit is the savings heuristic
  • find first solution with Scheduler then improve
    with Dispatcher
  • JSSP
  • Scheduler dominates Dispatcher
  • Dispatcher has to start from a Scheduler
    solution
  • precedence constraints cripple Dispatcher
  • as alternative resources increase neighbourhood
    increases
  • more failures due to precedence constraints

19
Resource Capacity
  • VRP
  • Dispatcher dominates Scheduler
  • as we increase vehicle capacity Dispatcher
    improves (relatively)
  • a loosening of resource constraints weakens
    Schedulers propagation
  • JSSP
  • allow activities to be performed on any machine,
    i.e. not a JSSP!
  • vary number of activities a machine can perform
    (from 100 down to 13)
  • Scheduler dominates Dispatcher
  • as capacity increases Scheduler gets even better
    than Dispatcher
  • probably because neighbourhood increases
  • many rejected Dispatcher moves due to precedence
    constraints

20
Precedence Constraints
  • VRP
  • Dispatcher dominates Scheduler
  • but we need to start from a Scheduler solution
  • Dispatcher degrades relatively as we add
    precedence constraints
  • JSSP
  • When no precedence constraints, we are in P and
    both find optimal solutions
  • As we add precedence constraints Dispatcher
    needs a Scheduler 1st solution
  • Dispatcher is then gradually dominated by
    Scheduler

21
Activity Duration v Transition Time
  • VRP
  • Dispatcher dominates Scheduler
  • varying duration and transition times has little
    effect
  • JSSP
  • Dispatcher dominates as we increase transition
    times
  • but we need to start with Scheduler solution

22
Optimisation Criterion
  • VRP minimise makespan
  • Dispatcher and Scheduler compete
  • possibly due to propagation through cost function
  • JSSP minimise transition times
  • Dispatcher competes with Scheduler
  • but we need to start with Scheduler solution

23
Slack
  • VRP
  • Dispatcher dominates Scheduler
  • As slack decreases Scheduler improves relative
    to Dispatcher
  • JSSP
  • Scheduler dominates
  • As slack decreases Scheduler finds best solution
    earlier
  • of course

24
Conclusions
  • Scheduling technology is robust
  • frequently comes to the rescue in VRP
  • VRP technology is crippled by precedence
    constraints
  • result of initial construction with Savings
  • Good option is frequently to
  • initial solution from Scheduler
  • improve with Dispatcher
  • Optimisation criteria has profound effect
  • we guess this is because of how it affect
    propagation
  • VRP still looks like a VRP when travel is
    compressed
  • good news for urban problems

25
Know your problem, and understand your technology
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