This document summarizes a case study analyzing concepts to optimize the logistics network for collecting containers from vehicle dismantlers in the Netherlands. The network involves multiple depots, dismantlers, and recyclers. The study examines allowing direct shipment from dismantlers to recyclers, changing allocation of dismantlers to logistic service providers, and standardizing container lifting mechanisms. The problem is characterized as a vehicle routing problem with multiple depots, pickup and delivery, and limited vehicle capacity of two containers. A heuristic is developed to solve this special type of problem, which has not been well addressed in literature.

1. DOI 10.1007/s00291-005-0003-6
REGULAR ARTICLE
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

2. 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
E-mail: [email protected], [email protected], [email protected],
[email protected]

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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
truck.
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

10. 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
Dismantling
Shredder
Carcass
Material storage
(container)
Depot for freight
consolidation
Material
recycler
Collection within 5
working days after
request
Materials
56 H. M. le Blanc et al.
literature before. We call this the 2-container collection

11. 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

12. 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)

13. 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
ELV -
dismantlers
Depots
Recyclers
All materials
except tires
Only tires
abroad
Recyclers in
the
Netherlands
Vehicle capacity of 2
containers
Fig. 3 Conceptual overview of the collection problem

14. 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.

15. 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

16. 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, minimi zing the costs of
drawing up a complete
route plan, while satisfying all the requirements. This

17. 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
heuristic.
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;

18. 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
Input
(root-) orders
Determine
sub-orders
Calculate
neighborhood
sub-orders
Route generation

19. Route selection
(set partitioning)
Output route
schedule
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)

20. where:
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

21. 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 [
so2RouteSubOrdr
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

22. 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

23. and all feasible
combinations are generated.
(2)
Influence neighborhoodsize
90
92
94
96
98
100
1 2 3 4 5 6 7 8 9 10
0
20
40
60
80
100
Cost Route generation time
C
o

24. s
t
in
d
e
x
C
o
n
m
p
u
ti
n
g
t
im
e
in
d
e
x
Neighborhood size

25. 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

26. (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
ENDIF
FOR ( SubOrder in RouteNeighborHood AND RootOrder
unplanned ) DO
InsertSubOrder( SubOrder )
UpdateRouteNeighborhood
IF( RouteFeasible )THEN
WriteRouteToRouteSelectionProblem
RouteGenerator
ENDIF
RemoveSubOrder
UpdateRouteNeighborhood
ENDFOR
Function InsertSuborder( SubOrder)
FOR ( Position in Route ) DO
Insert StopP
FOR ( Position in Route after Stop P ) DO

27. Insert StopD
UpdateRoute
IF ( BestInsertion AND RouteFeasible ) THEN
StoreBestInsertionPosition
ENDIF
Remove StopD
ENDFOR
Remove StopP
ENDFOR
IF ( BestInsertionExists ) THEN
Insert StopD and StopP at best position
UpdateRoute
ENDIF
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

28. performed at minimal costs is formulated as a set partitioning
problem. After
introducing some notation, the problem is given in Eq. [3]–[5].
Parameters
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.
Variables
Xr=1 if route r is selected, 0 otherwise.
The route selection problem
min
P
r
cr � prð Þ � Xr (3)
s:t:
P
r
P
so
�so;ro � aso;rð Þ � Xr ¼ 1 8ro (4)

29. Xr 2 0; 1f g 8r (5)
Note that
P
so
�so;ro � aso;r is either 0 or 1 by constructio n 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 (http://www.ilog.com). 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.
http://www.ilog.com

30. 5 Structure of the analysis
5.1 Simulation
We use a simulation model to analyze the performance of the
system. The
transportation orders from ELV-dismantlers are generated
following empirical
distributions. To obtain representative results, each simulation
run consists of 10
replications of one year. In the simulation, the operational
vehicle routing problem
is solved twice a week for a planning horizon of three
workdays. This means that
over 1000 set partitioning problems are generated and solved
per simulation run.
Orders generated during a certain collection period are planned
and executed
the next planning period. For containers of tires brought to the
depot, the orders for
shipping the containers to the recycler are also issued at the
beginning of the next
planning period. In this way transportation orders are fixed at
the beginning of a
planning period.
5.2 Data and scenarios
The scenarios are constructed in cooperation with the logistic
experts of ARN and
in cooperation with the logistic service providers hired by ARN.
Distances and
driving times used in the analysis were obtained from Evo-IT
(http://www.evo-it.nl).

31. The cost figures used were obtained from the NEA (2004),
which is an authority
on traffic and transportation issues in the Netherlands. We use
cost prices rather
than the commercial rates of individual LSPs. The data used for
simulating the
processes at the ELV-dismantlers are empirical data available in
the corporate
databases of ARN. A detailed description of these data can be
found in Schreurs
(2004).
Scenarios are defined along three dimensions:
– The lifting mechanisms used by the LSPs:
– The current situation: two different lifting mechanisms are
used.
– The standardized situation: all LSPs use the same lifting
mechanism.
– The assignment of transportation orders to the logistics
service providers
– Current fixed assignment: ELV dismantlers are assigned to
LSPs and
recyclers on the basis of province boundaries.
– Optimized fixed assignment: ELV-dismantlers are assigned to
the closest
LSP/recycler based on a distance criterion.
– Central planning: no fixed assignment exists; the LSP with the
best com-
bination possibilities executes the transportation order.

32. – The allowed routes for containers of tires:
– No direct shipment: all tire containers pass the depot.
– Direct shipment: this is allowed if it is advantageous to ship
tire containers
directly to a tire recycler instead of the depot.
Vehicle routing concepts in the closed-loop container network
of ARN—a case study 65
http://www.evo-it.nl
Figure 6 shows six scenarios defined along the last two
dimensions and their
scenario IDs. These six scenarios can be applied to both lifting
mechanisms, the
first dimension, resulting in a total of twelve. Scenario Cur -
indirect is our reference
scenario and corresponds to the current situation of ARN.
The current assignment of ELV-dismantlers to depots and
recyclers is based on
province boundaries, for historic reasons. In many cases, this
assignment is far
from efficient, since provinces can have irregular shapes. We
resolve this by simply
assigning each ELV-dismantler to the nearest depot with the
proper lifting mech-
anism. In the central planning scenario, the effect of a fixed
assignment is analyzed
by letting go of this restriction altogether and using dynamic
planning on a central
level.

33. Currently, nearly all tire containers are transported to the
recycler via a depot,
since the container must be weighed at the depot. Nowadays,
recyclers also have
accurate weighing facilities for trucks, making a stop at the
depot no longer
necessary. Direct shipment of containers filled with tires is
possible as long as the
date of delivery is communicated.
6 Results
6.1 Current logistic network
The results for the current logistic network with LSPs having
different types of
lifting mechanisms are presented in Table 3. For reasons of
confidentiality, the cost
figures have been indexed. A comparison of the various
scenarios for the yearly
indexed costs is also presented in Fig. 7.
Allowing the logistic service providers to ship tire containers
directly from
ELV-dismantlers to recyclers, results in cost savings ranging
from 6.3% to 9.1%,
depending on the way in which ELV-dismantlers are assigned to
LSPs. The average
route length increases both in time and distance, since it is more
attractive to make a
Allow direct shipments
Current assignment
Allow direct shipments
Optimized assignment

34. Allow direct shipments
Central planning
Only indirect shipments
Current assignment
Only indirect shipments
Optimized assignment
Only indirect shipments
Central planning
Scenarios
A
s
s
ig
n
m
e
n
t
o
f
o
rd
e

35. rs
Types of shipment allowed
Cur-direct
Opt-direct
CP-direct
Cur-indirect
Opt-indirect
CP-indirect
Fig. 6 An overview of the scenarios
66 H. M. le Blanc et al.
small detour to drop tire containers at a tire recycler rather than
bring them first to
the depot and then to the recycler. This phenomenon is
responsible for the drastic
decreases in the number of routes driven, since most tire
containers are transported
only once. Implementation of direct shipment is fairly easy and
only requires some
further arrangements with the recyclers.
Optimizing the assignment of ELV-dismantlers to depots and
recyclers results
in cost decreases ranging from 4.4% to 4.7%. This effect is
small, since the di-

36. Table 3 Results for the current network with restrictions on the
lifting mechanisms (Case 1)
Scenario ID Cur-
indirect
Opt-
indirect
CP-indirect Cur-
direct
Opt-
direct
CP-direct
Assignment Fixed,
current
Fixed,
optimized
Free, central
planning
Fixed,
current
Fixed,
optimized
Free,
central
planning

37. Type of shipments
for tires
Only
indirect
Only
indirect
Only
indirect
Allow
direct
Allow
direct
Allow
direct
Average costs per
year (indexed)
100 95.3 94.8 93.4 89.3 86.1
Average distance per
year (km)
505,779 471,610 467,188 483,092 458,972 433,735
Average number of
routes per year
2,887 2,906 2,907 2,346 2,336 2,226

38. Average number of
containers per route
2.45 2.44 2.44 2.39 2.32 2.42
Average route distance
(km)
175.2 162.3 160.7 205.9 196.4 194.8
Average route duration
(min)
291.0 277.6 276.1 331.3 319.7 325.4
Average driving time
per route (min)
177.1 164.3 162.8 208.7 198.4 198.2
Average load and
unloadtime per route
114.0 113.3 113.3 122.6 121.3 127.1
Fig. 7 Comparison of scenarios with current and standardized
lifting mechanism
Vehicle routing concepts in the closed-loop container network
of ARN—a case study 67
versity in container lifting mechanisms allows little freedom for
optimization. It is

39. fairly easy to change to another fixed assignment: it merely
requires renegotiation
of contracts with LSPs.
Compared to the optimal fixed assignment, the extra savings of
dynamic
allocation by central planning are limited, ranging from 0.6% to
3.6%. These
marginal cost savings are not offset by the changes in the
planning and control
mechanisms to implement dynamic assignment.
6.2 Network with uniform lifting mechanism for containers
The differences in lifting mechanisms in use by logistic service
providers are likely
to cause inefficiencies. ARN is lobbying for standardizing
container lifting
mechanisms at the logistic service providers. This situation is
compared to the
current situation in Table 4. Figure 7 shows the yearly indexed
costs of the various
scenarios for the current situation as well as for uniform lifting
mechanisms.
Currently, the assignment of dismantlers to depots and recyclers
takes differences
in lifting mechanisms into account. Therefore, standardization
of the lifting mech-
anism only makes sense when the assignment is changed. We
compare the current
situation with the optimized assignment and central planning
scenarios with a
uniform lifting mechanism.
Table 4 Results for the current network after loosening the
restrictions on the lifting mechanis ms

40. (Case 2)
Scenario ID Cur-
indirect
Opt-
indirect
CP-indirect Cur-
direct
Opt-
direct
CP-
direct
Assignment Fixed,
current
Fixed,
optimized
Free, central
planning
Fixed,
current
Fixed,
optimized
Free,
central
planning

41. Type of shipments for tires Only
indirect
Only
indirect
Only indirect Allow
direct
Allow
direct
Allow
direct
Average costs per year
(indexed)
100 87.2 86.9 93.4 81.6 80.8
Average distance per year
(km)
505,779 411,893 408,954 483,092 402,125 394,886
Average number of routes
per year
2,887 2,891 2,876 2,346 2,254 2,280
Average number of con-
tainers per route
2.45 2.45 2.47 2.39 2.39 2.36
Average route

42. distance (km)
175.2 142.5 142.2 205.9 178.4 173.2
Average route
duration (min)
291.0 258.8 259.4 331.3 306.6 301.1
Average driving time per
route (min)
177.1 145.1 145.0 208.7 181.4 177.2
Average load and
unloadtime per route
114.0 113.7 114.4 122.6 125.1 123.9
68 H. M. le Blanc et al.
Using optimal fixed assignment, the cost savings of
standardizing the lifting
mechanism are about 8.7% when we allow direct shipments. If
direct shipments are
not allowed the cost savings are 8.5%.
The cost savings of standardizing the lifting mechanism in the
case of central
dynamic planning are 8.3% when direct shipment is not allowed
and 6.1% when
direct shipment is allowed. Given standardized lifting
mechanisms, the cost
savings of dynamic central planning over optimized fixed

43. assignment are less than
1%, whether we allow direct shipment or not, which does not
offset the costs of the
organizational changes. Standardizing the lifting mechanism is
comparable with
increasing the network density for the LSPs. Improving the
combination pos-
sibilities in a dense network has a marginal effect on the costs
since, in a dense
network, there are already abundant combination possibilities.
These results on
central planning are supported by the findings of Cruijssen and
Salomon (2004),
who showed that the benefits of central planning are limited
when orders are large
compared to the vehicle capacity. Moreover, our orders are not
randomly assigned
to depots, but on the basis of province boundaries. Although, as
we have seen,
province boundaries are far from optimal, they still have some
logic and are much
better than random assignment as was initially the case in
Cruijssen and Salomon
(2004).
When we optimize the assignment of recyclers to LSPs,
standardizing the
lifting mechanism results in considerable cost savings that
justify the necessary
investment to implement this in the chain of ARN.
7 Conclusions and outlook
In this paper we have described a real-life project in optimizing
the logistic network
for containers with materials from end-of-life vehicles. The

44. underlying vehicle
routing model is a unique multi-depot pickup and delivery
model with alternative
delivery locations. The heuristic we used is based on generating
a set of promising
routes and selecting the optimal combination of routes by
solving a set partitioning
problem.
The reasons for the limited research on this type of problems
probably lies in
the fact that it is considered a typical reverse logistics problem
where waste or cores
for recycling are collected, bundled and brought to a central
recovery center. We are
not aware of forward logistic problems with similar
characteristics. Although we
developed a new heuristic and the heuristics described in the
literature stem from
problem instances that are typically product recovery or waste
disposal networks,
we do not have the impression that the mathematical techniques
differ. Relating
this project to earlier projects in the same recycling network, we
conjecture that,
although logistic concepts differ from forward logistics, the
mathematical tech-
niques and models in this network are not fundamentally
different.
From a business point of view, we analyzed the consequences of
a better
assignment of waste generators to logistics service providers
and of routing de-
cisions made by central planning. Furthermore, we analyzed the
influence of a

45. policy that did not allow the direct shipment from waste
generator sites to recycling
facilities and the effects of the different lifting mechanisms
used for containers.
With respect to the assignment of recyclers to logistics service
providers, we
recommend changing the current fixed assignment, which is
based on province
Vehicle routing concepts in the closed-loop container network
of ARN—a case study 69
boundaries, to the optimal fixed assignment. Considerable effort
would be in-
volved in implementation of the dynamic assignment option,
while the additional
savings over the optimal fixed assignment are limited. Since the
study shows that
allowing direct shipment will result in cost savings and the
organizational burden is
not very large, we recommend allowing direct shipment of tires
to recyclers. With
respect to the lifting mechanism, the study has shown that
standardization will
result in significant cost savings, making it worth the effort to
standardize the
lifting mechanism in the ARN network. The total percentage
cost savings of the
recommended new system with standardized lifting mechanism,
the option of
direct shipments and the optimal fixed assignment are over 18%
compared to the
current system.

46. Since cost reductions in closed-loop supply chains for EOL
products are
crucial, they can make the difference between recycling for
profit or for loss. In the
latter case, OEMs will not recycle as long as they are not forced
to do so by
legislation. The best way towards a sustainable society is
through business moti-
vations. Since we have only just started to set up and design
product recovery
networks, there are great opportunities for operations research
to assist by offering
advanced planning systems from the operational to the strategic
level.
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Vehicle routing concepts in the closed-loop container network
of ARN—a case study 71
Vehicle routing concepts in the closed-loop container network
of ARN---a case studyAbstractIntroductionDevelopments in
end-of-life vehicle recyclingOutline of the paperProblem
description and backgroundCase studyThe 2-container
collection problemLiteratureDescriptio n of the heuristicRoute
generationRoot and sub-ordersNeighborhoodsOutline of the
route generation algorithmRoute selectionStructure of the
analysisSimulationData and scenariosResultsCurrent logistic
networkNetwork with uniform lifting mechanism for
containersConclusions and outlookReferences

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/CooperBT-BlackHeadline
/CooperBT-BlackItalic
/CooperBT-Bold
/CooperBT-BoldItalic
/CooperBT-Medium

58. /CooperBT-MediumItalic
/CooperPlanck2LightSH
/CooperPlanck4SH
/CooperPlanck6BoldSH
/CopperplateGothicBT-Bold
/CopperplateGothicBT-Roman
/CopperplateGothicBT-RomanCond
/CopticLS
/Cornerstone
/Coronet
/CoronetItalic
/Cotillion
/CountryBlueprint
/CourierNewPS-BoldItalicMT
/CourierNewPS-BoldMT
/CourierNewPS-ItalicMT
/CourierNewPSMT
/CSSubscript
/CSSubscriptBold
/CSSubscriptItalic
/CSSuperscript
/CSSuperscriptBold
/Cuckoo
/CurlzMT
/CybilListzSH
/CzarBold
/CzarBoldItalic
/CzarItalic
/CzarNormal
/DauphinPlain
/DawnCastleBold
/DawnCastlePlain
/Dekker
/DellaRobbiaBT-Bold
/DellaRobbiaBT-Roman
/Denmark

59. /Desdemona
/Diploma
/DizzyDomingoSH
/DizzyFeiningerSH
/DocTermanBoldSH
/DodgenburnA
/DodoCasalsSH
/DodoDiogenesSH
/DomCasualBT-Regular
/Durian-Republik
/Dutch801BT-Bold
/Dutch801BT-BoldItalic
/Dutch801BT-ExtraBold
/Dutch801BT-Italic
/Dutch801BT-Roman
/EBT's-cmbx10
/EBT's-cmex10
/EBT's-cmmi10
/EBT's-cmmi5
/EBT's-cmmi7
/EBT's-cmr10
/EBT's-cmr5
/EBT's-cmr7
/EBT's-cmsy10
/EBT's-cmsy5
/EBT's-cmsy7
/EdithDaySH
/Elephant-Italic
/Elephant-Regular
/EmGravesSH
/EngelEinsteinSH
/English111VivaceBT-Regular
/English157BT-Regular
/EngraversGothicBT-Regular
/EngraversOldEnglishBT-Bold
/EngraversOldEnglishBT-Regular

60. /EngraversRomanBT-Bold
/EngraversRomanBT-Regular
/EnviroD
/ErasITC-Bold
/ErasITC-Demi
/ErasITC-Light
/ErasITC-Medium
/ErasITC-Ultra
/ErnestBlochSH
/Euclid
/Euclid-Bold
/Euclid-BoldItalic
/EuclidExtra
/EuclidExtra-Bold
/EuclidFraktur
/EuclidFraktur-Bold
/Euclid-Italic
/EuclidMathOne
/EuclidMathOne-Bold
/EuclidMathTwo
/EuclidMathTwo-Bold
/EuclidSymbol
/EuclidSymbol-Bold
/EuclidSymbol-BoldItalic
/EuclidSymbol-Italic
/EuroRoman
/EuroRomanOblique
/ExxPresleySH
/FencesPlain
/Fences-Regular
/FifthAvenue
/FigurineCrrCB
/FigurineCrrCBBold
/FigurineCrrCBBoldItalic
/FigurineCrrCBItalic
/FigurineTmsCB

61. /FigurineTmsCBBold
/FigurineTmsCBBoldItalic
/FigurineTmsCBItalic
/FillmoreRegular
/Fitzgerald
/Flareserif821BT-Roman
/FleurFordSH
/Fontdinerdotcom
/FontdinerdotcomSparkly
/FootlightMTLight
/ForefrontBookObliqueSH
/ForefrontBookSH
/ForefrontDemiObliqueSH
/ForefrontDemiSH
/Fortress
/FractionsAPlentySH
/FrakturPlain
/Franciscan
/FranklinGothic-Medium
/FranklinGothic-MediumItalic
/FranklinUnic
/FredFlahertySH
/Freehand575BT-RegularB
/Freehand591BT-RegularA
/FreestyleScript-Regular
/Frutiger-Roman
/FTPMultinational
/FTPMultinational-Bold
/FujiyamaPSMT
/FuturaBlackBT-Regular
/FuturaBT-Bold
/FuturaBT-BoldCondensed
/FuturaBT-BoldItalic
/FuturaBT-Book
/FuturaBT-BookItalic
/FuturaBT-ExtraBlack

62. /FuturaBT-ExtraBlackCondensed
/FuturaBT-ExtraBlackCondItalic
/FuturaBT-ExtraBlackItalic
/FuturaBT-Light
/FuturaBT-LightItalic
/FuturaBT-Medium
/FuturaBT-MediumCondensed
/FuturaBT-MediumItalic
/GabbyGauguinSH
/GalliardITCbyBT-Bold
/GalliardITCbyBT-BoldItalic
/GalliardITCbyBT-Italic
/GalliardITCbyBT-Roman
/Garamond
/Garamond-Antiqua
/Garamond-Bold
/Garamond-Halbfett
/Garamond-Italic
/Garamond-Kursiv
/Garamond-KursivHalbfett
/Garcia
/GarryMondrian3LightItalicSH
/GarryMondrian3LightSH
/GarryMondrian4BookItalicSH
/GarryMondrian4BookSH
/GarryMondrian5SBldItalicSH
/GarryMondrian5SBldSH
/GarryMondrian6BoldItalicSH
/GarryMondrian6BoldSH
/GarryMondrian7ExtraBoldSH
/GarryMondrian8UltraSH
/GarryMondrianCond3LightSH
/GarryMondrianCond4BookSH
/GarryMondrianCond5SBldSH
/GarryMondrianCond6BoldSH
/GarryMondrianCond7ExtraBoldSH

63. /GarryMondrianCond8UltraSH
/GarryMondrianExpt3LightSH
/GarryMondrianExpt4BookSH
/GarryMondrianExpt5SBldSH
/GarryMondrianExpt6BoldSH
/GarryMondrianSwashSH
/Gaslight
/GatineauPSMT
/GDT
/Geometric231BT-BoldC
/Geometric231BT-LightC
/Geometric231BT-RomanC
/GeometricSlab703BT-Bold
/GeometricSlab703BT-BoldCond
/GeometricSlab703BT-BoldItalic
/GeometricSlab703BT-Light
/GeometricSlab703BT-LightItalic
/GeometricSlab703BT-Medium
/GeometricSlab703BT-MediumCond
/GeometricSlab703BT-MediumItalic
/GeometricSlab703BT-XtraBold
/GeorgeMelvilleSH
/Georgia
/Georgia-Bold
/Georgia-BoldItalic
/Georgia-Italic
/Gigi-Regular
/GillSansBC
/GillSans-Bold
/GillSans-BoldItalic
/GillSansCondensed-Bold
/GillSansCondensed-Regular
/GillSansExtraBold-Regular
/GillSans-Italic
/GillSansLight-Italic
/GillSansLight-Regular

64. /GillSans-Regular
/GoldMinePlain
/Gonzo
/GothicE
/GothicG
/GothicI
/GoudyHandtooledBT-Regular
/GoudyOldStyle-Bold
/GoudyOldStyle-BoldItalic
/GoudyOldStyleBT-Bold
/GoudyOldStyleBT-BoldItalic
/GoudyOldStyleBT-Italic
/GoudyOldStyleBT-Roman
/GoudyOldStyleExtrabold-Regular
/GoudyOldStyle-Italic
/GoudyOldStyle-Regular
/GoudySansITCbyBT-Bold
/GoudySansITCbyBT-BoldItalic
/GoudySansITCbyBT-Medium
/GoudySansITCbyBT-MediumItalic
/GraceAdonisSH
/Graeca
/Graeca-Bold
/Graeca-BoldItalic
/Graeca-Italic
/Graphos-Bold
/Graphos-BoldItalic
/Graphos-Italic
/Graphos-Regular
/GreekC
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/GreekSans
/GreekSans-Bold
/GreekSans-BoldOblique
/GreekSans-Oblique
/Griffin

65. /GrungeUpdate
/Haettenschweiler
/HankKhrushchevSH
/HarlowSolid
/HarpoonPlain
/Harrington
/HeatherRegular
/Hebraica
/HeleneHissBlackSH
/Helvetica
/Helvetica-Bold
/Helvetica-BoldOblique
/Helvetica-Narrow
/Helvetica-Narrow-Bold
/Helvetica-Narrow-BoldOblique
/Helvetica-Narrow-Oblique
/Helvetica-Oblique
/HenryPatrickSH
/Herald
/HighTowerText-Italic
/HighTowerText-Reg
/HogBold-HMK
/HogBook-HMK
/HomePlanning
/HomePlanning2
/HomewardBoundPSMT
/Humanist521BT-Bold
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/Humanist521BT-BoldItalic
/Humanist521BT-Italic
/Humanist521BT-Light
/Humanist521BT-LightItalic
/Humanist521BT-Roman
/Humanist521BT-RomanCondensed
/IBMPCDOS
/IceAgeD

66. /Impact
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/Incised901BT-Light
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/Industrial736BT-Italic
/Informal011BT-Roman
/InformalRoman-Regular
/Intrepid
/IntrepidBold
/IntrepidOblique
/Invitation
/IPAExtras
/IPAExtras-Bold
/IPAHighLow
/IPAHighLow-Bold
/IPAKiel
/IPAKiel-Bold
/IPAKielSeven
/IPAKielSeven-Bold
/IPAsans
/ISOCP
/ISOCP2
/ISOCP3
/ISOCT
/ISOCT2
/ISOCT3
/Italic
/ItalicC
/ItalicT
/JesterRegular
/Jokerman-Regular
/JotMedium-HMK
/JuiceITC-Regular
/JupiterPSMT
/KabelITCbyBT-Book
/KabelITCbyBT-Ultra

67. /KarlaJohnson5CursiveSH
/KarlaJohnson5RegularSH
/KarlaJohnson6BoldCursiveSH
/KarlaJohnson6BoldSH
/KarlaJohnson7ExtraBoldCursiveSH
/KarlaJohnson7ExtraBoldSH
/KarlKhayyamSH
/Karnack
/Kashmir
/KaufmannBT-Bold
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/KeplerStd-Black
/KeplerStd-BlackIt
/KeplerStd-Bold
/KeplerStd-BoldIt
/KeplerStd-Italic
/KeplerStd-Light
/KeplerStd-LightIt
/KeplerStd-Medium
/KeplerStd-MediumIt
/KeplerStd-Regular
/KeplerStd-Semibold
/KeplerStd-SemiboldIt
/KeystrokeNormal
/Kidnap
/KidsPlain
/Kindergarten
/KinoMT
/KissMeKissMeKissMe
/KoalaPSMT
/KorinnaITCbyBT-Bold
/KorinnaITCbyBT-KursivBold
/KorinnaITCbyBT-KursivRegular
/KorinnaITCbyBT-Regular
/KristenITC-Regular
/Kristin

68. /KunstlerScript
/KyotoSong
/LainieDaySH
/LandscapePlanning
/Lapidary333BT-Bold
/Lapidary333BT-BoldItalic
/Lapidary333BT-Italic
/Lapidary333BT-Roman
/LatinoPal3LightItalicSH
/LatinoPal3LightSH
/LatinoPal4ItalicSH
/LatinoPal4RomanSH
/LatinoPal5DemiItalicSH
/LatinoPal5DemiSH
/LatinoPal6BoldItalicSH
/LatinoPal6BoldSH
/LatinoPal7ExtraBoldSH
/LatinoPal8BlackSH
/LatinoPalCond4RomanSH
/LatinoPalCond5DemiSH
/LatinoPalCond6BoldSH
/LatinoPalExptRomanSH
/LatinoPalSwashSH
/LatinWidD
/LatinWide
/LeeToscanini3LightSH
/LeeToscanini5RegularSH
/LeeToscanini7BoldSH
/LeeToscanini9BlackSH
/LeeToscaniniInlineSH
/LetterGothic12PitchBT-Bold
/LetterGothic12PitchBT-BoldItal
/LetterGothic12PitchBT-Italic
/LetterGothic12PitchBT-Roman
/LetterGothic-Bold
/LetterGothic-BoldItalic

69. /LetterGothic-Italic
/LetterGothicMT
/LetterGothicMT-Bold
/LetterGothicMT-BoldOblique
/LetterGothicMT-Oblique
/LetterGothic-Regular
/LibrarianRegular
/LinusPSMT
/Lithograph-Bold
/LithographLight
/LongIsland
/LubalinGraphMdITCTT
/LucidaBright
/LucidaBright-Demi
/LucidaBright-DemiItalic
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/LucidaCalligraphy-Italic
/LucidaConsole
/LucidaFax
/LucidaFax-Demi
/LucidaFax-DemiItalic
/LucidaFax-Italic
/LucidaHandwriting-Italic
/LucidaSans-Typewriter
/LucidaSans-TypewriterBold
/LucidaSansUnicode
/LydianCursiveBT-Regular
/Magneto-Bold
/Map-Symbols
/MarcusHobbesSH
/Mariah
/Marigold
/MaritaMedium-HMK
/MaritaScript-HMK
/Market
/MartinMaxxieSH

70. /MathTypeMed
/MatisseITC-Regular
/MaturaMTScriptCapitals
/MaudeMeadSH
/MemorandumPSMT
/Metro
/Metrostyle-Bold
/MetrostyleExtended-Bold
/MetrostyleExtended-Regular
/Metrostyle-Regular
/MicrogrammaD-BoldExte
/MicrosoftSansSerif
/MikePicassoSH
/MiniPicsLilEdibles
/MiniPicsLilFolks
/MiniPicsLilStuff
/MischstabPopanz
/MisterEarlBT-Regular
/Mistral
/ModerneDemi
/ModerneDemiOblique
/ModerneOblique
/ModerneRegular
/Modern-Regular
/MonaLisaRecutITC-Normal
/Monospace821BT-Bold
/Monospace821BT-BoldItalic
/Monospace821BT-Italic
/Monospace821BT-Roman
/Monotxt
/MonotypeCorsiva
/MonotypeSorts
/MorrisonMedium
/MorseCode
/MotorPSMT
/MSAM10

71. /MSLineDrawPSMT
/MS-Mincho
/MSOutlook
/MSReference1
/MSReference2
/MTEX
/MTEXB
/MTEXH
/MT-Extra
/MTGU
/MTGUB
/MTLS
/MTLSB
/MTMI
/MTMIB
/MTMIH
/MTMS
/MTMSB
/MTMUB
/MTMUH
/MTSY
/MTSYB
/MTSYH
/MT-Symbol
/MTSYN
/Music
/MysticalPSMT
/NagHammadiLS
/NealCurieRuledSH
/NealCurieSH
/NebraskaPSMT
/Neuropol-Medium
/NevisonCasD
/NewMilleniumSchlbkBoldItalicSH
/NewMilleniumSchlbkBoldSH
/NewMilleniumSchlbkExptSH

72. /NewMilleniumSchlbkItalicSH
/NewMilleniumSchlbkRomanSH
/News702BT-Bold
/News702BT-Italic
/News702BT-Roman
/Newton
/NewZuricaBold
/NewZuricaItalic
/NewZuricaRegular
/NiagaraEngraved-Reg
/NiagaraSolid-Reg
/NigelSadeSH
/Nirvana
/NuptialBT-Regular
/OCRAbyBT-Regular
/OfficePlanning
/OldCentury
/OldEnglishTextMT
/Onyx
/OnyxBT-Regular
/OpenSymbol
/OttawaPSMT
/OttoMasonSH
/OzHandicraftBT-Roman
/OzzieBlack-Italic
/OzzieBlack-Regular
/PalatiaBold
/PalatiaItalic
/PalatiaRegular
/PalmSpringsPSMT
/Pamela
/PanRoman
/ParadisePSMT
/ParagonPSMT
/ParamountBold
/ParamountItalic

73. /ParamountRegular
/Parchment-Regular
/ParisianBT-Regular
/ParkAvenueBT-Regular
/Patrick
/Patriot
/PaulPutnamSH
/PcEncodingLowerSH
/PcEncodingSH
/Pegasus
/PenguinLightPSMT
/PennSilvaSH
/Percival
/PerfectRegular
/Pfn2BlackItalic
/Phantom
/PhilSimmonsSH
/Pickwick
/PipelinePlain
/Playbill
/PoorRichard-Regular
/Poster
/PosterBodoniBT-Italic
/PosterBodoniBT-Roman
/Pristina-Regular
/Proxy1
/Proxy2
/Proxy3
/Proxy4
/Proxy5
/Proxy6
/Proxy7
/Proxy8
/Proxy9
/Prx1
/Prx2

74. /Prx3
/Prx4
/Prx5
/Prx6
/Prx7
/Prx8
/Prx9
/Pythagoras
/Ranegund
/Ravie
/Ribbon131BT-Bold
/RMTMI
/RMTMIB
/RMTMIH
/RMTMUB
/RMTMUH
/RobWebsterExtraBoldSH
/Rockwell
/Rockwell-Bold
/Rockwell-ExtraBold
/Rockwell-Italic
/RomanC
/RomanD
/RomanS
/RomanT
/Romantic
/RomanticBold
/RomanticItalic
/Sahara
/SalTintorettoSH
/SamBarberInitialsSH
/SamPlimsollSH
/SansSerif
/SansSerifBold
/SansSerifBoldOblique
/SansSerifOblique

75. /Sceptre
/ScribbleRegular
/ScriptC
/ScriptHebrew
/ScriptS
/Semaphore
/SerifaBT-Black
/SerifaBT-Bold
/SerifaBT-Italic
/SerifaBT-Roman
/SerifaBT-Thin
/Sfn2Bold
/Sfn3Italic
/ShelleyAllegroBT-Regular
/ShelleyVolanteBT-Regular
/ShellyMarisSH
/SherwoodRegular
/ShlomoAleichemSH
/ShotgunBT-Regular
/ShowcardGothic-Reg
/SignatureRegular
/Signboard
/SignetRoundhandATT-Italic
/SignetRoundhand-Italic
/SignLanguage
/Signs
/Simplex
/SissyRomeoSH
/SlimStravinskySH
/SnapITC-Regular
/SnellBT-Bold
/Socket
/Sonate
/SouvenirITCbyBT-Demi
/SouvenirITCbyBT-DemiItalic
/SouvenirITCbyBT-Light

76. /SouvenirITCbyBT-LightItalic
/SpruceByingtonSH
/SPSFont1Medium
/SPSFont2Medium
/SPSFont3Medium
/SPSFont4Medium
/SpsFont4Medium
/SPSFont5Normal
/SPSScript
/SRegular
/Staccato222BT-Regular
/StageCoachRegular
/StandoutRegular
/StarTrekNextBT-ExtraBold
/StarTrekNextPiBT-Regular
/SteamerRegular
/Stencil
/StencilBT-Regular
/Stewardson
/Stonehenge
/StopD
/Storybook
/Strict
/Strider-Regular
/StuyvesantBT-Regular
/StylusBT
/StylusRegular
/SubwayRegular
/SueVermeer4LightItalicSH
/SueVermeer4LightSH
/SueVermeer5MedItalicSH
/SueVermeer5MediumSH
/SueVermeer6DemiItalicSH
/SueVermeer6DemiSH
/SueVermeer7BoldItalicSH
/SueVermeer7BoldSH

77. /SunYatsenSH
/SuperFrench
/SuzanneQuillSH
/Swiss721-BlackObliqueSWA
/Swiss721-BlackSWA
/Swiss721BT-Black
/Swiss721BT-BlackCondensed
/Swiss721BT-BlackCondensedItalic
/Swiss721BT-BlackExtended
/Swiss721BT-BlackItalic
/Swiss721BT-BlackOutline
/Swiss721BT-Bold
/Swiss721BT-BoldCondensed
/Swiss721BT-BoldCondensedItalic
/Swiss721BT-BoldCondensedOutline
/Swiss721BT-BoldExtended
/Swiss721BT-BoldItalic
/Swiss721BT-BoldOutline
/Swiss721BT-Italic
/Swiss721BT-ItalicCondensed
/Swiss721BT-Light
/Swiss721BT-LightCondensed
/Swiss721BT-LightCondensedItalic
/Swiss721BT-LightExtended
/Swiss721BT-LightItalic
/Swiss721BT-Roman
/Swiss721BT-RomanCondensed
/Swiss721BT-RomanExtended
/Swiss721BT-Thin
/Swiss721-LightObliqueSWA
/Swiss721-LightSWA
/Swiss911BT-ExtraCompressed
/Swiss921BT-RegularA
/Syastro
/Symap
/Symath

78. /SymbolGreek
/SymbolGreek-Bold
/SymbolGreek-BoldItalic
/SymbolGreek-Italic
/SymbolGreekP
/SymbolGreekP-Bold
/SymbolGreekP-BoldItalic
/SymbolGreekP-Italic
/SymbolGreekPMono
/SymbolMT
/SymbolProportionalBT-Regular
/SymbolsAPlentySH
/Symeteo
/Symusic
/Tahoma
/Tahoma-Bold
/TahomaItalic
/TamFlanahanSH
/Technic
/TechnicalItalic
/TechnicalPlain
/TechnicBold
/TechnicLite
/Tekton-Bold
/Teletype
/TempsExptBoldSH
/TempsExptItalicSH
/TempsExptRomanSH
/TempsSwashSH
/TempusSansITC
/TessHoustonSH
/TexCatlinObliqueSH
/TexCatlinSH
/Thrust
/Times-Bold
/Times-BoldItalic

79. /Times-BoldOblique
/Times-ExtraBold
/TimesNewRomanMT-ExtraBold
/TimesNewRomanPS-BoldItalicMT
/TimesNewRomanPS-BoldMT
/TimesNewRomanPS-ItalicMT
/TimesNewRomanPSMT
/Times-Oblique
/Times-Roman
/Times-Semibold
/Times-SemiboldItalic
/TimesUnic-Bold
/TimesUnic-BoldItalic
/TimesUnic-Italic
/TimesUnic-Regular
/TonyWhiteSH
/TransCyrillic
/TransCyrillic-Bold
/TransCyrillic-BoldItalic
/TransCyrillic-Italic
/Transistor
/Transitional521BT-BoldA
/Transitional521BT-CursiveA
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/TranslitLS
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80. /Trebuchet-BoldItalic
/TrebuchetMS
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/TrebuchetMS-Italic
/TribuneBold
/TribuneItalic
/TribuneRegular
/Tristan
/TrotsLight-HMK
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/TubularRegular
/Txt
/TypoUprightBT-Regular
/UmbraBT-Regular
/UmbrellaPSMT
/UncialLS
/Unicorn
/UnicornPSMT
/Univers
/UniversalMath1BT-Regular
/Univers-Bold
/Univers-BoldItalic
/UniversCondensed
/UniversCondensed-Bold
/UniversCondensed-BoldItalic
/UniversCondensed-Italic
/UniversCondensed-Medium
/UniversCondensed-MediumItalic
/Univers-CondensedOblique
/UniversExtended-Bold
/UniversExtended-BoldItalic
/UniversExtended-Medium
/UniversExtended-MediumItalic
/Univers-Italic
/UniversityRomanBT-Regular
/UniversLightCondensed-Italic

81. /UniversLightCondensed-Regular
/Univers-Medium
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/URWWoodTypD
/USABlackPSMT
/USALightPSMT
/Vagabond
/Venetian301BT-Demi
/Venetian301BT-DemiItalic
/Venetian301BT-Italic
/Venetian301BT-Roman
/Verdana
/Verdana-Bold
/Verdana-BoldItalic
/Verdana-Italic
/VinerHandITC
/VinetaBT-Regular
/Vivaldii
/VladimirScript
/VoguePSMT
/WaldoIconsNormalA
/WaltHarringtonSH
/Webdings
/Weiland
/WesHollidaySH
/Wingdings-Regular
/WP-HebrewDavid
/XavierPlatoSH
/YuriKaySH
/ZapfChanceryITCbyBT-Bold
/ZapfChanceryITCbyBT-Medium
/ZapfDingbatsITCbyBT-Regular
/ZapfElliptical711BT-Bold
/ZapfElliptical711BT-BoldItalic
/ZapfElliptical711BT-Italic
/ZapfElliptical711BT-Roman

82. /ZapfHumanist601BT-Bold
/ZapfHumanist601BT-BoldItalic
/ZapfHumanist601BT-Italic
/ZapfHumanist601BT-Roman
/ZappedChancellorMedItalicSH
/ZurichBT-BlackExtended
/ZurichBT-Bold
/ZurichBT-BoldCondensed
/ZurichBT-BoldCondensedItalic
/ZurichBT-BoldItalic
/ZurichBT-ExtraCondensed
/ZurichBT-Italic
/ZurichBT-ItalicCondensed
/ZurichBT-Light
/ZurichBT-LightCondensed
/ZurichBT-Roman
/ZurichBT-RomanCondensed
/ZurichBT-RomanExtended
/ZurichBT-UltraBlackExtended
]
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Fantasia RESPONSE1
Chapter ten was all about the direct instruction for the students.
I was able to sit and think of aspects I would use if I was to
teach a new class of different learners. In this case what would
you do in the case of being sure that the students are
successful? In this case I will have differentiated instruction
and be sure that I am able to target each student. Before I can
start with my differentiated lessons, I will have to first get to
know the students and see what the needs of the children are. As
a teacher you must learn the strategies and methods would be
best to use with this class. Be sure to make sure you plan
effective enough to keep students’ engagements at the forefront
of the lesson. I also pulled thoughts about the lecture method

87. and when it would be appropriate to use the lecture method. As
a child growing up, I hated a teacher that would stand and make
us listen to a lecture all day every day, so now as a teacher I
know what I am expected of from my students, and I let them do
as many hands on and other activities so that I will not bore the
students out as quick as I was bored out in the past.
Chapter eleven focused on the authentic teaching method. The
main points associated with the real teaching method is primary
roles with various discussion techniques. As for teachers you
should always keep the children engaged in everything. Having
class discussions will also help you better understand as a
teacher what the students are gaining from the lesson that was
taught. Therefore, it is very important to have different class
discussions with the students in every lesson that is presented.
Another area I was able to strengthen from this chapter was the
three levels of problem solving. As for in the classroom just
always be sure to keep the students involved and engaged so
that they will be successful in all.
RSEPONSE 2 Deloris
Chapter 10: Using Teacher-Centered Teaching
Methods emphasis was on direct teaching and exposition
approaches to teaching integrated bodies of knowledge that
provide teachers with direct instructional alternatives. We as
teachers need to be responsible for implementing instructions to
our students. Planning and preparation in the classroom allow
students to stay focused and learn. In the classroom, teachers
engage in the following types of teaching methods and the
following objectives: 1- Identifies factors that should be
considered in selecting teaching techniques and strategies such
as students' ages, physical and mental characteristics, the
purpose of the lesson and content taught. 2 -Define /discuss
strengths and weakness of direct teaching and exposition
teaching approaches. 3- Describe ways to improve teachers'

88. lectures and presentations with effective questioning. 4-
Differentiate between different categories of questions. 5-
Compare and contrast focusing, prompting, and inquiring of
questions. 6-As we all have to do in the classroom, use
redirection and reinforcement as a strategy to encourage
appropriate behaviors.
Chapter 11: Using Authentic Teaching Methods- Authentic
instructional methods promote the development of students'
critical thinking and problem-solving skills and give students a
voice in the learning process. Also, authentic approaches to
instruction require a classroom atmosphere that is conducive to
active student participation and a close working relationship
between teachers and students. Active self-directed inquiring
learning requires that students want to learn. Being a Head Start
teacher, I am always using a variety of techniques to get my
students to master skills by demonstrations, individual one-on-
one lessons and hands-on activities. Through these lessons, the
students are engaged in everyday learning. As you know,
authentic teaching focuses on real-world issues and problems,
young children loved for their teachers to read books aloud and
the use of technology can make authentic learning activities
exciting and fun.
Moore. K.D. (2015). Effective Instructive Strategies From
Theory to Practice (4th ed.). Sage Publication. Inc.
Edited by Deloris B Carpenter on Mar 14 at 3:30pm
Teacher-Centered Instruction
The Rodney Dangerfield
of Social Studies
Mark C. Schug

89. During the 1970s and 1980s, a line of educational research
developed called “effective teaching.” Effective teachers we re
reported to favor research-supported practices that, when
properly
implemented in the classroom, produced stronger academic
achievement.
The name given to such instruction has varied. Terms like
“active teaching” and “explicit instruction” were used from time
to
time. Such phrases conveyed the image of teachers on their feet
in
the front of the room with eyes open, asking questions, making
points, gesturing, writing key ideas on the board, encouraging,
cor-
recting, demonstrating, and so forth. The role of the teacher was
obvious and explicit and tied to clearly identified content or
skills.
For the purposes of this paper, I use the term “teacher-centered
instruction” to refer to this approach. It implies a high degree of
teacher direction and a focus of students on academic tasks.
And it
vividly contrasts with student-centered or constructivist
approach-
es in establishing a leadership role for the teacher. Teacher
presen-
tation, demonstration, drill and practice, posing of numerous
fac-
tual questions, and immediate feedback and correction are all
key
elements.
Teacher-centered instruction has again and again proven its
value in studies that show it to be an especially effective

90. instruc-
tional method. Yet, when self-appointed education leaders meet
to
share best practices or write about effective teaching, teacher -
cen-
tered instruction, as the comedian Rodney Dangerfield used to
say,
gets no respect.
94
5
STUDENT-CENTERED INSTRUCTION
In fact, for most of the last century social studies leader s have
fought hard against the idea of teacher-centered instruction. At
nearly every opportunity—in journal articles, education
textbooks,
and speeches at professional meetings—slogans were voiced
about
teaching the child, not the subject, according to
developmentally
appropriate practices. Those who favor student-centered
approach-
es suggest that:
• “Hands-on” activities are superior to teacher-led
instruction. Projects, group work, field trips, almost any
other approach is to be preferred.
• Integrated content is superior to discipline-specific
content. The barriers between the disciplines such as
history and geography are the artificial creations of self-

91. serving academics. Integrated themes are regarded as
having greater integrity.
• Cooperative, group-learning approaches are superior to
whole group, teacher-led instruction. Students learn
best by interacting with each other rather than by
learning from adults.
• Academic content is inherently dull. Topics such as
social issues have more relevance and appeal to
students than subjects such as economics or geography.
Is there an alternative to student-centered instruction? If so,
what research supports it and how does it look in practice? Let’s
examine the often-overlooked case for teacher-centered instruc-
tion.
RESEARCH ON TEACHER-CENTERED INSTRUCTION:
DIRECT INSTRUCTION IN READING
Teacher-centered instruction derives from two lines of scholar-
ship and curriculum development (Schug, Tarver, and Western,
2001). One is associated primarily with the work of Siegfried
Engelmann and his colleagues, whose approach is widely
referred
to as “Direct Instruction” and whose research focused predomi -
TEACHER-CENTERED INTRUCTION 95
nantly on reading. The other line of scholarship is associated
pri-
marily with the work of Barak Rosenshine and his colleagues,
whose “process-outcome” research identified the teacher
practices

92. that were associated with improving student learning.
Engelmann’s work derives from close analysis of the compre-
hension and reasoning skills needed for successful student per-
formance in reading or mathematics, skills that provide the
intel-
lectual substance of the Direct Instruction programs he
developed.
In the case of reading, its substance is found in the sound
system of
spoken English and the ways in which English sounds are repre-
sented in writing—a major reason why Direct Instruction in
read-
ing is associated with phonemic awareness or phonics. But it is
not
equivalent to phonics. Direct Instruction can be used to teach
things other than phonics—mathematics and social studies, for
example—and phonics can be taught by means other than Direct
Instruction.
The detailed character of the Direct Instruction approach
developed by Englemann derives from a learning theory and a
set
of teaching practices linked to that theory. The learning theory
focuses on how children generalize from present understanding
to
understanding new examples. This theory informs the
sequencing
of classroom tasks for children and the means by which teachers
lead children through those tasks. The means include a complex
system of scripted remarks, questions, and signals to which
chil-
dren provide individual and choral responses in extended,
highly
interactive sessions. Children in Direct Instruction classrooms
also

93. do written work in workbooks or on activity sheets.
An impressive body of research over 25 years attests to the effi -
cacy of Engelmann’s model. In the most comprehensive review,
Adams and Engelmann (1996) identified 34 well-designed
studies
in which Direct Instruction interventions were compared to
other
teaching strategies. These studies reported 173 comparisons,
span-
ning the years from 1972 to 1996. The comparison yielded two
major results. First, 87 percent of posttreatment test score aver -
ages favored Direct Instruction, compared to 12 percent
favoring
other approaches. Second, 64 percent of the statistically
significant
outcomes favored Direct Instruction compared to only one
percent
WHERE DID SOCIAL STUDIES GO WRONG? 96
favoring other approaches, and 35 percent favoring neither.
A meta-analysis of data from the 34 studies also yielded large
effect sizes for Direct Instruction. Large gains were reported for
both regular and special education students, for elementary and
secondary students, and for achievement in a variety of subjects
including reading, mathematics, spelling, health, and science.
The
average effect size for the 34 studies was .87; the average effect
size
calculated for the 173 comparisons was .97. This means that
gain
scores for students in Direct Instruction groups averaged nearly

94. a
full standard deviation above those of students in comparison
groups. Effect sizes of this magnitude are rare in education
research.
TEACHER-CENTERED INSTRUCTION IN
READING AND OTHER SUBJECTS
The second line of research in teacher-centered instruction is
based on a synthesis of findings from experimental studies con-
ducted by many different scholars working independently,
mostly in
the 1980s. In these studies, teachers were trained to use specific
instructional practices. The effects of these practices on student
learning were determined by comparing similar students’
learning
in classes where the practices were not used. The synthesis
growing
out of these studies identified common “teaching functions” that
proved effective in improving student learning.
This research reached its zenith in 1986 when Rosenshine and
Robert Stevens co-authored a chapter in the Handbook of
Research on
Teaching. The chapter reviewed several empirical studies that
focused on key instructional behaviors of teachers. In several of
the
experiments, they found that effective teachers attended to inap-
propriate student behavior, maintained the attention of all stu-
dents, provided immediate feedback and evaluation, set clear
expectations, and engaged students as a group in learning.
Rosenshine and Stevens (1986) distilled the research down to a
set
of behaviors that characterize well-structured lessons. Effective
teachers, they said:

95. • Open lessons by reviewing prerequisite learning.
TEACHER-CENTERED INTRUCTION 97
• Provide a short statement of goals.
• Present new material in small steps, with student
practice after each step.
• Give clear and detailed instructions and explanations.
• Provide a high level of active practice for all students.
• Ask a large number of questions, check for
understanding, and obtain responses from all students.
• Guide students during initial practice.
• Provide systematic feedback and corrections.
• Provide explicit instruction and practice for seatwork
exercises and, where necessary, monitor students
during seatwork.
The major components of this sort of teacher-centered instruc-
tion are not all that unexpected. All teachers use some of these
behaviors some of the time, but the most effective teachers use
most of them nearly all the time.
Interest in Rosenshine’s second line of research was given an
important boost from E.D. Hirsch, Jr.’s book, The Schools We
Need &
Why We Don’t Have Them (1996). He summarized findings
from sev-
eral studies which contributed to the conclusion that teacher -
cen-
tered instruction works well in classrooms.

96. The first was a series of “process-outcome” studies conducted
from 1970 until 1973 at the University of Canterbury in New
Zealand. They showed that time spent focused on content and
the
amounts of content taught were important factors in
achievement.
Whether a lecture or questioning format was used, careful struc-
turing of content by the teacher followed by summary reviews
was
the most effective method.
In a later series of studies, Jere Brophy and his colleagues
(1973-1979) found that some teachers got consistently good
results
while others did not. They observed the teachers associated with
good and poor academic outcomes and reached at least two star-
tling conclusions—first, that teachers who produced the least
achievement used approaches that were more concerned with the
students’ self-esteem, and second, that learning progressed best
when the materials were not only new and challenging but could
WHERE DID SOCIAL STUDIES GO WRONG? 98
also be easily grasped by students. Brophy and his colleagues
also
found that the most effective teachers were likely to:
• Maintain a sustained focus on content.
• Involve all students.
• Maintain a brisk pace.
• Teach skills to the point of overlearning.
• Provide immediate feedback.
Finally, in a separate series of process-outcome studies that

97. spanned the period from the 1960s to the 1980s, Gage and his
col-
leagues at Stanford University found that effective teachers:
• Introduce materials with an overview or analogy.
• Use review and repetition.
• Praise and repeat student answers.
• Give assignments that offer practice and variety.
• Ensure questions and assignments are new and
challenging yet easy enough to allow success with
reasonable effort.
TEACHER-CENTERED INSTRUCTION IN SOCIAL STUDIES
Though research on teacher-centered instruction focuses on
the day-to-day work of teachers who favor this approach, the
rheto-
ric of leaders in social studies education fails to take note of
these
highly successful teachers. A review of recent articles in Theory
and
Research in Social Education, the flagship research journal of
the
National Council for the Social Studies and the College and
University Assembly, makes this point abundantly clear. The
authors and editor emphasize issues of social justice, race,
gender,
and class, while failing to address what are the most effective
teacher
practices. Teachers who favor teacher-centered instruction are
rarely the subjects of interviews or observation, and their
teaching
style and techniques are rarely mentioned. When such teachers
are
noticed at all by the leaders of the field, it is to use them as

98. exam-
ples of what not to do in the classroom. After all, these teachers
have rejected most of the hip, student-centered approaches.
They
TEACHER-CENTERED INTRUCTION 99
are ignored or dismissed by the self-appointed leadership
crowd—
the folks who speak at professional meetings, write the
textbooks
for teachers, and dominate professional discussion. Again,
Rodney
Dangerfield’s line might best describe such teachers. They get
no
respect!
There is some evidence that, despite the heavy emphasis placed
on student-centered techniques, many social studies teachers
might be successfully using teacher-centered instruction in the
classroom. It is hard to be certain, however, because as Cuban
(1991) observes, studies of classroom observations are rare in
social
studies. In his summary of the studies that are available, he con-
cludes that the most common pattern of social studies teaching
includes heavy emphasis on the teacher and the textbook as the
sources of information for assignments and discussion, followed
by
tests and seatwork—in other words, teacher-centered
instruction.
Whole group instruction dominates. Cuban comments that this
state of affairs seems nearly impervious to serious change. This
observation is congruent with observations made by others of
social

99. studies classrooms (Goodlad, 1984). But, if this is so, is it as
bad as
Cuban implies?
Educators who use teacher-centered approaches are generally
reluctant to use esoteric forms of instruction, and many
effective
teachers have not found success using student-centered teaching
approaches. Consider cooperative learning as an example. Its
research base is impressive in terms of its potential to achieve
aca-
demic and social outcomes (Slavin, 1990). But in practice, this
potential is rarely achieved, primarily because in order for
cooper-
ative learning to be successful, teachers must follow specific
steps,
carefully organizing the content and skills that students are to
“teach” each other. (After all, the students do not know this
mate-
rial as well as the teacher does.) They must group students care-
fully with regard to academic ability, race, and gender; place
stu-
dents in groups of four or five students with a high, a low, and
two
or three medium-achieving students in each group; and compute
student “improvement scores,” an essential component in
Slavin’s
work. In computing improvement scores, the teacher must first
compute base scores for each student and for each group of stu-
WHERE DID SOCIAL STUDIES GO WRONG? 100
dents from past quizzes and tests. They then need to administer
the test or quiz again to the class and convert the scores to

100. improve-
ment points.
Failing at any one step could jeopardize the results that had
been achieved when the approach was studied. Yet, few teachers
follow all these steps. While some choose occasional group
work,
most do not do anything resembling the cooperative learning
described in the literature—mostly because these well-
intentioned
techniques have been tried and have failed in practice. Instead,
most social studies teachers discover on their own that teacher-
cen-
tered techniques are among the best ways to improve student
learning. This happens despite the fact that cooperative learning
and similar student-centered approaches are stressed repeatedly
in
initial teacher training programs and at numerous professional
conferences and workshops. Teachers reject these approaches
because they conduct a common sense, cost benefit analysis.
The
costs of student-centered approaches are high, immediate, and
cer-
tain. The most obvious costs are additional time to prepare such
lessons and additional class time. To many teachers, the
benefits of
student-centered approaches—eventually improving student
achievement—appear to be highly uncertain and distant. As a
result, many place their faith in teacher-centered approaches.
Of course, either knowing that a classroom is student-centered
or knowing that it is teacher-centered reveals little about the
qual-
ity of instruction in the classroom. It tells nothing about the
facts
and concepts being presented, examples being used, or

101. interaction
between teacher and students. Teachers who favor teacher-cen-
tered approaches, however, tend to focus on what content to
teach,
the sequence of ideas, the examples used, the demonstrations
per-
formed, the questions asked, and the students’ responses, and
they
tend to be more interested in the details of instruction—all
central
components of effective teaching.
In any case, regardless of one’s personal preference for student-
or teacher-centered instruction, the ultimate questions should
be:
What are the results of instruction? Do students achieve more?
Under what conditions is learning enhanced? Research
consistent-
ly shows that, while student-centered instruction may work in
some
TEACHER-CENTERED INTRUCTION 101
cases, teacher-centered instruction works better with most stu-
dents and with most teachers. Unfortunately, this is precisely
what
the leaders of the field who are focused on promoting student-
cen-
tered methods ignore.
WHAT DO SOCIAL STUDIES TEACHING
METHODS BOOKS TEACH?
Though there is evidence that many teachers, parents, and

102. administrators prefer teacher-centered instruction, leaders of the
field still work overtime to push student-centered learning. In
fact,
today’s teaching methods textbooks in social studies are nearly
silent on how to develop teacher-led, teacher-centered
instruction.
Instead, the authors of these books are deeply influenced by the
progressive legacy of student-centered instruction.
Some early methods books do provide a more balanced
approach. Lee Ehman, Howard Mehlinger and John Patrick’s
(1974) book Toward Effective Instruction in Secondary Social
Studies, for
example, has some positive things to say about teacher
presenta-
tions. The index shows nine references to expository
instruction.
The book devotes 10 full pages to expository instruction, giving
advice on how to plan and deliver a good lecture. Prospective
teachers are advised to begin a lesson by explaining what
students
are expected to learn. Then they define unfamiliar ideas or
facts,
proceed in a well-organized manner, provide immediate correc-
tions to students, and close by reviewing the ideas that were
taught.
Most methods books from the latter half of the last century,
however, give short shrift to teacher-centered methods. Edgar
B.
Wesley’s 1950 book, Teaching Social Studies in High Schools,
includes
just seven references to lecture. And, though he discusses what
lectures are and explains how many social studies teachers use
“informal” lectures, the discussion is couched in his distaste for
such teacher-centered methods: “the teacher who lectures in the

103. public schools is likely to be charged with . . . cruelty to
pupils.” In
another example, Maurice P. Hunt and Lawrence E. Metcalf ’s
1968 book, Teaching High School Social Studies, includes
neither the
phrase “direct instruction” nor the word “lecture” in the index.
WHERE DID SOCIAL STUDIES GO WRONG? 102
The book is, however, filled with references to “reflective
thought”
and issues related to power, class, and race.
Additional evidence of the disproportionate emphasis on stu-
dent-centered instruction can be found in the Handbook of
Research
on Social Studies Teaching and Learning. This is regarded as a
highly
authoritative, landmark work in the field. Edited by James P.
Shaver (1931), it includes 53 chapters. These carefully selected
and meticulously edited chapters address numerous concerns in
social studies education. Yet, the index has a single reference to
direct instruction—Peter Martorella mentions it in his chapter
on
teaching concepts, devoting four paragraphs (in a book of over
600
pages) to this form of teaching. Even here, though, there is no
respect for teacher-centered instruction. Martorella summarizes
the work of Barak Rosenshine but then dismisses it. He explains
that teacher-centered instruction is only useful for low-level
cog-
nitive objectives and probably not worth employing in social
stud-
ies classrooms.

104. Perhaps most disturbing is that these are not isolated instances
of neglect. In fact, a brief review of the most widely used social
stud-
ies methods textbooks exposes a widespread disregard for direct
instruction.
• In Jack Zevin’s (2000) Social Studies for the Twenty-First
Century:
Methods and Materials for Teaching in Middle and Secondary
Schools, nei-
ther the phrase “direct instruction” nor the word “lecture”
appears
in the index. Didactic roles of teachers are described but such
roles
receive short shrift and little enthusiasm when compared to
descriptions of “reflective” and “affective” roles. Didactic
approaches are described in order to be contrasted with other,
bet-
ter approaches. Zevin never suggests how to plan and deliver
any
sort of teacher-led presentation.
• Peter H. Martorella’s (2001) Teaching Social Studies in
Middle
and Secondary Schools follows a similar pattern. Neither the
phrase
“direct instruction” nor the word “lecture” appears in the index.
Little attention is given to how such teacher-centered
instruction
might work or what research might support such an approach.
Even a short section on expository approaches turns out to
supply
scant advice on what such instruction might entail.
TEACHER-CENTERED INTRUCTION 103