6 Managing freight transport - Wiley Managing freight transport 6.1 Introduction 6.2 Freight trafc...

6 Managing freight transport6.1 Introduction6.2 Freight traffic assignment problems6.3 Service network design problems6.4 Vehicle allocation problems6.5 A dynamic driver assignment problem6.6 Fleet composition6.7 Shipment consolidation6.8 Vehicle routing problems6.9 Real-time vehicle routing problems

6.10 Integrated location and routing problems6.11 Vendor-managed inventory routing6.12 Case study: Air network design at Intexpress6.13 Case study: Meter reader routing and scheduling at Socal6.14 Case study: Dynamic vehicle-dispatching problem with

pickups and deliveries at eCourier6.15 Questions and problems

G. Ghiani, G. Laporte, R. Musmanno Introduction to Logistics System Management © John Wiley & Sons, Ltd 1 / 13

6 Managing freight transport Case study: Dynamic vehicle-dispatching at eCourier

eCourier- same-day courier company, operating in the Greater

London area;- pickups and deliveries of parcels (documents, packages,

pallets etc.) by means of a fleet of couriers, who usebicycles, motorcycles, cars or vans, depending on thecommodity to be transported, as well as on the pickup anddelivery locations;

- features of vehicle: speed under diverse traffic conditions,capacity, maximum distance it can cover;

- customers ask for service by calling the call center or bybooking via the website;

- each customer specifies the pickup and delivery locations,the time windows during which he wants the service to beperformed.

Traditional same-day courier service- utilizes human controllers who assign the jobs to the

couriers;- number of requests can reach several thousands;- information complexity reaches the limits of human analysis;- a controller must take into account a number of real-time

features (parcel type, vehicle type, time windows, traveltimes, current locations of the vehicles, possible pendingjobs, traffic and weather conditions);

- it can be convenient to reposition idle couriers inhigh-density zones, to anticipate future demand (task noteasy to perform for the controllers);

- managing all these aspects creates higher operationalcosts and lower quality of service (a higher customerinconvenience).

Decision support system (1/3)

- based on the integration of a number of technologies:> GPS and GPRS, with each courier being equipped with

a GPS device embedded into a GPRS palmtopcomputer;

> GIS for the navigation system and for tracking couriers’positions;

> optimization techniques to route the vehicles;> forecasting techniques.

- Palmtop computers also used to provide directions tocouriers;

- travel time forecasting performed by a neural network thattakes into account the vehicle type, as well as weather andreal-time traffic data;

- allocation system: assigns each job to the most appropriatecourier on the basis of current fleet status, time windows,possible service level agreements, road congestion as wellas weather conditions;

- route built for each courier according to the jobs allocated,satisfying all the constraints and minimizing the distancecovered by the vehicle.

- At the tactical level:> shift scheduling, subject to constraints on the quality of

service to be provided to customers;> problem solved on a weekly or quarterly basis (the

demand is usually characterized by significant yearly,weekly and daily seasonal variations) with the aim ofminimizing the staffing cost.

- At the operational level:> dynamic vehicle-dispatching problem with pickups and

deliveries solved in order to allocate each request to avehicle, to schedule the requests assigned to eachvehicle and to reposition idle vehicles.

Operational problem (1/4)

- Defined on a graph G = (V ,A);- fleet of m vehicles, located at a depot i0 ∈V at time t = 0;- k ∈K : request, defined by (i+k ; i

k ;Tk ):> i+k ∈V : pickup point;> i−k ∈V : delivery point;> Tk ≥ 0: occurrence time.

- tij: shortest travel time from vertex i ∈V to vertex j ∈V .

- Objective: maximization of the overall customer servicelevel, rather than minimization of the total covered distance;

- τk : delivery time of the k th request;- fk (τk ): penalty function associated with each customer,

including the case for which fk (τk ) represents the customersystem time (fk (τk )= τk −Tk , τk ≥Tk ) or a more involvedpenalty function (fk (τk )= 0, Tk ≤ τk ≤Dk andfk (τk )= τk −Dk , τk ≥Dk , where Dk is a soft deadlineassociated with the k th request).

Static version

- amounts to determining an ordered sequence of locationson each vehicle route such that each route starts at thedepot;

- a pickup and its associated delivery are satisfied by thesame vehicle;

- a pickup is always made before its associated delivery;- total penalty incurred by the vehicles z =

k∈Kfk (τk ) is

minimized.

Static version

k∈Kfk (τk ) is

minimized.

Static version

k∈Kfk (τk ) is

minimized.

Static version

k∈Kfk (τk ) is

minimized.

Static version

k∈Kfk (τk ) is

minimized.

Dynamic version

- distribution of the waiting time along the routes (this mayaffect the overall solution quality);

- reposition of idle vehicles to anticipate future demand;- objective function: minimization of the expected customer

inconvenience over the planning horizon;- z =

k∈KE[fk (τk )], E [fk (τk )]: expected penalty associated

with the delivery of the k th request, k ∈K ;- a vehicle cannot be diverted away from its current

destination to service a new request in the vicinity of itscurrent position.

Dynamic version

Anticipatory algorithm (1/2)

- Embedded in both an insertion and a local searchprocedure;

- Pk : set of pending requests (i.e. the requests that haveoccurred but have not been serviced) at time Tk , whenrequest (i+k , i−k ,Tk ) arrives;

- a reactive algorithm generates a new solution incorporatingi+k and i−k with the aim of minimizing the total inconvenienceassociated with the pending requests, zk =

r∈Pk

fr(τr).

r∈Pk

fr(τr).

r∈Pk

fr(τr).

- Minimization of zk plus the expected value (under perfectinformation) of the total penalty ξ[tk ,tk+∆tk ] associated withthe requests arriving in the short-term future [tk , tk +∆tk ];

- z′

r∈Pk

fr(τr)+E[ξ[tk ,tk+∆tk ]], where ∆tk is the short-term

duration;- active, if ∆tk = 0, k ∈K .

- z′

r∈Pk

- z′

r∈Pk

Results

- eCourier gains significant improvements in terms of loweroperational costs;

- at the tactical level: the company was able to reduce itscosts by approximately 10%;

- at the operational level, the anticipatory algorithm allowedthe company to improve the quality of service provided tocustomers by about 60%, and to better distribute requestsamong the fleet.

Results

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