Maintenance Routing Gábor Maróti CWI, Amsterdam and NS Reizigers, Utrecht [email protected] Models...
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Transcript of Maintenance Routing Gábor Maróti CWI, Amsterdam and NS Reizigers, Utrecht [email protected] Models...
Maintenance Routing
Gábor Maróti
CWI, Amsterdam
and
NS Reizigers, Utrecht
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Maintenance Routing
Gábor Maróti
Leo Kroon
Astrid Roelofs
CWI, Amsterdam
NS Reizigers, Utrecht
Erasmus University, Rotterdam
NS Reizigers, Utrecht
Free University, Amsterdam
NS Reizigers, Utrecht
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Maintenance Routing Problem formulation
successive shortest paths
Computational results
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Problem formulation
Train units
After reaching a kilometer limit, they have to be checked.
In practice: the most urgent units go for maintenance.
The operational plan must be changed.
Bottleneck: shunting
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
(Very) naive idea: solve the shunting problem at each station
Natural decomposition: solve the problem separately for the rolling stock types
(and try to estimate the shunting difficulty)
Solution: new rolling stock schedule in the planning horizon
Problem formulation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Input: duties: sequences of tasks on each day
list of urgent units
deadlines
the actual operational plan
Output: new operational plan, such that
the urgent units can reach the maintenance station
“the cost is minimal”
Problem formulation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
The planning horizon is short (e.g. 3 days).
delays
shortage of crew
shortage of rolling stock
necessary changes
in the plan
cancelled trains
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
What do they now do?
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
1. Assign a most urgent unit to a first available maintenance job
If no solution, change a bit the deadlines (1 day).
2. Try to route it there
3. Call the local sunting crew: “Is the route feasible?”
4. Iterate this process
What do they now do?
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
1
3
2
Deadlinesfor urgent units
Days
Nights
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Night change
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Night change
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Daily change
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Daily change: maybe possible
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Daily change: maybe possible
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Daily change
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Daily change (and a night change)
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Empty train movement
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
What do they now do?
Urgentunit
Assigned maintenance job
Empty train movement
(taking care of the balance)
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Nodes: arrival and departure events
Arcs: operational plan + extra possibilities
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
“Grey box”: permitted or forbidden arcs
A perfect matching is required
Night arcs:
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Night arcs:
Assumption: a small number of changes can be carried out
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Day arcs:
Simple daily change possibility
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Day arcs:
If we allow only one change for each train unit
it is enough to insert all these arcs
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Day arcs:
In case we allow also more complex changes
the graph becomes more complicated.
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Day arcs:
However, we did not implement multiple changes because
they did not give any extra possibility (in the test data)
A graph representation
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Empty train arcs:
extra arcs between the boxes: all or some of them
(a small number is enough)
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Models
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Solution: a new operational plan, i.e.
perfect matching on the Night Arcs
perfect matching on the Day Arcs
such that each urgent unit gets to the maintenance facility.
Models
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Quality of a solution: the extra shunting cost
Linear cost function: cost on the arcs
c (a) = 0 if a is in the original plan
c (a) 0 otherwise
Minimize the total sum of arc costs.
Idea: “the closer to the original plan the better”
Models
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Example:
StationUtrecht
expensive
cheap expensive
Models
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Night arcs: cheap, not too expensive or almost impossible
Day arcs: typically more expensive (more risky)
Empty train arcs: very expensive
carriage kilometer
crew schedule
Models
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Test data: rolling stock type “Sprinter”
52 units (duties)
1 maintenance job on each workday
1 maintenance station
10 terminal stations
2 further possible (daily) shunting stations
0 50km
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Successive shortest paths
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
1. Match the urgent train units to the maintenance jobs
2. For each urgent unit:
determine a shortest path in the graph
delete this path from the graph
take the next urgent unit
Algorithm
Successive shortest paths
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Easy, simple, very fast
Takes no care of matching conditions (day, night)
Ad hoc ideas are necessary
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Solution = Perfect matching in each box
1
3
2
s.t. the deadline conditions are fullfilled
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Matching variables m on the Night Arcs and Day Arcs (0-1 valued).
Still needed:
linear inequalities expressing that
1. each urgent unit reaches the maintenance facility
2. in the time limit
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Multicommodity flow
A 1-flow for each urgent unit
1
3
2
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
2
Possible terminal nodes
Deadline
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Variables:
matching variables m flows x1, x2, x3, …
Constraints:
matching constraints
conservation rule for each flow
starting and terminal constraints for the flows
xi m(e)
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Objective function:
minimize (c (a) m(a) : a Night or Day Arcs)
If m is integral, the values x may be chosen float (read-valued).
If x and m are integral on the Day Arcs,
the other variables may be chosen float.
Multicommodity flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Having fixed a matching m,
s
t
set two new nodes s and t,set all arc capacities 1.
Does there exist an s-t network flow of value 3?
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
x : Arcs[0 ; 1]
conservation rule for every nodess, t
the flow value is 3 (# of urgent units)
x(e)m(e) for Night Arcs
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Given a digraph G and a function C : ArcsR
is a node potential (for the longest path)
if
C(uv)(u)(v)
u v
for every arc uv.
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Having fixed a matchig m:
longest path = the only path
is an upper bound on the distance from the maintenance nodes (with appropriate initial values) (C 1)
0
00
BigBigBig
Big
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
1
32
Instead of the deadlines:
1
53
distances.
d(u) := 2 deadline(u)1
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
The important inequalities:
(u) (v) Big m(uv)) for Day and Night Arcs
(u) d(u) for urgent unit starting nodes
LB(v) (v)UB(v) for each node
The bounds LB and UB from the graph structure
Then Big := UB(v) LB(u) 1
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
matching variables m flow variables x potential variables
integral
may be chosen float
Variables:
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Constraints
matching variables m flow variables x potential variables
Variables:
matching constraints flow constraints potential constraints
Network flow
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Objective function:
minimize (c (a) m(a) : a Night or Day Arcs)
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Computational results
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Test data: rolling stock type “Sprinter”
3 - 5 days planning horizon
3 - 5 urgent units
IBM PC, Pentium III 900 MHz, 256 MB RAM
Software: ILOG OPL Studio 3.0, CPLEX 7.0
MF NFNP
3 units 17 sec 10 sec
5 units Nr. 1. 4 – 10 sec 10 sec
5 units Nr. 2 22 sec 12 sec
Computational results
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Only night connections (5 nights):
NFNP
3 nights 10 sec
4 nights 15 sec
5 nights 15 - 500 sec
Computational results
All possibilities:
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Maintenance Routing
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
Problem formulation
successive shortest paths
Computational results
What do the planners now do?
A graph representation
Models
multicommodity flow network flow and node potential
Future
Future
Models for Maintenance Routing 2nd AMORE Seminar, Partas, 30 - 10 - 2001
A lot cooperation with planners and shunting crew in
modelling the night shunting possibilities (costs)
determining the practical relevance of the solutions
finding the set of day connections
New criteria for the rolling stock scheduling
Thank you.