=Paper=
{{Paper
|id=None
|storemode=property
|title=Reverse Logistics of Recovery and Recycling of Non-Returnable Beverage Containers in the Brewery Industry: A "Profitable Visit" Algorithm
|pdfUrl=https://ceur-ws.org/Vol-769/paper9.pdf
|volume=Vol-769
}}
==Reverse Logistics of Recovery and Recycling of Non-Returnable Beverage Containers in the Brewery Industry: A "Profitable Visit" Algorithm==
https://ceur-ws.org/Vol-769/paper9.pdf
Reverse Logistics of Recovery and Recycling of Non-
Returnable Beverage Containers in the Brewery
Industry: A “Profitable Visit” Algorithm
Mario Monsreal1,2,3
1
AutoID LAB, Zaragoza Logistics Center, Zaragoza, Spain.
2
Center of High Management in Engineering and Technology,
Anáhuac University Mexico, México City, México.
3
Latin-American Center of Logistics Innovation-México, México City, Mérida, México.
Abstract. Reverse logistics is one of industries´ activities that is still little
known and developed. This paper analyses the necessities of collecting non-
returnable packaging at the point of sale, as well as their processing and sale to
recycling companies, while considering marketing and operational variables for
the reverse scheme. The objective is to increase the quality of recycling
material by avoiding contamination and therefore, raising the quantity of
recycled material used in the production of new packages. The pilot project
analyses the operation of a brewery company in a medium-sized city in
México. A collection system for non-returnable glass bottles and cans is
designed by applying routing algorithms. Specifically a new “profitable
algorithm” based on the well-known Nearest Neighbor is proposed and
compared in order to achieve higher volume of collected material while
lowering the cost of collection.
Keywords: Reverse Logistics, Non-Returnable Packages, Recycling, Routing
algorithm
1 Introduction
Recycling of beverage packaging is a common activity in Western Europe, Japan,
Canada and the United States. Usually, non-refillable beverage bottles and cans are
returnable in order to be recycled. On the other side of the coin, in developing
countries, there are still deficiencies in the development of organized packaging
recycling systems.
This paper analyses the requirements of a reverse logistics network of non-
returnable beverage packaging in Mexico’s Brewery Industry and presents the results
of the economic analysis in the case of implementing the system in a particular city
selected for the pilot study.
The interest of recycling non-returnable packaging is a voluntary company
initiative, therefore a deposit system is not considered.
The objective is to increase the quantity of recycled material used in the production of
new packaging, while keeping recycling costs at their minimum. It allows resources
to be saved and waste to be reduced.
60
2 Reverse Logistics Network
The concept of reverse logistics has been created to respond to the necessity of
businesses to develop and/or restructure their material returns. There are different
reasons that have motivated the development of this area such as strict environmental
regulations, customer demand or economically driven opportunities to reuse products
or recycling materials [1].
There are many definitions of reverse logistics in the literature [2]. However, the
most suitable definition might consider the reverse logistics within the frame of the
logistics in general. Rubio Lacoba (2003) [3] defines the reverse logistics as “the
process of planning, developing, and efficiently controlling the flow of materials,
products, and information from the place of origin to the place of consumption in
such a way that while satisfying the consumer’s needs, the available remaining
material is managed to be reintroduced into the supply chain, obtaining an added
value and/or if not possible procuring a suitable disposal of this remaining material”.
The concept of reverse logistics is taken as a reference to analyze the current situation
in the management of non-returnable packaging and to propose an improved system.
2.1 Current Situation
Currently non-returnable packages are disposed of by consumers in the unsorted
municipal waste. At the dumpsite, they are partially recovered by “waste pickers” and
sold to recycling companies. Packaging producers buy recycled materials; however,
given their low quality, as they have already been mixed with other substances, only a
small proportion, around 20 percent of these materials can be used in the production
of new packages [4]. Figure 1 describes the material flow in the current situation.
New New
Products Products
Material Beverage Packages Distribution
Suppliers Beer Plant
Package Producer Centers
Damaged
Damaged NR
Packages
Products or
Recycled Damaged Packages Point of
Material Packages Sales
Products
High
Recycled Volume NR
Other Material Recycling Packages Intermediate
Consumers
Industries Plants Collector
Picked NR NR
Packages Packages
Dumpsite
Conventions
Existing Material Flow Existing Point
End of Flow
Fig. 1. Current situation.
61
2.2 Proposed Network
In order to increase the quality of recycling material by avoiding contamination at the
dumpsite and therefore, raising the quantity of recycled material used in the
production of new packaging; it is necessary to collect the non-returnable packages
separate from other waste. The proposed recovery and recycling network [5] (Figure
2) considers collecting the packages at the point of sale and transporting them to a
recovery center to be conditioned before sending to recyclers.
New New
Products Products
Material Beverage Packages Distribution
Suppliers Beer Plant
Package Producer Centers
Damaged
Packages
Recycled Damaged Damaged NR NR Packages Point of
Products or to be Recycled
Material Packages Sales
Packages
Products
High Volume
Recycled NR Packages
Other Material Recycling (Glass, Al) Recovery
Consumers
Industries Plants Center NR
Waste e.g. Packages to
paper, metals be Recycled
Dumpsite
Conventions
Existing Material Flow Existing Point
Proposed NR Packaging Flow Non-existing Point, inherent to
the design of the reverse network
End of Flow
Fig. 2. Proposed recovery and recycling network.
3 Pilot Study
3.1 Delimitation
The pilot project analyses a medium-sized city in México, which was selected as one
of the cities in the Mexican Republic with the highest consumption of beverages in
returnable containers. This city is placed in the fifth position of consumption by
volume and in which the company has the largest market share. [6] The pilot project
analyzes the recycling of non-returnable packaging in the brewery industry. In this
case, non-returnable glass bottles and aluminum cans (see figure 3).
62
G e o g ra - C it y S ta te R e g io n a l N a tio n a l
p h ic a lly e .g . M e r id a
In s titu tio n B re w e r y M u lti - c o m p a n y A n o t h e r c o m p a n ie s
F E M S A F E M S A a n d /o r in d u s tr ie s
P a c k a g e s N R G la s s A lu m in u m P E T
M a te r ia l B o t t le s C a n s
C o lle c tio n R e -u s e R e c y c lin g o f M a te r ia l in :
O b je c t iv e
P r o d u c tio n o f P r o d u c tio n
n e w p a c k a g e s o th e r p ro d u c ts
Fig. 3. Delimitation of the Pilot Project.
Figure 4 represents the proportion of non-returnable packaging in the year of the
study and figure 5 shows the growth projections until the year 2015.
S a le s P r o p o r tio n o f B e e r P a c k a g e s in C C M M e r id a
in Y e a r 2 0 0 5
R e tu rn a b le G la s s
B o ttle s
83%
N o n -r e tu rn a b le
N o n - re tu r n a b le
G la s s B o ttle s
A lu m in u m C a n s
3%
14%
Fig. 4. Proportion of beer packaging in year of the study.
63
Fig. 5. Growth projections in non-returnable packaging.
3.2 Collection and Routing
One of the determining factors in the reverse logistics network is to ensure a
sufficient return volume that will guarantee a continuous flow of materials in the
recovery and recycling network. The collection in a reverse logistics system has two
basic objectives [1]:
1. The effective acquisition of products or materials from used material
generators or clients, involves offering convenient service and consistent
timing as well as considering the processes in which the products or materials
will be transformed and incorporated to determine how materials should be
handled during collection.
2. To operate the collection and transport in an efficient form from the cost
perspective. The need for temporary storage of product accumulation after
collection, transport volume, separation at the source, and the characteristics
of special transport vehicles should be considered in order to facilitate this
objective.
Figure 6 represents the principle aspects and some of the possible configurations of
collection and transport in reverse logistics systems. The dotted line points out the
configuration that was assumed for the first routing algorithm.
64
CC rr i itt ee rr iiaa PP oo ss ss iibb i il liitt i iee ss ff oo rr
ee aa cc hh cc rr iitt ee rr iiaa
L e v e l o f S o u rc e -s e p a ra te d S o u rc e -s e p a ra te d In te g r a tin g
C o m b in a t io n flo w s o f g o o d s a r e f lo w s o f g o o d s a r e d is t r ib u t io n
in t h e c o lle c te d c o lle c te d b y a n d c o lle c t io n
C o lle c t io n s im u lta n e o u s ly d iff e r e n t v e h ic le s
C o lle c tio n O n - s ite c o lle c tio n U n m a n n e d S ta ffe d a n d
In fra s tru c tu re ( b y t h e c lie n t ) d r o p - o ff s ite s s m a rt d ro p -o ff
s it e s
C o lle c t io n P e r io d ic B y m o n it o r in g D e f in e d b y th e T r ig g e r e d b y
P o lic y s c h e d u le s d e m a n d c lie n t e .g . c a ll a d is t r ib u t io n
s e r v ic e s c h e d u le
V e h ic le T y p e C o m p a rtm e n t C o m p a c t in g N e w D e s ig n o f
m e c h a n is m V e h ic le
C o n fig u r a tio n C o n s id e r e d fo r A lg o r ith m I
Fig. 6. Configuration considered for the first and second routing algorithm.
3.3 Routing Planning - Algorithm I
The basic approach for route models is known as route problems for vehicle with
limited capacity (Capacitated Vehicle Routing Problem – CVRP). This model is
known in mathematics as NP-hard or of difficult solution when it increases the
number of sites that should be visited. Therefore, only small and medium instances of
the problem can be solved optimally. For this reason, one resorts to the use of
powerful heuristic algorithms that will find a good solution. Our problem is based on
the CVRP but with additional constraints. It requires that each vehicle performs
multiple trips while complying with a time window, i.e. a workday period. Therefore,
our problem is known as the CVRP with Multiple trips with time Windows or
CVRPMTW.
First, one uses the nearest neighbor algorithm to calculate an initial solution. In
this first run of the algorithm, the vehicle is taken to the closest client. Consecutively,
it goes to the closest neighbor revising each time not to exceed the maximum capacity
of the transport vehicle nor the maximum route time including the time to return to
the origin. The initial solution is improved by applying the shift and route reduction
algorithm presented by Schultze and Fahle [7] known as Vehicle Routing Problem
with Time Window Constraints – VRPTW. Figure 7 describes the information flow
in the algorithm to attain the collection routes and their cost.
65
Client location (Latitude,
longitude) Start
Return quantity per client
and material (glass,
aluminum)
Service time per container Read information
Average transport speed
Fix route cost
Service and transport cost
per time unit Generate routes First Solution
Maximum transport with the “nearest Routes generated
capacity according to the
neighbor“
Maximum route time shortest distance
Is there any
No route with 3 or
less clients?
Yes
**Route reduction
Improved Routes
**Switch clients to Reduced number
other routes to of routes or
reduce time Reduced total
routing time
Total routing cost
Is the time
No reduction less
than 1 minute?
Yes Conventions
Start / End
End Action
Decision
** Schulze, J.; Fahle T.: A parallel algorithm for the vehicle Data
routing problem with time window constraints. Annals of
Operations Research 86 585?607, 1999.
Fig. 7. Algorithm I used to plan the collection routes.
The results define the routes with service time, time of transit, and their respective
costs, taking into account the possibility to group routes (i.e. Multiple trips). The
maximum operation time for a vehicle was 7 hours a day (420 minutes/day). The
algorithm was programmed in Microsoft VisualBasicTM and was executed from
ExcelTM.
66
3.4 Results - Algorithm I
In total, 1688 clients were analyzed that acquired products in non-returnable
packaging in the city of Merida. A 10% rate of package recovery was assumed.
Clients were classified into two groups according to their monthly contribution:
Clients that contributed a monthly 10% or more of the transporter vehicle capacity,
were assigned one visit a week. There were 448 clients in this group. The rest of the
clients, 1240, were visited once every two weeks.
Table 1 represents the results of applying the algorithm to the set of all the clients
that are visited weekly, as well as sub-groups of these clients classified in four
quadrants according to their location.
Table 1. Result of the algorithm for weekly collection.
Weekly Visit (Clients with monthly return >= 10% vehicle capacity)
Total Clients Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4
448 178 Clients 24 Clients 22 Clients 224 Clients
No. Total Time No. Total Time No. Total Time No. Total Time No. Total Time
Routes (min.) Routes (min.) Routes (min.) Routes (min.) Routes (min.)
Algorithm Solution 33 3.732,56 13 1.344,32 2 387,97 2 398,02 15 1.490,59
Number of grouped routes 10 4 1 1 4
(Assuming operation time = 420 min/day)
Result: It is necessary to operate 2 vehicles five days in a week
The total time of the routes for all of the clients (3.732,56 min) is slightly greater than
the sum of route time for the four quadrants (3.620,90 min). The number of routes
grouped (10) is the same as adding the number of grouping routes of the quadrants.
One can conclude that it is necessary to have 2 transporter vehicles operating five
days a week to cover all weekly routes of non-returnable packaging. Figure 8
illustrates the routes on the city map.
67
Fig. 8. Graphed routes per sectors on the city map.
Table 2 represents the results for clients who were visited once every two weeks. In
this case, the route time for all clients (3.073,70 min) is slightly less than the sum of
the route time for the 4 quadrants (3.162,73 min). The number of grouped routes is 8
for all clients and 10 for the sum of each quadrant. The results obtained by applying
the algorithm to all clients are slightly better than the results obtained by applying it
separately to each quadrant. The number of grouped routes is eight, therefore one
vehicle can sufficiently cover in an 8 day period (one day per route) the routes for the
collection of non-returnable packaging of clients that are visited every two weeks.
68
Table 2. Result of the algorithm for bi-weekly collection.
Biweekly Visit (Clients with monthly return < 10% vehicle capacity)
Total Clients Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4
1240 389 Clientes 106 Clientes 103 Clientes 642 Clientes
No. Total Time No. Total Time No. Total Time No. Total Time No. Total Time
Routes (min.) Routes (min.) Routes (min.) Routes (min.) Routes (min.)
Algorithm Solution 16 3.073,70 5 833,29 1 332,10 2 547,61 9 1.449,73
Number of grouped routes 8 3 1 2 4
(Assuming operation time = 420 min/day)
Result: It is necessary to operate 1 vehicle 8 days within two weeks
3.5 Algorithm II
The second algorithm, although very similar to the first, in the beginning of the flow,
has two substantial differences. The first is the profitability variable that, through a
logical flow, outputs two possible values, 0 or 1, or “inactive”, “active” respectively.
This determines if the visit to the specific client being evaluated is profitable in terms
of a specified threshold which can be in terms of cost. If indeed it is, so then a visit to
this client is granted by the algorithm; note that costs (and thus profit) incurred
(provided) by the visit are related to the material volume and traveling distance to the
specific client. Therefore this algorithm assures the efficiency of each visit leveraging
distance and volume of material to be picked up. The second difference is the
automatic visit frequency allocation of clients based on profitability variable and/or
route saturation. This cycle can determine the direction of the visit frequency in
which the client should be moved (higher or lower), and of course, whether it should
be moved in order to purge and balance the initial solution. Figure 9 shows the main
flow of algorithm II and figure 10 represents the flow to decide the profit variable.
69
Inputs Start Outputs
Read information
•Clients location
(coordinates)
•Return quantity
Client clasification by
per client and material
quadrant taking as the
•Transport capacity
origin (0,0) the
•Times (trip, service, load
recopilation and
and unload)
selection center
•Operation costs (salaries,
comissions, maintenance,
fuel)
Generate routes with the
“nearest neighbor” in
quadrant X, with visit
frequency Y and taking
restrictions of capacity,
time and visit profitability
varible
Are there clients with Move the clients with
main visit variable = 0 Yes main visit variable = 0
or routes without or clients in
saturation of capacity or unsaturated routes to a
time?
lower frequency visit
Conventions No
Start / End
Action Is there
routes with First solution
No
Decision
only one quadrant X and visit
Data client? frequency Y
Yes
Does the
client No Move the client to a
exceeds higher frequency
route visit
capacity?
Yes
Fig. 9. Algorithm II used to plan the collection routes.
70
Calculation of the
From Main Process equilibrium point of visit
profitability
Calculation of distances
Displacement of
and times (trip, service
the centroid to the
and return) of every client
origin (0,0)
from the center (origin)
Calculation of visits´ costs
from the center (origin)
Identification of the
nearest client to the origin Client discarded
(0,0) in quadrant X for this iteration
(nearest neighbor)
Is the
“profitable No
visit” variable
activated?
Yes
Inclusion of the client in
the visit sequence of rout
Z, and eliminated from
Conventions the master DB
Start / End
Action
Is the route
Decision saturated in Yes Publication
capacity or or route Z
Data
time?
No
Displacement from the Are still Yes
origin to the centroid available
(coordinates) of the last clients on
client in the visit sequence quadrant X on
of route Z the master
DB?
Recalculation of distances
of every available No
costumer from the new To Main Process
centroid (new
coordinates)
Fig. 10. Flow profitability variable in algorithm II.
3.6 Results - Algorithm II
71
As in algorithm I, 1688 clients, with non returnable product sales, were evaluated,
also considering a 10% sales collection volume. As can be observed in Table 3 only
1225 clients had profitable visit values, and therefore an assigned visit. Nine grouped
routes and a total time of 3100.09 min. is much less time than a weekly visit scenario
for algorithm I. With the second algorithm there are no higher or lower visit
frequencies. In this case, the remaining clients presenting non profitable values are
not visited. However, the collection amount was 97.4% of the amount collected by
algorithm I.
Table 3. Results algorithm II.
10% Collection Percentage
Weekly Visit
Quadrant 1 Quadrant 2 Quadrant 3 Quadrant 4
All Clients 1225
429 Clients 73 Clients 53 Clients 670 Clients
Routes Time (min.) Routes Time (min.) Routes Time (min.) Routes Time (min.) Routes Time (min.)
First Solution
37 3,100.09 15 1,187.79 2 286.19 2 192.68 18 1,433.43
"nearest neighbor"
Grouped routes number (Assumming 9 3 1 1 4
operation time = 420 min/day)
3.7 Reconditioning
Collected non-returnable beverage packaging is transported to a centralized recovery
center. At the recovery center, materials are prepared for shipment to a recycler. Glass
is sorted according to color; the paper label is removed, and finally, the glass is
crushed. Aluminum cans are compacted into bales in order to increase transportation
efficiency.
The end material has less contamination and obtains a higher price when sold to
recyclers, who take care of the purification process.
Bales of aluminum cans can be sold directly to processing facilities. At the processing
facility they are shredded, crushed, discolored, melted down and cast into ingots. The
ingots are fed into rolling mills that reduce the thickness of the metal from 20-plus
inches to a sheet of about 10/1,000 of an inch thick. This metal is then coiled and
shipped to can manufacturers where they are turned into new cans.
Crushed glass is sold to glass recyclers where contaminants are removed; the
glass is washed and crushed into small pieces in order to have a clean cullet [8]. This
cullet is sold to container manufacturers where it is mixed with virgin material and
fed into a furnace. The resulting molten glass is drawn from the furnace and
channeled through a feeder into the bottle-making machines.
3.8 Aluminum Can Recycling
Aluminum is a metallic material that can be recycled and re-used as often as
necessary without any representative loss in quality. The high value of the metal is
maintained and offers a sufficient economic incentive for the metal to actually be
72
collected, treated, melted and used again in a similar or comparable way at the end of
the product’s service life [9].
The alloy used to produce an aluminum can sheet is a precise mixture which
includes primarily manganese and magnesium. The recycling of the material should
be done with similar alloyed materials and free of contaminants. Aluminum recyclers
have defined quality levels for accepting recycling material [9].
If a can is not recycled, it will take around 500 years to degrade. In the same way, a
recycled can may save 95% of the needed energy to produce a new can and will
support the conservation of the mineral bauxite. Recycled aluminum is most often
used for the production of new beverage containers, components for the automobile
and aerospace industries, and building materials such as windows frames and rain
gutters [10].
The aluminum can has many advantages as beverage packaging: it requires less
energy to cool, there is no danger of crushed packaging, less space is required for
empty packaging and an empty can only weighs one twentieth of an empty glass
bottle [11].
3.9 Glass Recycling
Glass is manufactured from a mixture of three main ingredients: sand, soda ash,
limestone and other additives, which create the color of the glass. In order to make
recycled glass competitive with virgin material it is important that the glass scrap
feedstock is of high quality in terms of color separation and low contamination.
Recycled glass can replace virgin materials by up to 100 percent in the manufacture
of new glass bottles and jars, depending on the quality, or can be used for a variety of
other purposes such as a blasting abrasive [12], production of fiberglass insulation,
decorative glassware, ceramic goods, and a roadbed aggregate [10].
Currently, FEMSA Beverage Packaging is able to re-use only 30 percent on
average of recycled glass in the production of new bottles due to the quality of the
reclaimed scrap glass, called “cullet”.
The substitution of recycled glass instead of virgin materials enables bottle
manufacturers to operate at lower furnace temperatures and improve emission
characteristics e.g. nine gallons of fuel oil are saved for each ton of glass that is made
from recycled cullet instead of virgin materials [10]. Recycling one ton of glass into
new bottles and jars saves 315 kg of tons of CO2 compared to using raw materials
taking into account all the raw material extraction, processing, and transport energy
used [13].
4 Summary and Outlook
A concept for recovering and recycling non-returnable beverage packaging was
developed. First, the reverse logistics network was defined according to the current
situation and the proposed packaging reverse flow. Second, the packaging collection
was planned using routing algorithms in order to identify how it can be carried out
73
and the involved cost. Subsequently, required processes at the recovery center are
analyzed for conditioning the materials before sending to the recycler.
As per the routing algorithms a new profitable routing algorithm based on the
nearest neighbor was proposed and tested. This algorithm showed substantial
advantages. First it takes into account the cost of arcs and nodes (traveling distances
and service times), as well as automatically determines the visit frequency for each
client. Also, it evaluates whether a visit should be granted or not based on its
“profitability”. The latter is a relevant feature for reverse logistic schemes since these
types of schemes have a rather high amount of uncertainty. Due to this mentioned
uncertainty an algorithm that assures that each visit of the route is profitable
(including its return to the depot) ensures that even if the circuit is broken at any
moment and the vehicle forced to return to its point of origin (depot), the company
will not lose money or even economic profit. This is not the case with some other
algorithms based on complete cycle evaluations or without the profitable visit
decision.
Further development of the reverse logistics network configuration includes the
classification of the point of sales according to the probability that consumers take
back non-returnable packages. In this sense, e.g. bars and restaurants where
consumers drink the product in-site will have a higher recovery rate than
supermarkets.
From a social point of view, currently “waste pickers” make their income by
sorting the waste at the dumpsite and selling the material to recycling companies. It is
necessary to offer an alternative to relocate these people to other jobs, for example,
some of them could work at the recovery center.
Acknowledgments
I extend my sincere thanks to Professor Mónica Vanegas from Technical University
of Berlin, Institute for Machine Tools and Factory Management, Department
Assembly Technology and Factory Management for her invaluable contribution. Also
to the southeast region logistics department of the brewery “Cuauhtémoc-
Moctezuma” for their interest and cooperation in carrying out this project, as well as
the graduate college "Stochastic Modeling and Quantitative Analysis of Complex
Systems in Engineering" sponsored by the German Research Foundation (DFG) for
the scientific support needed in the analysis and evaluation of this research.
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