Data Analytics I need help with a review, please. DOI 10.1007/s00291-005-0003-6 REGULAR ARTICLE Ieke le Blanc . Maaike van Krieken . Harold Krikke . Hei

Data Analytics

I need help with a review, please.

DOI 10.1007/s00291-005-0003-6


Ieke le Blanc . Maaike van Krieken .

Harold Krikke . Hein Fleuren

Vehicle routing concepts
in the closed-loop container network of ARN
—a case study

Published online: 17 November 2005
© Springer-Verlag 2005

Abstract In this paper we discuss a real-life case study to optimize the logistics
network for the collection of containers from end-of-life vehicle dismantlers in the
Netherlands. Advanced planning concepts, such as dynamic assignment of dis-
mantlers to logistic service providers, are analyzed using a simulation model.
Based on this model, we periodically solve a vehicle routing problem to gain
insight into the long-term performance of the system. The vehicle routing problem
considered is a multi-depot pickup and delivery problem with alternative delivery
locations. A special characteristic of the problem is the limited vehicle capacity of
two containers. We solve this problem with a heuristic based on route generation
and set partitioning.

Keywords Reverse logistics . Closed-loop supply chain management . Vehicle
routing . Set partitioning . Distribution planning

1 Introduction

Concern for the environment has led to EU legislation for the recovery of discarded
products. The original equipment manufacturer (OEM), as the creator of the prod-
ucts, is responsible for and pays for the reverse chain activities. Extended Producer
Responsibility (EPR) is the starting point for all EU legislation on end-of-life waste
(Spicer and Johnson 2004). EPR extends the responsibility of the producer to cover
the entire life cycle, including end-of-life disposal. The way EPR is implemented is
left to the member states. In this paper we will address a case involving containers

The authors would like to thank Roelof Reinsma and Annemieke van Burik of Auto Recycling
Nederland for their assistance and support during the project. Furthermore, we thank the two
anonymous referees for their valuable comments on the manuscript.

H. M. le Blanc (*) . M. van Krieken . H. Krikke . H. Fleuren
CentER Applied Research, Tilburg University, P.O. Box 90153,
5000 LE Tilburg, The Netherlands

OR Spectrum 28:53–71 (2006)

used by the national Dutch auto recycling system. Based on this case, we will
analyze new route planning concepts that are based on central planning.

1.1 Developments in end-of-life vehicle recycling

The automotive industry is one of the major European industries confronted with a
massive number of end-of-life products. A total of 14.2 million passenger cars were
sold in Europe in 2003, all of which will be discarded at some time. With the
approaching deadline for implementation of the European directive on the re-
cycling of end-of-life vehicles (Directive 2000/53/EC), many EU member states
are taking initiatives in this direction (ACEA 2004). EU legislation prescribes a
recovery target of at least 85% of each car, 80% of which through reuse and
recycling by 2006. In some EU member states, national legislation is even stricter.

In the Netherlands, the national representatives of the automotive industry,
including all car manufacturers, joined hands with the founding of Auto Recycling
Nederland (ARN). ARN is responsible for the funding and the physical operations
entailed in implementing the national legislation on EPR for its members. In the
terms of Spicer and Johnson (2004), ARN is a producer responsibility organi-
zation. Under the authority of ARN, certain materials are dismantled at the col-
lection points for separate recovery; administration and reporting are essential.
Krikke et al. (2004) describe this type of reverse supply chain as a “control-type.”
These “control-type” supply chains assure that recovery is carried out in ac-
cordance with formal requirements by reporting mass-balances, showing the rela-
tionship between input, output and the degree of recovery. The costs of the logistic
network for collection, consolidation, disposition and transport of these materials
are high. Pressure from the market, together with the harmonization of national
legislations, will hopefully lead to more efficiency in the “control-type” reverse
supply chains.

1.2 Outline of the paper

The aim of the present study is to quantify the expected benefits of new advanced
planning concepts for the logistic network for containers of Auto Recycling
Nederland. The problem and its real-life setting will be discussed in Section 2. We
will limit this presentation to the part of the recycling network involving con-
tainers. In Section 3 we will discuss literature relating to the problem at hand.
Vehicle routing literature describing similar problems is scarce. On account of the
particular characteristics of the problem, we needed to develop a new heuristic.
This heuristic is described in Section 4. In Section 5, the results of the case study
are discussed. These results incorporate sensitivity analysis and analysis of alter-
native scenarios. Finally, in Section 6, the results are summarized and suggestions
for further research are given.

The various aspects of end-of-life vehicle recycling will not be described here;
the interested reader is referred to Püchert et al. (1994) for a discussion of the
business aspects of ELV recycling and for more details on the Dutch system of
ARN to Van Burik (1998) and Le Blanc et al. (2004).

54 H. M. le Blanc et al.

2 Problem description and background

2.1 Case study

The case study deals with optimizing the collection of containers that are used to
transport end-of-life materials from dismantled vehicles. Due to pressures from
the market, the ARN system will need to further improve the reverse chain for the
processing of end-of-life vehicles (ELVs). As chain director, ARN outsources the
actual processes to existing ELV-dismantlers, shredder companies, recyclers and
logistic service providers (LSPs). The LSPs are contracted for a period of three
years and are responsible for the logistics activities in a certain province. Their
activities include the transportation of the containers to a depot, consolidation at the
depot, in some cases value-adding activities such as sorting and finally transpor-
tation to the recycling company. The current logistic planning activities are decen-
tralized and performed by the individually contracted LSPs. LSPs are assigned to
ELV-dismantlers on the basis of province boundaries. In a central planning sce-
nario, transportation orders are not sent directly to the individual LSPs, but col-
lected on a centralized level and assigned in clusters to the LSPs, making use of the
cost benefits of combining orders. Allocation of ELV-dismantlers to LSPs is no
longer fixed, but adjusted regularly based on the optimization of routes on a central
level. Cruijssen and Salomon (2004) call this the principle of transportation order
sharing and find savings up to 15% in an empirical study, depending on the
characteristics of the network. In the literature, this concept is referred to as fourth
party logistics (4PL), representing an entity outside the organization that assembles
and integrates third-party capabilities to achieve transformational efficiencies not
attainable by the organization on its own (Bumstead and Cannons 2002).

In this paper, we consider manually dismantled, high-volume materials stored
and collected in containers. Table 1 gives an overview. An ELV-dismantler who has
a full container submits a request for collection to the logistic service provider
(LSP). Within five working days, the LSP visits the dismantler and exchanges the
full container for an empty one. Glass, rubber strips and PU-foam are collected in a
compartmented container, specially designed for ARN. Tires and bumpers are
collected in 35m3 containers for all ELV-dismantlers. Currently, all materials are
brought to the depot. Here, all materials, except tires, are sorted and processed and
then transferred by bulk transport to recyclers, mostly located in neighboring
countries. Since tires need no processing at the depot and the four contracted
recycling companies are located in the Netherlands, they can be sent directly to

Table 1 The materials collected in containers with their applications after recycling

Material Average amount per wreck Application of the recovered material

Tires 27.9 kg High quality: retreaded and sold as tire
Low quality: paving tiles and insulation mats

Bumpers 5.6 kg Engine covers and wheel arches
Glass 25.4 kg Bottles and glass fiber
PU-foam 6.7 kg Car seat padding and mattresses
Rubber strips 7.7 kg High purity: as roll container wheels

Low purity: as fuel in cement kilns

Vehicle routing concepts in the closed-loop container network of ARN—a case study 55

recyclers, bypassing the depot. In our computational experiments, we examine
the cost benefit of this option. We focus on the planning of requests from ELV-
dismantlers to have containers collected. Since the recyclers of materials other than
tires are located abroad, transport of these materials to the recyclers usually takes
the form of a linehaul trip. Linehaul trips offer no combination possibilities and the
costs of these trips are assumed to be fixed. Figure 1 gives an overview of the
processes in the ARN network.

Currently, LSPs use two types of lifting mechanisms for loading and unloading
containers onto a truck. The first system uses an iron chain to drag the container up
onto the truck, while the second system uses a pneumatic hook to pickup the
container and place it on the truck. Although both systems work fine, they are not
compatible. A container or truck suitable for the hook system is not suitable for the
chain system and vice versa. This restriction must be taken into account in planning
the trips, since LSPs do not have both lifting mechanisms, which leads to a com-
plexity-reducing separable structure. Figure 2 shows the map of the Netherlands
with province boundaries and the lifting mechanism in use (hook or chain). We feel
that standardization of the lifting mechanism would be an improvement.

The goal of the study is to analyze and improve the system of collecting
containers. To this end, we examine the following situations:

– Allowing direct shipment of containers from dismantler to recycler, bypassing
the consolidation depot.

– Changing the allocation of dismantlers to LSPs from the current assignment,
based on province boundaries, to optimal fixed assignment or to dynamic as-
signment based on optimal routing decisions in each planning period.

– Standardizing the lifting mechanism for loading and unloading containers onto a

Although this is mainly a tactical study, we choose to solve the operational
problem as well, to get a good estimate of transportation costs and performance.
This is because the small nuances in different scenarios cannot be adequately
expressed in tactical models, hence the need for detailed operational routes. The
problem resembles a unique multiple logistic service provider vehicle routing
model with pickup and delivery allowing alternative delivery locations and with
small vehicle capacity (two containers), which has not been described in the

Fig. 1 An overview of the processes in the ARN network for the recycling of ELVs

Consumer hands in
ELV for dismantling



Material storage

Depot for freight


Collection within 5
working days after



56 H. M. le Blanc et al.

literature before. We call this the 2-container collection problem. In the next sub-
section we will give a formal description of the problem.

2.2 The 2-container collection problem

The 2-container routing problem consists of a set of ELV-dismantlers, a set of
depots, owned by an LSP and a set of recyclers. Distance and travel times between
all locations are known. Both ELV-dismantlers and depots can initiate transporta-
tion orders for containers. At an ELV-dismantler, empty containers are exchanged
for full ones, while at a recycling facility full containers are exchanged for empty

Fig. 2 Overview of the ARN network indicating the two lifting mechanism (hook and chain) in
use per province

Vehicle routing concepts in the closed-loop container network of ARN—a case study 57

ones of the same type. Since a shortage of containers never occurs in practice in a
closed-loop system, the depot locations are assumed to have sufficient storage of
all container types to exchange. Orders may be for either one or two containers; all
orders concern containers of the same type. Full containers coming from ELV-
dismantlers can be delivered either to a depot or to a recycling facility; full con-
tainers coming from a depot can only be delivered to a recycling facility. Which
delivery location is selected depends on policy, practical restrictions, the estimated
gate fee for dropping the order at the location and the costs of including the delivery
location in the route. The gate fee depends on the residual value of the product and
can even be negative, i.e. money is paid by the recycler to acquire the material.
Figure 3 gives a conceptual mapping of the problem.

A vehicle’s route starts and ends at the depot. A route may take no longer than
nine hours, one hour of which is overtime for a 50% higher rate. For each stop, a
fixed stopping time and a variable loading and unloading time are incurred. The
costs of a route are composed of a distance and a time component. The model
allows for differentiating the kilometer and hourly rates per LSP. Vehicle capacity
in the model is limited to two containers. Each LSP is deemed to have an unlimited
number of vehicles. This is realistic since these types of trucks are widely used. In
the next section we will explore relevant literature dealing with similar problems.

3 Literature

Literature on vehicle routing is abundant, (see Bodin et al. 1983; Toth and Vigo
2002). In reverse supply chains, variants of the classical vehicle routing problem
occur that have been less extensively studied (Dethloff 2001). Beullens (2001)
provides an excellent overview of vehicle routing models and the special types of
models occurring in reverse logistics.

The problem closest to the situation at hand is the skip problem (SP) as de-
scribed in De Meulemeester et al. (1997). Vehicles start at a depot and have to
deliver empty skips to customers, collect full skips from customers and deliver the
full skips to either the depot or one of the disposal facilities. A vehicle has the



All materials
except tires

Only tires


Recyclers in

Vehicle capacity of 2


Fig. 3 Conceptual overview of the collection problem

58 H. M. le Blanc et al.

capacity to carry one skip at a time. Skips can be of multiple types and this is a
restriction in exchanging full for empty. De Meulemeester et al. (1997) develop two
heuristics and an exact procedure for solving this real-life problem. The exact
procedure is based on enumeration. The first heuristic is based on the classical
Clarke and Wright savings heuristic. The second heuristic calculates a solution to a
formulated transportation problem, providing a lower bound to the optimal solu-
tion. The solution to the transportation problem is made feasible in a number of
heuristic steps. On average, the variant of the Clarke and Wright savings algorithm
performed best.

Bodin et al. (2000) describe a variant of the skip problem called the rollon-
rolloff vehicle routing problem (RRVRP). In a RRVRP trip, a truck with a capacity
for one container departs from a depot to serve customers who need a container
placed, collected or exchanged (full for empty). The network consists of only one
depot and one disposal facility and all containers are of the same type. In that sense
the model of Bodin et al. (2000) is a simplification of the real-life case of De
Meulemeester et al. (1997). Bodin et al. (2000) develop four types of algorithms.
The first algorithm is again an adaptation of the Clarke and Wright heuristic. The
second algorithm is a trip insertion and trip improvement heuristic. The third
algorithm is a so-called decomposition algorithm, which starts by enumerating
routes, followed by solving a set covering problem. The resulting solution is
improved with some swaps. The last and most advanced algorithm is a truncated
dynamic programming heuristic, generating partial solutions that are completed by
adding the not covered orders by solving a bin-packing model. The contribution of
Bodin et al. (2000) is of a theoretical nature, since they only test the heuristics using
a set of randomly generated instances. The dynamic programming algorithm per-
forms the best, although calculation times are long. The other algorithms are faster,
but the trip insertion and trip improvement heuristics in particular are not com-
petitive in terms of solution quality.

Archetti and Speranza (2004) describe another variant of the problem, the so-
called 1-skip collection problem (1-SCP). As the name indicates, vehicle capacity
is limited to one skip or container. Since Archetti and Speranza deal with a real-life
problem, they consider several practical restrictions such as multiple container
types, time windows, different priorities for different customers and a limited fleet
size. Archetti and Speranza develop a three-phase algorithm. In phase 1, the set of
skips that needs to be collected that day is determined and ranked in priority. In
phase 2, a solution for the subset of skips is constructed. In phase 3, the solution is
further improved by using local search procedures.

Although some of the models come close to the situation at hand, none of them
has the same characteristics. All of these models consider the vehicle capacity to be
limited to precisely one skip or container instead of two as in our case. Extending
the algorithms described in literature to the situation with two containers is not
trivial. Techniques known from more general vehicle routing models could be
used; however, these techniques do not exploit the discrete capacity of only two
containers. Hence, in this paper we develop a new heuristic for tackling the prob-
lem at hand.

Vehicle routing concepts in the closed-loop container network of ARN—a case study 59

4 Description of the heuristic

The heuristic we developed to handle the case described is a two-step heuristic. In
the first step a large number of candidate routes is generated. In the second step, a
combination of routes is selected, minimizing the costs of drawing up a complete
route plan, while satisfying all the requirements. This combination of route gen-
eration and set partitioning is referred to in vehicle routing literature as the set
partitioning approach, see for example Fleuren (1988). This type of algorithms
where a promising set of possibilities is generated and a solution is found by set
partitioning is referred to as petal algorithms (Laporte et al. 2000). An alternative
way of applying set partitioning in this setting is by using column generation, see
for example Agarwal et al. (1989). Since we have a fast set partitioning solver at
our disposal and our average number of orders per route is limited, we chose to do
an enumeration of a large set of feasible routes. Figure 4 gives an overview of the

4.1 Route generation

The purpose of route generation is to construct a set of feasible routes, such that
the route selection procedure can make a “good” choice from the set. To tackle
this multi-depot pickup and delivery problem with alternative delivery locations,
we introduce the concept of root-orders and sub-orders. This is described in
Section 4.1.1.

While the number of feasible routes grows exponentially, we suffice with the
generation of a promising subset of routes. To restrict the number of candidate
routes generated, we use the concept of order neighborhoods; this is the topic of
Section 4.1.2.

Finally, the route generation procedure is described in Section 4.1.3.

4.1.1 Root and sub-orders

To handle the pickup and delivery problem with alternative delivery locations and
selection of logistic service providers, we distinguish root- and sub-orders. Every
transportation order has a general root-order with location- and LSP-specific sub-
orders. Since each sub-order has a unique pickup and delivery location as well as a
logistic service provider, our algorithm can proceed along the same lines as a
standard pickup and delivery heuristic. However, we have to add some constraints
to ensure that only one sub-order is performed per root-order.

Fig. 4 The framework for the routing heuristic

(root-) orders



Route generation

Route selection
(set partitioning)

Output route

Step 1 Step 2

60 H. M. le Blanc et al.

Example. ELV-dismantler WreckRec has a container of tires that needs to be
transported either to the tire recycler TireRec or to a depot of a logistic service
provider. There are two competing logistic service providers with a depot: LogOpt
and LogCheap. This single root-order results in four sub-orders as shown in
Table 2.

If a sub-order is selected with delivery to the depot, where delivery to the
recycler was also an option, we have to correct the route costs for the future
transportation costs from the depot to a recycler. In this situation, the sub-order
generates a new root-order in the next planning period for the transport to the
recycler. Since planning periods are short, three working days, this heuristic step is
not a severe limitation. These costs are estimated using the Eq. [1].

CostCorso ¼ � � LHCso � Loadso (1)

α=Correction factor between 1/4 and 1
LHCso=Linehaul costs to deliver a container from the depot of sub-order so to

the cheapest recycler in transportation costs and gate fee.
Loadso=Number of containers in sub-order so
The correction factor α expresses the combination possibilities for the trans-

portation orders from depot to recycler. If α=1 no combinations are made and the
full linehaul costs are charged to collect a single container. The perfect combination
would be two containers from the depot to the recycler and two containers from an
ELV-dismantler adjacent to the recycler back to the depot, which corresponds with
α=1/4. In our implementation we use α=0.8, which follows from empirical anal-
ysis in cooperation with ARN.

4.1.2 Neighborhoods

While the total number of feasible routes can be very large, up to several million,
we use the concept of neighborhoods to limit the set of candidate routes. Every
order has a set of neighbors, ordered on a distance-based criterion. When we add
orders to a route, we only consider orders that are in the neighborhood of the route,
which is the union of neighborhoods of the orders in the route.

Formally, we can describe this as follows. At the start of an empty route, every
sub-order can be inserted. Since we develop a set of routes, each root-order can
occur on several routes. For each sub-order we define a set of neighboring sub-
orders belonging to different root-orders. Let nb_subordso denote this set of
neighboring sub-orders for sub-order so. RouteSubOrdersr denotes the set of sub-

Table 2 The sub-orders in the example of WreckRec

Sub-order LSP performing the order Pickup location Delivery location

1 LogOpt WreckRec LogOpt depot
2 LogOpt WreckRec TireRec
3 LogCheap WreckRec LogCheap depot
4 LogCheap WreckRec TireRec

Vehicle routing concepts in the closed-loop container network of ARN—a case study 61

orders in route r. The neighborhood of a route r, denoted as nb_router, is the union
of the neighborhoods of the sub-orders in a route, i.e. nb router ¼ [

nb subordso:

To determine the neighborhood of a sub-order we need a distance measure. This
is a heuristic step in the procedure. Consider two sub-orders so_A and so_B, with
pso and dso denoting the respective pickup and the delivery location of sub-order so.
Our distance measure is based on the best way to combine two orders rather than
drive them separately. Mathematically this criterion is given in [2].

distso A;so B ¼ min d pso A; dso Að Þ þ d dso A; pso Bð Þ þ d pso B; dso Bð Þ;f
d pso A; pso Bð Þ þ d pso B; dso Að Þ þ d dso A; dso Bð Þ;
d pso A; pso Bð Þ þ d pso B; dso Bð Þ þ d dso B; dso Að Þ;
d pso B; dso Bð Þ þ d dso B; pso Að Þ þ d pso A; dso Að Þ;
d pso B; pso Að Þ þ d pso A; dso Bð Þ þ d dso B; dso Að Þ;
d pso B; pso Að Þ þ d pso A; dso Að Þ þ d dso A; dso Bð Þg
�d pso A; dso Að Þ � d pso B; dso Bð Þ

For each sub-order, we list the distances to all suborders belonging to a different
root-order and include the nearest nb_size sub-orders in nb_subordso. Experiments
with the required size of the neighborhood to find suitable solutions in acceptable
computational time for the given study indicated that nb_size=6 performs well; we
will use this value in the rest of this paper. Figure 5 shows the diminishing im-
provements found by extending the neighborhood size is shown for a represen-
tative sample of 25 real-life instances consisting of an average of 54 root-orders and
114 sub-orders. Further increasing the neighborhood size will marginally improve
the solution and cause a big increase in the route generation times. Note that above
a certain threshold the route generation is no longer restricted and all feasible
combinations are generated.


Influence neighborhoodsize







1 2 3 4 5 6 7 8 9 10






Cost Route generation time













Neighborhood size

Fig. 5 The influence of changing the size of the neighborhood on the quality of the solution based
on a representative sample of 25 real-life instances (computing time index 100=1498 s)

62 H. M. le Blanc et al.

4.1.3 Outline of the route generation algorithm

The aim of the route generator is to create a large number of attractive and feasible
routes. As stated in Section 4.1.2, we restrict the enumeration of routes by only
appending orders from the neighborhood. A route is feasible if the maximum time
allowed for one day and the maximum vehicle capacities along the route are not
exceeded. Every time a full container is picked up from an ELV dismantler, it must
be exchanged for an empty container of the same type. If this is not possible, the
route is infeasible. We make use of a recursive function implementation for the
systematic generation of routes. The RouteGenerator function describes the main
idea behind the route generation algorithm.

A sub-order is added to a route by inserting the pickup stop and the delivery
stop of the sub-order in the route. Since we deal with the pickup and delivery
situation, for each possible position where the pickup stop (StopP) can be in-
serted, we find the cheapest position to insert the delivery stop (StopD). The
InsertSubOrder function describes the main ideas behind the insertion of a sub-
order in a route.

Although the number of routes generated is restricted by the size of the order
neighborhood, it can still be very large in some cases. Occasionally, over 2.5

Function RouteGenerator
IF ( Route empty )

RouteNeighborHood := Set of all SubOrders
FOR ( SubOrder in RouteNeighborHood AND RootOrder unplanned ) DO

InsertSubOrder( SubOrder )
IF( RouteFeasible )THEN




Function InsertSuborder( SubOrder)
FOR ( Position in Route ) DO

Insert StopP
FOR ( Position in Route after Stop P ) DO

Insert StopD

IF ( BestInsertion AND RouteFeasible ) THEN

Remove StopD

Remove StopP

IF ( BestInsertionExists ) THEN

Insert StopD and StopP at best position


Vehicle routing concepts in the closed-loop container network of ARN—a case study 63

million routes are generated. In that case, because of memory limitations of our
computers, we reduce the maximum allowed size of the neighborhood by one and
restart the route generation.

4.2 Route selection

The problem of finding the optimal combination of routes such that all orders are
performed at minimal costs is formulated as a set partitioning problem. After
introducing some notation, the problem is given in Eq. [3]–[5].

δso,ro = 1 if sub-order so belongs to root-order ro, 0 otherwise.
aso,r = 1 if sub-order so is contained in route r, 0 otherwise.
cr = denotes the costs of driving route r in euro.
pr = denotes the profit or costs (negative pr) of route r as a result of the chosen

delivery locations for the orders in route r in euro.

Xr=1 if route r is selected, 0 otherwise.

The route selection problem



cr � prð Þ � Xr (3)





�so;ro � aso;rð Þ � Xr ¼ 1 8ro (4)

Xr 2 0; 1f g 8r (5)

Note that

�so;ro � aso;r is either 0 or 1 by construction of the route generator

and therefore the route selection problem is a pure set partitioning problem. To
exploit the special structure of the set partitioning problem we make use of a special
set partitioning solver, rather than more generic mixed-integer linear programming
solvers such as Cplex ( We use the solver developed by Van
Krieken et al. (2004). This solver uses Lagrangean relaxation and dual heuristics to
determine the lower bound and branch and bound for finding the optimal solution.
Furthermore, several problem reduction techniques are used to reduce the number
of variables and constraints in the problem (Van Krieken et al. 2003). The solver is
very effective at solving the set partitioning instances under consideration, al-
though the number of variables can become very large. Problems with over a
million variables are solved in a couple of minutes on a normal desktop computer.

64 H. M. le Blanc et al.

5 Structure of the analysis

5.1 Simulation

We use a simulation model to analyze the performance of the system. The
transportation orders from E

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