PPT10 - Planning and Managing Long Haul Freight Transportation

Supply Chain Logistics
Topic :
Planning and Managing Short-Haul Freight Transportation
and
Planning and Managing Long Freight Transportation

Single Flow Routing and
Multiple Vehicle Roundtrip
Routing
Chapter 8
Materials are taken from Prof. Marc Goetschalckx Course Note with some modification

Agenda
• Vehicle Routing Problem
– Problem definition
– Variants
– Conclusions
Supply Chain Modeling: Logistics, Spring 2013 ©KGA. All Rights Reserved

Vehicle Routing Problem
Classification
Three characteristics:
One or more vehicles?
Vehicle status before and after
the transportation services:
Yes or No?
Origin-destination or flow
routing
shipper point of view
Static versus Dynamic

Vehicle Routing Problem
Classification
# Vehicles Single Multiple
Vehicle Status
Request-Only (flow) Single Flow
(SPP)
Multiple Flows
(NFP)
Prior & Post (vehicle) Single Vehicle
Roundtrip
(TSP)
Multiple Vehicles
Roundtrip
(VRP)

VRP Definition
• Goal is to efficiently use a fleet of vehicles
• Given a number of stops to pick up or deliver passenger or goods
• Under a variety of constraints
– Vehicle capacity (volume, weight, both)
– Delivery time restrictions
– Precedence constraints (e.g. vehicle routing problem with
backhauling, dial-a-ride problem)

Vehicle Routing Decisions
• Tactical to operational decisions
• Decisions by decreasing level or time frame
– Number of vehicles (fleet planning)
– Which customers served by which vehicle (allocation
or clustering)
– Sequence of stops for each vehicle (sequencing)

Vehicle Routing Variants (1/4)
• Traveling salesman problem (TSP)
– Simplest VRP
– Single vehicle, no capacity constraint
– 2nd stage in cluster-first, route second algorithm
• Vehicle routing problem (VRP)
– Known customer locations and demand
– A fleet of vehicles of identical capacity
– Start and return to a single depot

• Linehaul-backhaul (VRPB)
– Goods to be delivered and goods to be picked up
– Deliver first before pick up
– Saving on deadhead
• Vehicle routing with time windows (VRPTW)
– Allowable time interval
– Hard (arrive early, wait) or soft (ok with certain penalty)

• Inventory routing (IRP)
– Minimize transportation and holding inventory costs while
avoiding stock out and observing storage capacity limitation

• Mixed pickup and delivery
– Delivery and pickup can be intermixed
– Soft-drink and beer delivery
– Dial-a-ride problem

Vehicle Routing Algorithms
Classification
By required inputs By methodology
Basic data:
• Customer (total and demand)
• Vehicle (total and capacity)
• Facility (location)
VRP based
• Sweep, Savings, Exchange, Nearest
Neighbor
Route generating
• Input: basic problem data
• Large variety
• Tightly capacitated
Optimization based
• GAP, SPP, k-trees
• Simulation annealing, Tabu search
Route selecting
• Input: a set of feasible solutions
• Set partitioning algorithm (SPP)
• Complex costs and constraints
Artificial intelligence based
• Genetic search
Generalized Assignment Problem (GAP) Ad hoc heuristics or optimization based

• Single depot
• N customers (𝑥𝑖, 𝑦𝑖, 𝐷𝑖) – known location and demand
• K vehicles (𝑐𝑎𝑝𝑘) – known and equal size
• Each vehicle starts and returns to the depot
• Single sourcing service constraint
• Objective: minimize travel cost
• Cost is proportional to the total distance traveled
• Travel distance norm: triangle inequality and symmetric
(Euclidean)
Standard Vehicle Routing (VRP)

𝐷𝑖 quantity to be delivered to customer 𝑖 (𝑖 = 1, … , 𝑁)
𝑐𝑎𝑝𝑘 capacity of vehicle 𝑘 (𝑘 = 1, … 𝐾)
𝑐𝑖𝑗𝑘 cost of vehicle 𝑘 to travel from cust.𝑖 to cust. 𝑗
𝑣𝑐𝑘 cost of using vehicle 𝑘 in the solution
𝑥𝑖, 𝑦𝑖 location coordinates of customer 𝑖
𝑧𝑖𝑘 =
1, if customer 𝑖 is assigned to vehicle 𝑘
0, otherwise
𝑧0𝑘, 𝑧𝑘 =
1, if vehicle 𝑘 is used
0, otherwise
Standard Vehicle Routing (VRP)

VRP Example
3 vehicles with capacity of 120 each.
Each vehicle one route.
Label X-coord. Y-coord. Quantit
y
C1 2440 1794 23
C2 2844 2820 28
C3 1434 3669 32
C4 372 1745 37
C5 1592 2077 31
C6 3257 873 57
C7 663 2877 46
C8 929 453 41
D1 1164 1083 0
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c
2
c
3
c
4
c
5
c
6
c
7
c
8
Depot
c
1

VRP Example
Distances
C1 C2 C3 C4 C5 C6 C7 C8 D1
C1 0 1,103 2,128 2,069 894 1,231 2,081 2,020 1,461
C2 1,103 0 1,646 2,696 1,456 1,990 2,182 3,045 2,417
C3 2,128 1,646 0 2,198 1,600 3,338 1,105 3,255 2,600
C4 2,069 2,696 2,198 0 1,264 3,014 1,169 1,407 1,032
C5 894 1,456 1,600 1,264 0 2,055 1,226 1,754 1,082
C6 1,231 1,990 3,338 3,014 2,055 0 3,278 2,366 2,104
C7 2,081 2,182 1,105 1,169 1,226 3,278 0 2,439 1,863
C8 2,020 3,045 3,255 1,407 1,754 2,366 2,439 0 672
D1 1,461 2,417 2,600 1,032 1,082 2,104 1,863 672 0

Homogeneous Fleet
• Maximum number of routes usually parameter (for example max =
3)
• Maximum number of routes
𝑖=1
𝑁
𝑞𝑖
𝐶𝑎𝑝
=
295
120
= 3
Heterogeneous Fleet
• Individual cost and capacity for each vehicle k
– 𝑐𝑘 = 1 for vehicle count minimization
• GAP since demand may be not equal to 1
– Requires MIP solver rather than LP solver
VRP Example
Maximum Number of Routes

𝑥𝑣𝑐 =
1, if vehicle 𝑣 is used to serve city 𝑐
0, otherwise
min 𝑥10 + 𝑥20 + 𝑥30
s.t. 23𝑥11 + 28𝑥12 + 32𝑥13 + 37𝑥14 + 31𝑥15 + 57𝑥16 + 46𝑥17 + 41𝑥18 ≤ 120𝑥10
23𝑥21 + 28𝑥22 + 32𝑥23 + 37𝑥24 + 31𝑥25 + 57𝑥26 + 46𝑥27 + 41𝑥28 ≤ 120𝑥20
23𝑥31 + 28𝑥32 + 32𝑥33 + 37𝑥34 + 31𝑥35 + 57𝑥36 + 46𝑥37 + 41𝑥38 ≤ 120𝑥30
𝑥11 + 𝑥21 + 𝑥31 = 1
𝑥12 + 𝑥22 + 𝑥32 = 1
𝑥13 + 𝑥23 + 𝑥33 = 1
𝑥14 + 𝑥24 + 𝑥34 = 1
𝑥15 + 𝑥25 + 𝑥35 = 1
𝑥16 + 𝑥26 + 𝑥36 = 1
𝑥17 + 𝑥27 + 𝑥37 = 1
𝑥18 + 𝑥28 + 𝑥38 = 1
𝑥𝑣𝑐 = 0,1 , ∀𝑣, 𝑐
VRP Example
Minimum Number of Routes:
Heterogeneous Fleet GAP
𝑧𝐿𝑃 = 2.458
𝑧𝐼𝑃 = 3 with loads (120, 91, 84)

• Vehicle utilization
𝜌 =
𝑖 𝐷𝑖
𝑁. 𝑐𝑎𝑝
=
295
360
= 82%
• Average load and slack
𝑞 =
𝑖 𝐷𝑖
𝑁
=
295
3
= 98.33
𝑠 = 𝑐𝑎𝑝 − 𝑞 = 120 − 98.33 = 21.67 < 36.875 = 𝐷
VRP Example
Route Statistics
What can you conclude?
Perform route improvement algorithm

VRP Route Generating
Algorithms
• Route construction
– Savings, nearest neighbor
• Route improvement
– Exchange improvements (2-opt, 3-opt, or chain-opt),
move and swap
• Two phase algorithms
– Cluster first, route second
• Sweep A, GAP (Fisher and Jaikumar)
– Route first, cluster second
• Sweep B, Giant Tour

Nearest Neighbor
1. Start a new route with the depot
2. Find nearest unvisited customer
3. If customer demand is less than remaining vehicle
capacity; append customer, otherwise go to 5
4. If all customers are visited, stop
5. If maximum number vehicles is used, stop, else go to 1

VRP Example
Nearest Neighbor
Index Anchor
Point
Append
Point
Append
Distance
Vehicle
Quantity
1 D1 C8 672 41
2 C8 C4 1,407 78
3 D1 C5 1,082 31
4 C5 C1 894 54
5 C1 C2 1,103 82
6 C2 C3 1,646 114
7 D1 C7 1,863 46
8 C7 C6 3,278 103
TOTAL 17,681 295

Facility
Route
0 1 2 3 4 5 6 Qty Dist.
1 D1 C8 C4 D1 78 3,111
2 D1 C5 C1 C2 C3 D1 114 7,325
3 D1 C7 C6 D1 103 7,245
TOTAL 295 17,681
VRP Example
Nearest Neighbor
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c2
c3
c4
c5
c6
c7
c8
Depot
c1

• Maximum number of routes = K
• Serial variant
– One route at-a-time
– Simpler implementation
• Parallel variant
– No more than K routes at-a-time
– In each step, extend current partial routes, create new
partial routes, combine two partial routes
– More complex programming
VRP Example
Clarke and Wright Savings

Savings Illustration
i j
h
D
Added
Deleted
Unchanged
0 = depot index
Serial Savings Parallel Savings
i j
h
D
k
m
m
𝑠𝑖ℎ = 𝑑𝑖0 + 𝑑𝑜ℎ − 𝑑𝑖ℎ
𝑠𝑖𝑗 = 𝑑𝑖0 + 𝑑𝑜𝑗 − 𝑑𝑖𝑗
𝑠𝑘𝑚 = 𝑑𝑘0 + 𝑑𝑜𝑚 − 𝑑𝑘𝑚

• Compute savings for every feasible pair of points (see table)
𝐷1 + 𝐷2 = 23 + 28 = 51 < 120
𝑠12 = 𝑑10 + 𝑑02 − 𝑑12 = 2775
VRP Example
Clarke and Wright Savings: Initial Savings (common)
O D C1 C2 C3 C4 C5 C6 C7 C8 Qty.
C1 0 2775 1933 424 1649 2334 1243 113 23
C2 2775 0 3371 753 2043 2531 2098 44 28
C3 1933 3371 0 1434 2082 1366 3358 17 32
C4 424 753 1434 0 850 122 1726 297 37
C5 1649 2043 2082 850 0 1131 1719 0 31
C6 2334 2531 1366 122 1131 0 689 410 57
C7 1243 2098 3358 1726 1719 689 0 96 46
C8 113 44 17 297 0 410 96 0 41
Qty. 23 28 32 37 31 57 46 41 0
Serial Savings

1. Eliminate origin row (C2) and destination column (C3)
2. Append destination label to origin column label (C2:C3)
3. Prepend origin label to destination row label (C2:C3)
4. Eliminate cell at origin column and destination row
5. Update quantities for origin column and destination row (28 + 32 =
60)
VRP Example
Clarke and Wright Savings: Update Savings Matrix
Serial Savings

VRP Example
Clarke and Wright Savings: 2nd Iteration
C1 C2:C3 C4 C5 C6 C7 C8 Qty.
C1 0 2775 424 1649 2334 1243 113 23
C2:C3 1933 1434 2082 1366 3358 17 60
C4 424 753 0 850 122 1726 297 37
C5 1649 2043 850 0 1131 1719 0 31
C6 2334 2531 122 1131 0 689 410 57
C7 1243 2098 1726 1719 689 0 96 46
C8 113 44 297 0 410 96 0 41
Qty. 23 60 37 31 57 46 41 0
Serial Savings

VRP Example
Clarke and Wright Savings: 3rd Iteration (continuation)
C1 C2:C3:C7 C4 C5 C6 C8 Qty.
C1 0 2775 424 1649 2334 113 23
C4 424 753 0 850 122 297 37
C5 1649 2043 850 0 1131 0 31
C6 2334 2531 122 1131 0 410 57
C2:C3:C7 1243 1726 1719 689 96 106
C8 113 44 297 0 410 0 41
Qty. 23 106 37 31 57 41 0
Serial Savings
No feasible selections left, start new route

VRP Example
Clarke and Wright Savings: 3rd Iteration (restart)
C1 C4 C5 C6 C8 Qty.
C1 0 424 1649 2334 113 23
C4 424 0 850 122 297 37
C5 1649 850 0 1131 0 31
C6 2334 122 1131 0 410 57
C8 113 297 0 410 0 41
Qty. 23 37 31 57 41 0
Serial Savings

VRP Example
Clarke and Wright Savings: 4th Iteration
C1:C6 C4 C5 C8 Qty.
C4 424 0 850 297 37
C5 1649 850 0 0 31
C1:C6 122 1131 410 80
C8 113 297 0 0 41
Qty. 80 37 31 41 0
Serial Savings
C5 – (C1:C6)

VRP Example
Clarke and Wright Savings: 5th Iteration
C4 C5:C1:C6 C8 Qty.
C4 0 850 297 37
C5:C1:C6 122 1131 410 111
C8 297 0 0 41
Qty. 37 111 41 0
Serial Savings
No feasible selections left, start new route
C4 C8 Qty.
C4 0 297 37
C8 297 0 41
Qty. 37 41 0

VRP Example
Clarke and Wright Savings
Serial Savings
Shortcut Customer Q Length
Step Points Savings Length 1 2 3 4 5 6 7 8
1 C2:C3 3371 6663 D1 C2 C3 D1
2 C2:C3:C7 3358 7031 D1 C2 C3 C7 D1 106 7031
3 C1:C6 2334 4796 D1 C1 C6 D1
4 C5:C1:C6 1694 5311 D1 C5 C1 C6 D1 111 5311
5 C4:C8 297 3111 D1 C4 C8 D1 78 3111
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c2
c3
c4
c5
c6
c7
c8
Depot
c1

Clark-Wright Savings Facts
• The points that offer the greatest savings when combined
on the same route are those that are farthest from the
depot and that are closest to each other

VRP
Sweep Algorithm
• Cluster first, route second sweep (variant A)
– Ray determines clusters
– TSP construction routines to route each cluster
• Route first sweep, cluster second (variant B)
– Ray determine routes
– TSP corresponding to initial route

Label Quantity Total
C1 23 23
C2 28 51
C5 31 82
C3 32 114
C7 46 46
C4 37 83
C8 41 41
C6 57 98
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c2
c3
c4
c5
c6
c7
c8
Depot
c1
VRP Example
Cluster First, Route Second Sweep (East)
Points in clusters routed with TSP algorithm
• Cluster 1: convex hull + cheapest insertion
• Cluster 2 and 3: trivial triangles
Total Distance:
6892 + 4064 + 5142 = 16,098

Label Quantity Total
C1 23 23
C2 28 51
C5 31 82
C3 32 114
C7 46 46
C4 37 83
C8 41 41
C6 57 98
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c2
c3
c4
c5
c6
c7
c8
Depot
c1
VRP Example
Route First Sweep, Cluster Second
Total Distance:
8220 + 4064 + 5142 = 17,426

Example of Sweep Method
A trucking company has 10,000-unit vans for merchandise pickup to be
consolidated into larger loads for moving over long distances. A day’s
pickups are shown in the figure below. How should the routes be designed
for minimal total travel distance?
Geographical
region
Depot
1,000
2,000
3,000
2,000
4,000
2,000
3,000 3,000
1,000
2,000
2,000
2,000
Pickup
points

Example of Sweep Method
Sweep direction
is arbitrary
Depot
1,000
2,000
3,000
2,000
4,000
2,000
3,000 3,000
1,000
2,000
2,000
2,000
Route #1
10,000 units
Route #2
9,000 units
Route #3
8,000 units

VRP Example
Giant Tour
• Creates shortest length TSP of all customers but ignores demand
requirements during the routing phase
• Example: using Convex Hull + priciest insertion heuristics to get the
following tour

VRP Example
Giant Tour
Customer Q Length
1 2 3 4 5 6 7 8
D1 C5 C1 C6 D1 111 5311
D1 C2 C3 C7 D1 106 7031
D1 C4 C8 D1 78 3111

Improvement Algorithms
• Intra-route improvements (TSP)
– Always feasible for VRP
– 2-, 3-, and chain exchange
• Inter-route improvements
– Test and make only feasible exchanges
– Move (one point to another route – steepest descent)
– Swap (exchange two points between routes –
steepest descent)
– Cyclic (exchange a cycle of points between
successive routes), computationally very demanding

Inter-Route Improvement
1
i
2
1’
2’
D
Unchanged
Deleted
Added
MOVE
i
1
2’
2
j
D
SWAP
1’

Inter-Route Improvement
3-cycle Improvement
i
3
2’
2
j
D
3’
k
1
1’
Unchanged
Deleted
Added

Swap Example
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500
c2
c3
c4
c5
c6
c7
c8
Depot
c1
C1 C2 C3 C4 C5 C6 C7 C8 D1
C1 0 1,103 2,128 2,069 894 1,231 2,081 2,020 1,461
C2 1,103 0 1,646 2,696 1,456 1,990 2,182 3,045 2,417
C3 2,128 1,646 0 2,198 1,600 3,338 1,105 3,255 2,600
C4 2,069 2,696 2,198 0 1,264 3,014 1,169 1,407 1,032
C5 894 1,456 1,600 1,264 0 2,055 1,226 1,754 1,082
C6 1,231 1,990 3,338 3,014 2,055 0 3,278 2,366 2,104
C7 2,081 2,182 1,105 1,169 1,226 3,278 0 2,439 1,863
C8 2,020 3,045 3,255 1,407 1,754 2,366 2,439 0 672
D1 1,461 2,417 2,600 1,032 1,082 2,104 1,863 672 0
Swap the overlapping points (2 and 5)
• Remove points by deleting links:
D-C5-C1 and D-C2-C3
1082 + 894 – 1461 = 515
2417 + 1646 – 2600 = 1463
• Add the points using insertion methods
and add links:
D-C5-C3 and C1-C2-C6
1082 + 1600 – 2600 = 82
1103 + 1990 – 1231 = 1862

Common VRP Extensions
• Linehaul-Backhaul (VRPB)
– Customers before suppliers on a route
• VRP with time windows (VRPTW)
– Additional feasible time interval for visit
• VRP with stochastic demands (SVRP)
– Fixed routes for uncertain demand
• Inventory-Routing problem
– When to visit a customer, how much to deliver

Rear access truck
Side access truck

VRPB Problem Definition
• Single depot
• 𝑁𝐿 customers (𝑥𝑖, 𝑦𝑖, 𝐷𝑖) and 𝑁𝐵customers (𝑥𝑖, 𝑦𝑖, 𝑆𝑖)
• K equal size vehicles (𝑐𝑎𝑝𝑗)
• Rear loaded vehicles (all customers before any supplier)
• Minimize total travel distance
• Travel distance norm

VRPB Route Illustration

Algorithms Adapted from VRP to
VRPB
• Nearest Neighbor
• Sweep Variant B
• Clark and Wright Savings
• Generalized Assignment (seed points and seed rays)
• Set Partitioning

VRP Example
Label X Y Qty. Shape Type
C1 190 701 12 Circle Cust.
S1 137 119 23 Triangle Supplier
D1 362 465 0 Square Depot
4 trucks with capacity 75 each.
What can you conclude about its
utilization level?
0
200
400
600
800
0 200 400 600 800 1000
c1
c2
c3
c4
c5
s2
s3
depot
s1

Nearest Neighbor for VRPB
• Start new route at depot with linehaul
• Go to nearest unvisited customer until vehicle capacity
would be violated
• Cross over to backhauling
• Go to the nearest unvisited supplier until vehicle capacity
would be violated
• Return to depot and go to step 1

VRP Example
Nearest Neighbor

Sweep for VRPB
• Put ray at starting angle and rotate
• Build two partial routes until next facility of either type
would violate truck capacity
– Next visited customer appends to the tail of the
linehaul route
– Next visited supplier appends to the head of the
backhaul route
– Close route and go to step 2

VRP Example
Sweep B

Clark and Wright Savings for VRPB
• Principle is to delay crossover arc
• Adjust savings for crossover arc
𝑆 = max
𝑖,𝑗
𝑑0𝑗 + 𝑑𝑖0 − 𝑑𝑖𝑗
𝑠𝑖𝑗 =
𝑑0𝑗 + 𝑑𝑖0 − 𝑑𝑖𝑗 − 𝛼𝑆 if 𝑖 ∈ 𝑪, 𝑗 ∈ 𝑺
𝑑0𝑗 + 𝑑𝑖0 − 𝑑𝑖𝑗 if 𝑖, 𝑗 ∈ 𝑪 or 𝑖, 𝑗 ∈ 𝑺
Or
𝑠𝑖𝑗 =
(1 − 𝛼) ⋅ (𝑑0𝑗+𝑑𝑖0 − 𝑑𝑖𝑗) if 𝑖 ∈ 𝑪, 𝑗 ∈ 𝑺
𝑑0𝑗 + 𝑑𝑖0 − 𝑑𝑖𝑗 if 𝑖, 𝑗 ∈ 𝑪 or 𝑖, 𝑗 ∈ 𝑺
• Crossover savings penalty = 0.25
𝑆𝑖𝑗
𝑎𝑑𝑗
= 𝑆𝑖𝑗
𝑜𝑟𝑖𝑔
⋅ 1 − 0.25

C1 C2 C3 C4 C5 S1 S2 S3
C1 0 125.10 470.73 7.76 36.78 90.26 210.42 9.32
C2 125.10 0 69.08 43.23 485.35 170.23
C3 470.73 0 3.50 28.64 445.46 3.14
C4 7.76 0 206.59 212.29 217.51 398.73
C5 36.78 69.08 3.50 206.59 0 232.97 9.52 224.50
S1 0 460.54
S2 0
S3 460.54 0
VRP Example
Clark-Wright Savings: Initial Savings
Use crossover savings penalty = 0.25
Infeasible savings are not computed or shown

C1 C2:S2 C3 C4 C5 S1 S3 Line Q Back Q
C1 0 125.10 470.73 7.76 36.78 90.26 9.32 12
C3 470.73 0 3.50 28.64 3.14 49
C4 7.76 0 206.59 212.29 398.73 51
C5 36.78 69.08 3.50 206.59 0 232.97 224.50 18
S1 0 460.54 23
C2:S2 29 66
S3 460.54 0 46
VRP Example
Clark-Wright Savings: First Iteration

C1:C2:S2 C3 C4 C5 S1 S3 Line Q Back Q
C3 470.73 0 3.50 28.64 3.14 49
C4 7.76 0 206.59 212.29 398.73 51
C5 36.78 3.50 206.59 0 232.97 224.50 18
S1 0 460.54 23
C1:C2:S2 41 66
S3 460.54 0 46
VRP Example
Clark-Wright Savings: Second Iteration

C3 C4 C5:C1:C2:S2 S1 S3 Line Q Back Q
C3 0 3.50 28.64 3.14 49
C4 0 206.59 212.29 398.73 51
S1 0 460.54 23
C5:C1:C2:S2 59 66
S3 460.54 0 46
VRP Example
Clark-Wright Savings: Fourth & Fifth
Iteration
C3 C4 S1 S3 Line Q Back Q
C3 0 28.64 3.14 49
C4 0 212.29 398.73 51
S1 0 460.54 23
S3 460.54 0 46

VRP Example
Clark-Wright Savings: Sixth & Seventh
Iteration
C3 C4 S1:S3 Line Q Back Q
C3 0 28.64 49
C4 0 212.29 51
S1:S3 0 69
C3 C4:S1:S3 Line Q Back Q
C3 0 49
C4:S1:S3 0 51 69

PPT10 - Planning and Managing Long Haul Freight Transportation

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to PPT10 - Planning and Managing Long Haul Freight Transportation

Similar to PPT10 - Planning and Managing Long Haul Freight Transportation (20)

More from Binus Online Learning

More from Binus Online Learning (20)

Recently uploaded

Recently uploaded (20)

PPT10 - Planning and Managing Long Haul Freight Transportation