l. douglas smith donald c. sweeney ii james f. campbell robert m. nauss
Post on 18-Jan-2016
49 Views
Preview:
DESCRIPTION
TRANSCRIPT
1Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008
Center for Business and Industrial Studies
Center for Transportation
Studies
Analytical Modeling for Inland Waterway Traffic Management and Infrastructure: Experience from the Upper Mississippi
River Navigation System
L. Douglas Smith
Donald C. Sweeney II
James F. Campbell
Robert M. Nauss
College of Business Administration University of Missouri – St. Louis
One University Blvd.St. Louis MO 63121
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 2
Center for Business and Industrial Studies
Center for Transportation
Studies
Upper Mississippi River (UMR) Navigation System
Extends 663 miles from St. Louis to Minneapolis.
Includes 29 lock and dam facilities to raise vessels 300 feet.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 3
Center for Business and Industrial Studies
Center for Transportation
Studies
Commercial barge traffic
Carried 73.3 million tons in 2004.
Agricultural products travel downstream – most to New Orleans for export.
Other bulk commodities (petroleum, chemicals, etc.) travel back and forth in dedicated tows.
Barges measure 35 ft x 195 ft and hold 1500 tons.
UMR tows include up to 15 barges totaling nearly 1200 ft long.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 4
Center for Business and Industrial Studies
Center for Transportation
Studies
Barges have great capacity but travel slowly (9 mph downstream, 5 upstream)
A 15-barge tow carries more than two unit trains.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 5
Center for Business and Industrial Studies
Center for Transportation
Studies
Locks on the UMR vary in capacity
Old locks are 600-ft long, but some locks have been expanded to 1200-ft.
Locking a small tow in a 600-ft long lock takes about 30 minutes.
Locking a 1200-ft long tow in a 600-ft long lock takes about 2 hours because it has to be broken and winched through!
600 feet
1200 feet
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 6
Center for Business and Industrial Studies
Center for Transportation
Studies
Schematic of a lock service system
DownstreamMooring Buoy Recreational
DownstreamMooring Buoy Commercial
UpstreamMooring Buoy Recreational
UpstreamMooring Buoy Commercial
Lock Chamber Downstream DepartureUpstream Departure
7Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008
Center for Business and Industrial Studies
Center for Transportation
Studies
The UMR is a series of interdependent service facilities (locks) with multiple queues that serve vessels and tows with highly seasonal traffic patterns and varying itineraries.
Five 600-foot locks in series between two 1200-foot locks north of St. Louis create traffic bottlenecks with seasonal delays.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 8
Center for Business and Industrial Studies
Center for Transportation
Studies
Alternative remedies proposed to deal with the bottlenecks
U.S. Army Corps of Engineers (USACE) proposes to expand the five locks to 1200 feet (approx. $2.8 billion over five years).
National Research Council (NRC) proposed exploring less costly alternatives:
- Smaller infrastructure investments (more modest expenditure) to increase efficiency of existing assets.
- Alternative scheduling procedures (minimal expenditure).
9Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008
Center for Business and Industrial Studies
Center for Transportation
Studies
Realistic models are needed to test the effects of scheduling rules and infrastructural improvements under different traffic scenarios.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 10
Center for Business and Industrial Studies
Center for Transportation
Studies
Waiting times vary among the five locks
Different mixes of traffic, river conditions, and vessel maneuverability.
Upstream movements differ from downstream movements.
Itineraries, lockage times and pool transit times vary with tow configuration.
Occasional impairments to operations.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Sum
(w
aithrs)
1 2 3 4 5 6 7 8 9 10
11
12
month
20
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Sum
(w
aithrs)
1 2 3 4 5 6 7 8 9 10
11
12
month
21
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Sum
(w
aithrs)
1 2 3 4 5 6 7 8 9 10
11
12
month
22
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Sum
(w
aithrs)
1 2 3 4 5 6 7 8 9 10
11
12
month
24
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Sum
(w
aithrs)
1 2 3 4 5 6 7 8 9 10
11
12
month
25
LO
CK
_N
O
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 11
Center for Business and Industrial Studies
Center for Transportation
Studies
Considerations in locally sequencing vessel lockages
Immediate Efficiency
Equity to Users
Flexibility to derive future efficiency as succeeding events occur
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 12
Center for Business and Industrial Studies
Center for Transportation
Studies
Deterministic analysis of processing sequences to minimize total expecting waiting time of vessels when clearing current queues at a lock
Lockage times depend on changes in lock configuration (turnback or exchange) in addition to type of tow.
Nauss (2007 EJOR) used integer programming to create the optimal locking sequence for clearing all the queues at a lock.
- If waiting times were weighted equally for each towboat in the queue, solutions involved selecting vessels according to fastest locking time and may alternate upstream and downstream
Here, we add constraints for equity considerations
- Delay vessel in IP solution no more than a designated interval relative to its FIFO position (6 hours or 8 hours)
The new constraints are nonlinear and necessarily change the solution from FLT sequence
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 13
Center for Business and Industrial Studies
Center for Transportation
Studies
IP Problem parameters
ND: total number of TB’s in queue on the downstream side of the lock (headed upstream). NU: total number of TB’s in queue on the upstream side of the lock (headed downstream). N: total number of TB’s in queue on both sides of the lock (N = ND + NU).
itmdtr for i = 1, ...ND : expected lockage time for the ith TB on the downstream side if lockage is a turnback.
tmdex1 for i = 1, ...ND : expected lockage time for ith TB on the downstream side if lockage is an exchange. :N...,1for Ui itmutr expected lockage time for the ith TB on the upstream side if lockage is a turnback.
:N...,1for Ui itmuex expected lockage time for the ith TB on the upstream side if lockage is an exchange.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 14
Center for Business and Industrial Studies
Center for Transportation
Studies
IP objective function and constraints
(1)
N
1jjENDLOCKMINIMIZE
subject to:
(2)
N
1jDijij N...1i 1 )EXDTRD( (downstream vessel i locked as turnback or exchange)
(3)
N
1
N...1 1 )( j
Uijij iEXUTRU (similarly for upstream vessel i)
(4) 1N,...1j 1 ZZZZ jUDjDUjDDjUU (jth lockage must be upstream or downstream
turnback or exchange)
(5)
D U
i ii ENDLOCKTRUtmutrtmdtr
N
11
N
111i11 *TRD* (start with turnback)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 15
Center for Business and Industrial Studies
Center for Transportation
Studies
IP constraints (cont.)
(6) i
N
1i)1j( (ENDLOCK
D
tmdtr
TRD ij+ itmdex ijEXD ) +
UN
1ii(tmutr ijTRU itmuex ijEXU ) ≤ N,...2jfor ENDLOCKj
(calculate ending time for next lockage)
(7) 1N,...1j Z1TRD)TRDEXD( jDD
N
1i1j,iijij
N
1i
DD
with ZjDD ≤ 1N,...1 TRD Zand )TRDEXD(N
11,jDD
N
1
jUU
ijiijij
i
(force consistency in definition of downstream turnback)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 16
Center for Business and Industrial Studies
Center for Transportation
Studies
IP constraints (cont.)
(8) 1N,...1 1)(N
11,
N
1
jZTRUTRUEXU jUUi
jiijiji
UU
with ZjUU ≤ 1N,...1 TRU Zand )TRUEXU(N
11,jUU
N
1
jUU
ijiijij
i
(force consistency in definition for upstream turnback)
(9) 1N,...1j Z1EXU)TRDEXD( jDU1j,i
N
1iijij
N
1i
UD
(force consistency in definition for next lockage upstream as an exchange)
1N,...1 Zand)( with Z 1,
N
1jDU
N
1jDU
jEXUTRDEXD jii
ijiji
UD
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 17
Center for Business and Industrial Studies
Center for Transportation
Studies
IP constraints (cont.)
(10) 1N,...1 1)(N
11,
N
1
jZEXDTRUEXU jUDi
jiijiji
DU
1N,...1 Zand)( with ZN
11,jUD
N
1jUD
jEXDTRUEXUDU
ijiijij
i
(force consistency in definition for next lockage downstream as an exchange)
(11) N,...1j 1)TRUEXU()TRDEXD( ij
N
1iijij
N
1iij
UD
(force jth lockage to be one of the four alternatives)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 18
Center for Business and Industrial Studies
Center for Transportation
Studies
IP formulation (cont.)
(12) D1i N...,1i0EXD
U1i N...,1i0EXU
ijEXD = 0 or 1 N,...1jN,...1 ,Di
ijTRD = 0 or 1 N,...1jN,...1 ,Di
ijEXU = 0 or 1 N,...1jN,...1 ,Ui
(13) ijTRU = 0 or 1 N,...1jN,...1 ,Ui
1N,...1j 1or 0Z jDD
1N,...1j 1or 0Z jUU
1N,...1j 1or 0Z jDU
.1N,...1j 1or 0Z jUD
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 19
Center for Business and Industrial Studies
Center for Transportation
Studies
Additional nonlinear constraints for equity
(14) N,...1i FIFO)ENDLOCK()TRDEXD( D
N
1jiDjijij
waitlim
(15) .N,...1i FIFO)ENDLOCK()TRUEXU( U
N
1jiUjijij
waitlim
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 20
Center for Business and Industrial Studies
Center for Transportation
Studies
Random problem sets for peak traffic
20 random sets of single and double tows with 0.9 probability of a double tow; 20 random sets of single and double tows with 0.7 probability of a double tow
Problems solved with varying equity constraints
- Waitlim set very large (99999 minutes) to relax the constraint and revert to FLT
- Waitlim set to 6 hours (360 minutes)
- Waitlim set to 8 hours (480 minutes)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 21
Center for Business and Industrial Studies
Center for Transportation
Studies
IP results for 90:10 ratio of double tows : single tows
Tow Configuration
Average Number
in Beginning
Queue
Av Total
Clearing Time FIFO
Av Total Time without
Displacement Restriction
(pct change in parentheses)
Av Total Time
with 480-min. Limit
(pct change in parentheses)
Av. Total Time
with 360-min. Limit
(pct change in parentheses)
Doubles Upstream 8.8 1174.1 580.3 (-50.6) 812.0 (-30.8) 933.7 (-20.5) Singles Upstream 1.1 1099.1 61.4 (-94,4) 64.0 (-94.2) 58.8 (-94.6) Doubles Downstream 9.15 1055.7 1612.8 (+52.8) 1400.3 (+32.6) 1309.3 (+24.0) Singles Downstream 0.95 1047.9 37.4 (-96.4) 33.8 (-96.8) 43.8 (-95.8) Doubles total 17.95 1115.5 1111.7 (-0.3) 1131.8 (+1.5) 1140.0 (+2.2) Singles total 2.05 1102.1 45.8 (-95.8) 46.0 (-95.8) 46.4 (-95.8) All vessels 20 1114.1 1004.2 (-9.9) 1022.5 (-8.2) 1029.8 (-7.6)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 22
Center for Business and Industrial Studies
Center for Transportation
Studies
IP results for 70:30 ratio of double tows : single tows
Tow Configuration
Average Number
in Beginning
Queue
Av Total
Clearing Time FIFO
Av Total Time without
Displacement Restriction
(pct change in parentheses)
Av Total Time
with 480-min. Limit
(pct change in parentheses)
Av. Total Time
with 360-min. Limit
(pct change in parentheses)
Doubles Upstream 6.55 892.0 603.1 (-32.4) 785.3 (-12.0) 853.3 (-4.3) Singles Upstream 2.75 808.7 146.1 (-81.9) 145.6 (-82.0) 141.2 (-82.5) Doubles Downstream 7 929.5 1347.5 (+45.0) 1210.6 (+30.2) 1162.8 (+25.1) Singles Downstream 3.7 873.3 111.6 (-87.2) 112.5 (-87.1) 122.6 (-86.0) Doubles total 13.55 913.7 998.7 (+9.3) 1016.0 (+11.2) 1021.2 (+11.8) Singles total 6.45 845.0 120.0 (-85.8) 120.7 (-85.7) 121.1 (-85.7) All vessels 20 897.9 714.6 (-20.4) 726.3 (-19.1) 729.9 (-18.7)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 23
Center for Business and Industrial Studies
Center for Transportation
Studies
Summary of inferences from deterministic analysis
Without waitlim constraints to promote equity, optimal solution is FLT (if consider set-up and locking times that both depend on whether the lock is turned back)
As expected, greater diversity in vessel mix gives greater opportunity for improvement
Adding waitlim constraints has minor effect on total time in queue for all vessels
Must recognize that benefits will be less in slack periods
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 24
Center for Business and Industrial Studies
Center for Transportation
Studies
The system is nondeterministic and the objective is complicated
The queueing problem and optimal sequence can change with each arrival.
Actual activity times deviate from expected times used in the deterministic model.
Self-adapting behavior in periods of congestion can distort data and alleviate some problems without changing formal operating procedures.
First-come, first served is seen as a guiding principle that promotes equity (absent a priority charging scheme).
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 25
Center for Business and Industrial Studies
Center for Transportation
Studies
Scheduling rules need to be tested under stochastic conditions
For local scheduling, fastest locking time (FLT) is seen as promoting efficiency, FIFO is seen as promoting equity.
The barge industry demands simple rules that are easy to understand and implement without revealing proprietary information (including cargoes and destinations).
We developed a series of local scheduling rules with variants on FLT to consider efficiency and equity and tested their impact on the stochastic system.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 26
Center for Business and Industrial Studies
Center for Transportation
Studies
Simulation model requirements
Must accommodate multiple classes of vessel traffic with different arrival patterns, itineraries and service characteristics.
Queueing and processing structure that captures physical realities of upstream and downstream traffic movements to and from the locks.
Detailed measures of system performance that show the mix of vessel traffic movements, facility utilization, waiting times and queue sizes in the vicinity at each lock at different times.
Tests of statistical significance of observed effects on system performance.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 27
Center for Business and Industrial Studies
Center for Transportation
Studies
Discrete event simulation model infrastructure
SAS (Statistical Analysis System) front-end for historical analysis and generating equations for time and event-varying model parameters.
ARENA 10.0 discrete-event simulator to represent system behavior and generate experimental results under different rules and traffic scenarios.
SAS back-end for reporting and analysis of simulated system performance.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 28
Center for Business and Industrial Studies
Center for Transportation
Studies
ARENA simulation model
Discrete-event simulation model with Markovian structure for generation of vessel itineraries and activity times and for exercising alternative traffic control policies.
Seasonal random arrivals generated with monthly effects, day-of-week effects, and time-of-day effects that differ according to vessel-tow characteristics.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 29
Center for Business and Industrial Studies
Center for Transportation
Studies
Generating random arrivals
Nonstationary exponential distributions are used in conjunction with probabilistic intensification and thinning processes to impose differential arrival rates for various classes of vessel according to:
- Month of year
- Day of Week
- Time of day
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 30
Center for Business and Industrial Studies
Center for Transportation
Studies
Imposing other systematic variation
Itineraries and activity times differ according to vessel-tow configuration, sequence of lockage operations, traffic levels and river conditions.
Lock operations data were partitioned for different locks and vessel-tow combinations and 100+ regression and logistic models were created for dynamic setting of system parameters.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 31
Center for Business and Industrial Studies
Center for Transportation
Studies
Lognormal distributions for conditional activity times
Raw lockage times Residuals of partitioned log regression
log(lockhrs for double lockage at 24U) = 0.599 - 0.096*feb + 0.080*jun-0.080*jul + 0.040*sep + 0.053*oct - 0.117*turnback
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 32
Center for Business and Industrial Studies
Center for Transportation
Studies
Simulated versus actual year 2000 arrivals by day of week (percent each tow type) in 100 replications
Day ofWeek
Double Singlewith Barges
Jack-knife Knock-out Singlew/o Barges
Rec’n
Sun 63.162.9
8.57.8
1.61.5
1.61.6
4.14.8
21.121.4
Mon 65.365.8
11.010.7
1.61.5
1.91.5
7.47.4
12.913.2
Tue 66.966.9
11.912.8
2.01.7
2.82.2
7.57.6
8.98.8
Wed 64.865.0
13.114.1
2.01.6
2.32.0
7.97.7
9.99.5
Thu 62.663.3
14.915.1
1.61.6
2.42.2
7.47.1
11.110.7
Fri 63.862.7
11.512.7
1.61.4
2.22.2
6.16.4
14.714.5
Sat 59.760.3
9.48.6
1.61.3
2.11.9
5.66.0
21.521.8
(Top number is percent from simulation; bottom number is year 2000 actual
percent.)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 33
Center for Business and Industrial Studies
Center for Transportation
Studies
Simulated versus actual year 2000 arrivals by time of day (percent each tow type) in 100 replications
Hour of Day
Double Singlewith Barges
Jack-knife
Knock-out
Single w/o Barges
Rec’n
00 74.875.7
14.113.2
2.21.9
1.72.2
6.66.4
0.60.6
11 49.250.0
12.011.0
1.01.0
2.42.0
6.46.3
29.029.7
16 57.257.2
9.110.7
1.71.4
1.81.8
6.06.8
24.122.1
20 71.071.6
12.112.4
1.31.6
2.32.0
7.57.6
5.74.8
(Top number is percent from simulation; bottom number is year 2000 actual percent.)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 34
Center for Business and Industrial Studies
Center for Transportation
Studies
Average monthly utilization for Lock 22
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1 2 3 4 5 6 7 8 9 10 11 12
Month
Perc
en
t U
tiliza
tio
n
2000 OMNI
FIFO
RECPRIO
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 35
Center for Business and Industrial Studies
Center for Transportation
Studies
Comparisons of average monthly queue sizes upstream and downstream
Down Up Total Down Up TotalLock20 0.44 0.42 0.86 0.59 0.68 1.2721 0.48 0.44 0.92 0.46 0.49 0.9522 0.74 0.8 1.54 1.04 1.05 2.0924 0.65 0.82 1.47 0.94 0.94 1.8825 0.67 0.82 1.49 0.93 1.08 2.01
Average queue sizes in MayActual Year 2000 FIFORECPRIO Results
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 36
Center for Business and Industrial Studies
Center for Transportation
Studies
Alternative rules for sequencing lockages
FIFO (First In, First Out) - the traditional benchmark in the simulation literature.
FIFORECPRIO - a variation on FIFO where priority is given to recreational vessels (this policy closely matches the prevailing Corps guidelines).
FLTX – Fastest Locking time with priority escalation for vessels experiencing long delays.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 37
Center for Business and Industrial Studies
Center for Transportation
Studies
Analysis
We used the results from 100 replications (years) of simulated activity to assess the impact of the alternative scheduling rules.
Experiments were also performed at different traffic levels
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 38
Center for Business and Industrial Studies
Center for Transportation
Studies
Mean wait and lock transit times (minutes) with Year 2000 traffic levels
Mean wait and transit times in minutes are for the study area over 100 simulated years of operation with current traffic levels
Vessel-tow Configuration
FIFO FIFORECPRIO FLTX 480 min
FLT FLTX 360 min
Wait Transit Wait Transit Wait Transit Wait Transit Wait Transit Double 161.1 275.7 162.9 277.5 168.3 281.9 170.6 284.2 164.5 278.2 Jackknife 178.9 262.6 183.5 267.2 123.6 205.9 101.3 183.5 125.4 208.0 Knockout 169.5 230.7 172.6 233.9 123.6 183.8 112.4 172.7 123.5 183.9 Single 161.0 192.0 163.9 194.9 109.3 139.8 94.6 125.0 113.4 143.9 X-Barge 155.8 179.3 157.8 181.4 97.7 121.3 86.1 109.6 101.7 125.5 Recreation 189.4 202.6 49.1 62.4 48.4 61.7 48.3 61.5 48.4 61.7 All Lockages 165.2 247.9 146.7 229.4 137.9 219.9 136.6 218.6 136.2 218.1 Replication Averages, Pairwise groupings (A, B, …) and Sequencing Impact from Basic ANOVA
A 247.5
(+18.2 Min.)
B 229.3
(0 Min.)
C 220.5
(-8.8 Min.)
C 219.1
(-10.1 Min.)
C 218.6
(-10.7 Min.)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 39
Center for Business and Industrial Studies
Center for Transportation
Studies
Time savings are greater at increased traffic levels
We evaluated the sequencing alternatives with ranges in traffic level from -10% to +30% of year 2000 levels, while keeping the mix of vessel arrivals, seasonality and lockage types as observed in year 2000.
There was an increasing advantage of FLT as demand increases, particularly for the single tows, but an emerging need to deal with extreme waits for double tows.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 40
Center for Business and Industrial Studies
Center for Transportation
Studies
TrafficLevels
NominalPolicy
(FIFORECPRIO)
SinglesPriority
(SINGPRIO)
FastestLocking
Time (FLT)
Time in Queue
Total Lock
TransitTime
Time inQueue
Total LockTransitTime
Time in Queue
Total LockTransitTime
Year 2000 D: 163S: 164
D: 277S: 195
D: 174S: 101
D: 289S: 131
D: 171S: 97
D: 284S: 125
Year 2000Plus 10%
D: 288S: 278
D: 402S: 308
D: 311S: 130
D: 425S: 159
D: 287S: 119
D: 399S: 149
Year 2000Plus 20%
D: 880S: 797
D: 992S: 827
D: 1003S: 177
D:1115S: 205
D: 794S: 152
D: 900S: 181
Effects on average times at locks (over 100 simulated years) differ greatly for double-tow (D) vs. single-tow (S) lockages
Average Times in Queue and at Lock (mins.)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 41
Center for Business and Industrial Studies
Center for Transportation
Studies
Overall performance with 360 min. and 480 min. priority shifting criteria are quite similar
Medians and 95th Percentiles of Waiting Times
with YR 2000 Commercial Traffic Plus 20%
(without common random number streams for arrival generators)
FIFORECPRIO (benchmark)
FLTX (1yr - no priority shift)
FLTX 480 min
FLTX 360 min.
Vessel-tow Configuration
Median 95th Median 95th Median 95th Median 95th Double 327 3,689 165 3,688 401 3,283 385 2,902
Jackknife 378 3,645 116 705 200 3,029 211 2,667 Knockout 320 3,245 127 716 187 2,914 190 2,593
Single 293 3,411 106 630 157 2,920 161 2,598 Single ex Barge 271 3,079 92 573 137 2,997 140 2,639
Recreational 51 125 49 123 51 130 50 131 Average No.
Lockages 22,063 21,751 21,837 21,755
Resulting Averages of Total Transit Time All vessels
825 618 (-25%) 741 (-11%) 685 (-17%)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 42
Center for Business and Industrial Studies
Center for Transportation
Studies
We had to use common number streams for arrival generators to get results completely consistent with the IP, further suggesting differences in performance of FLTX-360 and FLTX-480 would be hard to detect in practice.
Medians and 95th Percentiles of Waiting Times
with YR 2000 Commercial Traffic Plus 20%
(but without common random number streams for arrival generators)
FIFORECPRIO (benchmark)
FLTX (1yr - no priority shift)
FLTX 480 min
FLTX 360 min.
Vessel-tow Configuration
Median 95th Median 95th Median 95th Median 95th Double 314 3,694 164 3,385 377 3,120 386 3,224
Jackknife 365 3,641 117 721 199 2,787 217 3,045 Knockout 310 3,303 127 699 183 2,687 194 2,934
Single 281 3,481 105 628 152 2,734 162 2,984 Single ex Barge 269 3,243 91 572 134 2,767 144 3,006
Recreational 51 125 49 123 50 130 50 131 Average No. of
Lockages 21,810 21,730 21,770 21,770
Resulting Averages of Total Transit Time All vessels
818 A
586 C
(-28%) 713 B
(-13%) 729 B
(-11%)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 43
Center for Business and Industrial Studies
Center for Transportation
Studies
Adding local queue balancing constraints for flexibility hurt system-wide performance in our experiments
Medians and 95th Percentiles of Waiting Times For Queue Balancing Variants with Year 2000 Commercial Traffic Plus 20%
FIFORECPRIO (bench mark)
BALQLCLX-5 (1yr - no priority
shift)
BALQLCLX-5 480 min.
BALQLCLX-5 360 min.
BALQLCLX-4 360 min
Vessel-tow Configuration
Median 95th Median 95th Median 95th Median 95th Median 95th Double 334 3,772 148 6,732 579 5,593 522 5,145 524 5,599
Jackknife 378 3,732 131 628 339 5,012 359 4,834 364 5,335 Knockout 324 3,322 117 546 242 4,521 258 4,399 256 4,726
Single 296 3,473 115 553 228 4,649 237 4,511 238 4,887 Single ex barge 275 3,062 101 549 199 4,426 203 4,180 210 4,529
Recreational 51 125 50 124 52 131 52 131 52 132
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 44
Center for Business and Industrial Studies
Center for Transportation
Studies
Using “helper boats” to speed lockages can greatly reduce congestion for moderate increases in traffic (with some capital investment required)
UMR Locks 20 through 25 - Mean Lock Transit Times in Minutes
0
100
200
300
400
500
600
700
800
900
1000
-20% -15% -10% -5% +0% +5% +10% +15% +20% +25% +30%
Mean Annual Total Number of Lockages as a Percentage of 2000 Lockages
Me
an
Tra
ns
it T
ime
pe
r L
oc
k in
Min
ute
s
FIFORECPRIO FLT FAST 1200 LOCKS SLOW 1200 LOCKS Historic Data 1992-2006 Helper Boats
Helper Boats
FIFORECPRIO FLT
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 45
Center for Business and Industrial Studies
Center for Transportation
Studies
New locks eliminate congestion under all traffic scenarios but at great capital cost
UMR Locks 20 through 25 - Mean Lock Transit Times in Minutes
0
100
200
300
400
500
600
700
800
900
1000
-20% -15% -10% -5% +0% +5% +10% +15% +20% +25% +30%
Mean Annual Total Number of Lockages as a Percentage of 2000 Lockages
Me
an
Tra
ns
it T
ime
pe
r L
oc
k in
Min
ute
s
FIFORECPRIO FLT FAST 1200 LOCKS SLOW 1200 LOCKS Historic Data 1992-2006
New 1200’ “Fast” Locks
New 1200’ “Slow” Locks
FIFORECPRIO FLT
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 46
Center for Business and Industrial Studies
Center for Transportation
Studies
Our Findings
The IP Model helped us develop scheduling rules for further testing via stochastic simulation.
Benefits (or costs) differ among classes of user.
The FLTX rule promotes immediate efficiency while imposing fairness, and results in improved system-wide performance under a range of priority-shifting intervals.
Adding constraints upon FLTX to keep local queues balanced harmed system-wide performance.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 47
Center for Business and Industrial Studies
Center for Transportation
Studies
Findings (cont.)
Stochastic phenomena (variations in traffic intensity, traffic mix, activity times and random arrivals) mute the benefits of scheduling strategies inferred from deterministic optimizing models for clearing queues that exist at a point in time
Self-adapting behavior in extreme conditions eliminates (and hides) some of the stochastic problem – making it difficult to isolate the true benefits from scheduling solutions that may be implemented.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 48
Center for Business and Industrial Studies
Center for Transportation
Studies
Strategic considerations for eliminating seasonal congestion
Fixed and variable costs under alternative remedies vary greatly and are incurred by stakeholders (public and private) in different proportions
Incidental economic effects differ
Environmental effects differ
Relative advantages depend heavily on future traffic scenarios
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 49
Center for Business and Industrial Studies
Center for Transportation
Studies
Political and economic issues
Infrastructure investments must be justified by the U.S. Army Corps of Engineers on the basis of net national economic benefit- How to estimate benefits from greater capacity
Market Benefits: Reduction in expected queue time with or without traffic displacement
Non-market Benefits: Carbon footprint for water transportation versus rail and highway
External Benefits: Congestion relief on railways and highways
Revenue sources for infrastructure improvements- Federal earmarks from general revenues- Existing fuel tax specific to the industry - Newly proposed lockage fees (risk of displacement as with the Chunnel if
competing modes adjust rates to retain or capture business) Containing Federal budgetary deficits versus economic stimulus Ethanol subsidies (corn for domestic bio-fuel instead of export for
food)
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 50
Center for Business and Industrial Studies
Center for Transportation
Studies
Future research
Exploring effects of alternative congestion charging mechanisms and priority booking fees
Developing other decision rules with consideration of conditions at adjacent locks
Investigating consequences of traffic restrictions during new construction
Extending the IP model to clearing a system of three locks to see if different rules emerge for clearing the middle lock versus the locks at both ends.- System-wide measures of queue balance- System-wide measures of dispersion in vessel mix at locks.
Integration of IP and simulation in various degrees.
Fraunhofer Institute, Dortmund, Germany, May 16, 2008Fraunhofer Institute, Dortmund, Germany, May 16, 2008 51
Center for Business and Industrial Studies
Center for Transportation
Studies
Future research (cont.)
Improving IP heuristics- Recognize that vessels within a class will not be
reordered from upstream or downstream arrival sequence, as doing so will not generate efficiencies
- Possibly restricting attention to the first x lockages because new arrivals will change the problem.
Solving the IP over a range of anticipated future states of the system (and looking for commonalities in immediate action inferred from the different solutions).
Using time-discounted objectives in the IP solution (unfortunately adding additional nonlinearity).
Developing alternative metrics for flexibility that may be considered in setting IP boundary conditions or in the decision rules for stochastic analysis
top related