A genetic approach for freight transportation planning
| Pages | 719-738 |
| Date | 01 June 2006 |
| DOI | https://doi.org/10.1108/02635570610666467 |
| Published date | 01 June 2006 |
| Author | A.C. Caputo,L. Fratocchi,P.M. Pelagagge |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
A genetic approach for freight
transportation planning
A.C. Caputo, L. Fratocchi and P.M. Pelagagge
Faculty of Engineering, University of L’Aquila, L’Aquila, Italy
Abstract
Purpose – This purpose of this paper is to present a methodology for optimally planning long-haul
road transport activities through proper aggregation of customer orders in separate full-truckload or
less-than-truckload shipments in order to minimize total transportation costs.
Design/methodology/approach – The model is applied to a specific Italian multi-plant firm
operating in the plastic film for packaging sector. The method, given the order quantities to be shipped
and the location of customers, aggregates shipments in subgroups of compatible orders resorting to a
heuristic procedure and successively consolidates them in optimized full truck load and less than truck
load shipments resorting to a Genetic Algorithm in order to minimize total shipping costs respecting
delivery due dates and proper geographical and truck capacity constraints.
Findings – The paper demonstrates that evolutionary computation techniques may be effective in
tactical planning of transportation activities. The model shows that substantial savings on overall
transportation cost may be achieved adopting the proposed methodology in a real life scenario.
Research limitations/implications – The main limitation of this optimisation methodology is that
an heuristic procedure is utilized instead of an enumerative approach in order to at first aggregate
shipments in compatible sets before the optimisation algorithm carries out the assignments of
customer orders to separate truckloads. Even if this implies that the solution could be sub-optimal, it
has demonstrated a very satisfactory performance and enables the problem to become manageable in
real life settings.
Practical implications – Theproposed methodologyenables to rapidly chooseif a customer order
should be shipped via a FTL or a LTL transport and performs the aggregation of different orders in
separate shipments in order to minimize total transportation costs. As a consequence, the task of
logistics managers is greatly simplified and consistently better performances respect manual planning
can be obtained.
Originality/value – The described methodology is original in both the kind of approach adopted to
solve the problem of optimising orders shipping in long-haul direct shipping distribution logistics, and
in the solution technique adopted which integrates heuristic algorithm and an original formulation of a
GA optimisation problem. Moreover, the methodology solves both the truckload assignment problem
and the choice of LTL vs FTL shipment thus representing an useful tool for logistics managers.
Keywords Transportation,Freight forwarding, Distributionmanagement, Optimizationtechniques,
Italy
Paper type Research paper
Nomenclature
A¼constant
B¼constant
C¼cost (e)
COG ¼compatible orders groups
DSS ¼decision support system
ECL ¼economic compatibility limit (e)
F¼specific fee (e/kg)
FTL ¼full truck load
FZ ¼fixed charge characteristic of a
geographical zone (e)
GA ¼genetic algorithm
K¼fitness function parameter
L¼string length (bits)
LC ¼local transport cost (e)
LTL ¼less than truck load
m¼number of trucks
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Freight
transportation
planning
719
Industrial Management & Data
Systems
Vol. 106 No. 5, 2006
pp. 719-738
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570610666467
n¼number of customer orders
NS ¼number of customers
OVTB ¼order visibility time bucket (days)
q¼transported quantity (kg)
T¼truck number variable
UEC ¼unitary extra cost, fixed cost per
additional stop (e/stop)
W¼weight (kg)
WR ¼weight range
Z¼geographical zone
Subscripts
FTL ¼full truck load
LTL ¼less than truck load
i¼index
j¼index
k¼index
ind ¼individual
Greek symbols
d
¼auxiliary binary variable
Introduction
Distribution logistics has always been a key factor for the competitiveness of industrial
companies, but recently, its importance has grown significantly, due to the evolution of
both markets and production systems (Zografos and Ginnouli, 2002). In fact, in a context
of growing globalisation, firms have to supply markets distant from their own
warehouses and plants. Moreover, the diffusion of information and communication
technologies has introduced new ways to market products, like e-commerce paradigms,
that increase goods mobility (Caputo et al., 2004; Soliman and Youssef, 2003; Trappey
et al., 2004). This is also enhanced by the progressive shift from a make-to-stock
production to a just-in-time and quick response paradigm implying a continuous flow of
materials through the supply chain. Another phenomenon which increases mobility of
goods is the de-localization of manufacturing activities in countries with low labour cost.
Furthermore, customers’ expectations in terms of delivery service level are constantly
growing. Such factors make the distributive logistic system, and the related costs,
increasingly important and often critical for competitiveness of companies.
Road transport is by far the main mode of goods transportation in continental
industrialised countries. As an example, in the period from 1970 to 2000 road transport
has grown in the European Union countries from 52 to 74 per cent of the total quantity
of tonne kilometres hauled reaching a value of 1;348 £109tkm (European
Communities, 2003).
To supply customers by road transport, logistics managers can usually choos e
between two different shipping modes: full truck loads (FTLs) and less than truck
loads (LTLs) (Crainic, 1999). In FTL shipping, the manager hires an entire truck to
carry goods directly to customers’ locations. This kind of shipment is used if the
quantity of goods to be delivered is near to the truck capacity. In this case, the shipping
cost depends from the final destination and the number of intermediate stops and,
provided that the full truck capacity is actually saturated, this results in the lowest cost
per transported tonne. In LTL shippings, instead, only a fraction of the entire truck
capacity is hired and the cost is proportional to the transported amount with specific
fees depending on weight ranges (WRs) and the destination zone. This means that the
carrier dependent fees, the customer locations and the amount of goods to be shipped
define the actual convenience of one shipping mode respect the other one.
In the literature, a wide range of planning models for supply chain management
(Ma and Davidrajuh, 2005; Narasihman and Santosh, 2004) and specifically freight
transportation were developed (Brown and Ronen, 1997; Crainic, 2000; Crainic and
Laporte, 1997; Roy and Delorme, 1989) although they found limited practical application.
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