Supply chain logistics : vehicle routing and scheduling
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Transcript of Supply chain logistics : vehicle routing and scheduling
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Vehicle Routing and Scheduling
Martin SavelsberghThe Logistics InstituteGeorgia Institute of Technology
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Vehicle Routing and Scheduling
Part I:Basic Models and Algorithms
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Introduction
Freight routingrouting of shipments
Service routingdispatching of repair technicians
Passenger routingtransportation of elderly
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Freight Routing (LTL)
pickup and delivery to and from end-of-line terminalshuttle from end-of-line terminal to regional distribution centertransportation between distribution centers
railsleeper teamssingle driver
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LTL Linehaul Network
EOLsDCs
Customers
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Origin-Destination Route
OriginEOL
DC
Destination
Linehaul scheduling
Pickup and delivery
Shuttle scheduling
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Routing and Scheduling
Routing and scheduling does not follow a single “one-size-fits-all” formula. Routing and scheduling software must usually be customized to reflect the operating environment and the customer needs and characteristics
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Models
Traveling Salesman Problem (TSP)Vehicle Routing Problem (VRP)Vehicle Routing Problem with Time Windows (VRPTW)Pickup and Delivery Problem with Time Windows (PDPTW)
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Traveling Salesman Problem
In the TSP the objective is to find the shortest tour through a set of cities, visiting each city exactly once and returning to the starting city.Type of decisions:
routing
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Traveling Salesman Problem
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Vehicle Routing Problem
In the VRP a number of vehicles located at a central depot has to serve a set of geographically dispersed customers. Each vehicle has a given capacity and each customer has a given demand. The objective is to minimize the total distance traveled.Type of decisions:
assigningrouting
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Vehicle Routing Problem
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Vehicle Routing Problem with Time Windows
In the VRPTW a number of vehicles is located at a central depot and has to serve a set of geographically dispersed customers. Each vehicle has a given capacity. Each customer has a given demand and has to be served within a given time window.Type of decisions:
assigningroutingscheduling
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Vehicle Routing Problem with Time Windows
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Pickup and Delivery Problem with Time Windows
In the PDPTW a number of vehicles has to serve a number of transportation requests. Each vehicle has a given capacity. Each transportation request specifies the size of the load to be transported, the location where it is to be picked up plus a pickup time window, and the location where it is to be delivered plus a delivery time window.Type of decisions:
assigningroutingscheduling
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Pickup and Delivery Problem with Time Windows
Delivery
Pickup
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Pickup and Delivery Problem with Time Windows
Delivery
Pickup
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Routing and Scheduling
Objectivesminimize vehiclesminimize milesminimize laborsatisfy service requirementsmaximize ordersmaximize volume delivered per mile
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Routing and Scheduling
Practical considerationsSingle vs. multiple depotsVehicle capacity
homogenous vs. hetrogenousvolume vs. weight
Driver availabilityFixed vs. variable start timesDoT regulations (10/1, 15/1, 70/8)
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Routing and Scheduling
Practical considerations (cont.)Delivery windows
hard vs. softsingle vs. multipleperiodic schedules
Service requirementsMaximum ride timeMaximum wait time
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Routing and Scheduling
Practical considerations (cont.)Fixed and variable delivery timesFixed vs. variable regions/route
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Recent Variants
Dynamic routing and scheduling problemsMore and more important due to availability of GPS and wireless communicationInformation available to design a set of routes and schedules is revealed dynamically to the decision maker
Order information (e.g., pickups)Vehicle status information (e.g., delays)Exception handling (e.g., vehicle breakdown)
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Recent Variants
Stochastic routing and scheduling problems
Size of demandTravel times
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Algorithms
Construction algorithmsImprovement algorithms
Set covering based algorithms
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Construction Heuristics
Savings heuristicInsertion heuristics
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Savings
Initial
i j
Intermediate Final
Savings s(i,j) = c(i,0) + c(0,j) – c(i,j)
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Savings Heuristic (Parallel)
Step 1. Compute savings s(i,j) for all pairs of customers. Sort savings. Create out-and-back routes for all customers.Step 2. Starting from the top of the savings list, determine whether there exist two routes, one containing (i,0) and the other containing (0,j). If so, merge the routes if the combined demand is less than the vehicle capacity.
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Savings Heuristics (Sequential)
Step 1. Compute savings s(i,j) for all pairs of customers. Sort savings. Create out-and-back routes for all customers.Step 2. Consider route (0,i,…,j,0) and determine the best savings s(k,i) and s(j,l) with routes containing (k,0) and (0,l). Implement the best of the two. If no more savings exist for this route move to the next.
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Savings Heuristic -Enhancement
Route shape parameters(i,j) = c(i,0) + c(0,j) - λ c(i,j)
The larger λ, the more emphasis is placed on the distance between customers being connected
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Insertion
Initial
i
j
k
Intermediate Final
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Insertion HeuristicsStart with a set of
unrouted stops
Select an unrouted stop
Are there any unrouted stops?
Insert selected stop in current set of routes
Yes
DoneNo
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Nearest addition
Selection:If partial tour T does not include all cities, find cities k and j, j on the tour and k not, for which c(j,k) is minimized.
Insertion:Let {i,j} be either one of the two edges involving j in T, and replace it by {i,k} and {k,j} to obtain a new tour including k.
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Nearest Insertion
Selection:If partial tour T does not include all cities, find cities k and j, j on the tour and k not, for which c(j,k) is minimized.
Insertion:Let {i,j} be the edge of T which minimizes c(i,k) + c(k,j) - c(i,j), and replace it by {i,k} and {k,j} to obtain a new tour including k.
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Farthest Insertion
Selection:If partial tour T does not include all cities, find cities k and j, j on the tour and k not, for which c(j,k) is maximized.
Insertion:Let {i,j} be the edge of T which minimizes c(i,k) + c(k,j) - c(i,j), and replace it by {i,k} and {k,j} to obtain a new tour including k.
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Cheapest Insertion
Selection:If partial tour T does not include all cities, find for each k not on T the edge {i,j} of T which minimizes c(T,k) = c(i,k) + c(k,j) -c(i,j). Select city k for which c(T,k) is minimized.
Insertion:Let {i,j} be the edge of T for which c(T,k) is minimized, and replace it by {i,k} and {k,j} to obtain a new tour including k.
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Worst case results
Nearest addition: 2Nearest insertion: 2Cheapest insertion: 2
Farthest insertion: > 2.43 (Euclidean)> 6.5 (Triangle inequality)
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Implementation
Priority Queueinsert(value, key)getTop(value, key)setTop(value, key)
k-d TreedeletePt(point)nearest(point)
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ImplementationTree->deletePt(StartPt)NNOut[StartPt] := Tree->nearest(StartPt)PQ->insert(Dist(StartPt, NNOut(StartPt)), StartPt)loop n-1 time
loopPQ->getTop(ThisDist, x)y := NNOut[x]If y not in tour, then breakNNOut[x] = Tree->nearest(x)PQ->setTop(Dist(x, NNOut[x]), x)
Add point y to tour; x is nearest neighbor in tourTree->deletePt(y)NNOut[y] = Tree->nearest(y)PQ->insert(Dist(y, NNOut[y]), y)
Delayed update
Find nearest point
Update
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Improvement Algorithms
Start with a feasible solution x
x
y
z
Is there an improving neighbor
y in N(x)?
Define neighborhood N(x).
Replace x by y
Yes
N(x)x is locally optimal
No
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2-change
O(n2) possibilities
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3-change
O(n3) possibilities
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1-Relocate
O(n2) possibilities
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2-Relocate
O(n2) possibilities
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Swap
O(n2) possibilities
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GENI
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Vehicle Routing and Scheduling
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Vehicle Routing and Scheduling
1-relocate
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B-cyclic k-transfer
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Set covering based algorithms
Assignment decisions are often the most importantAssignment decisions are often the most difficult
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Vehicle routing and scheduling
Possible route
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Set partitioning formulation
c1 c2 c3 c4 …Cust 1 1 1 0 1 … = 1Cust 2 1 0 1 0 … = 1Cust 3 0 1 1 0 … = 1…Cust n 0 1 0 0 … = 1
y/n y/n y/n y/n …
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Set covering based algorithms
Advantagevery flexible
heuristics for route generationcomplicating constraints in route generation
Disadvantagesmall to medium size instances