Stanley Lima
email: stanley@winplace.com.br

Renato Santos

Sidnei Alves

De Araujo

Pedro Schimit

Stanley Jefferson

Araujo Lima

Renato Alessandro

Rocha Santos

Sidnei Alves De Schimit

Pedro Henrique Triguis

Combining Genetic Algorithm with Constructive and Refinement Heuristics for Solving the Capacitated Vehicle Routing Problem

Keywords: Capacitated vehicle routing problem, Heuristics, Genetic algorithm, Gillett & Miller, Hill climbing

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The optimization of production processes in the industrial environment has been an object of study and research in various areas of knowledge, such as business management, economics, logistics, among others, being most notably studied in production engineering [START_REF] Kunnathur | Dynamic Rescheduling Using a Simulation-Based Expert System[END_REF] [START_REF] Heinonen | Hybrid ant Colony Optimization and Visibility Studies Applied to a Job-Shop Scheduling Problem[END_REF]. In logistics, for example, the distribution of products and routing of vehicles are gaining space and becoming competitive advantages for companies. To reduce the delivery time, increasing the availability of products and inputs, it is mandatory to make deliveries respecting the deadlines. Nevertheless, the reduction of costs associated with the vehicles routing activity becomes an important tool for effective planning of products distribution.

The Vehicle Routing Problem (VRP) has attracted, in the last years, an increasing attention of researchers due to the great difficulty of its solution and its presence in many practical applications [START_REF] Cordeau | Chapter 7 Transportation on Demand[END_REF]. As consequence, there has been great effort to develop robust algorithms that can be modeled according to the scenario that describes a determined situation.

The Capacitated Vehicle Routing Problem is a variant of VRP and consists, basically, in determining the routes to be followed by a fleet of homogeneous vehicles, to serve a given number of customers, without violating the capacities of the vehicles [START_REF] Xu | Solving a Practical Pickup and Delivery Problem[END_REF]. The CVRP belongs to the class of NP-hard problems, that is, problems whose the solution usually requires non-polynomial complexity time algorithms and because of this are usually solved with the use of heuristic and metaheuristics algorithms such as Gillett & Miller [START_REF] Gillett | A Heuristic Algorithm for the Vehicle-Dispatch Problem[END_REF], Clarke and Wright [START_REF] Laporte | The Vehicle Routing Problem: an Overview of Exact and Approximate Algorithms[END_REF] [7], Tabu Search [START_REF] Laporte | The Vehicle Routing Problem: an Overview of Exact and Approximate Algorithms[END_REF] [8], Simulated Annealing [START_REF] Laporte | The Vehicle Routing Problem: an Overview of Exact and Approximate Algorithms[END_REF] and Genetic Algorithms [9] [10].

This work presents a strategy for CVRP optimization using Genetic Algorithms combined with two heuristics: Gillett & Miller and Hill Climbing. The first one is used to generate solutions that are included in the initial population of GA, starting it with some feasible solutions while the second is responsible for making a refinement of the solutions generated by the GA, after a certain number of generations without improvement. It is worth mentioning that among the works available in the literature, we did not find approaches using the constructive and refinement heuristics considered in this work combined with GA for solving CVPR.

The performed experiments showed that the proposed strategy presented good results for CVRP solution, confirming that the use of the employed heuristics helps the GA to converge on promising points in the search space with a smaller number of generations.

2 Theoretical Background

Capacitated Vehicle Routing Problem

The CVRP is the classic version of VRP in which all customers have their demands previously defined and that must be attended entirely by a only vehicle, the fleet is homogeneous, in other words, all vehicles are similar and run from only distribution center. In this problem, just the vehicle capacity restriction is imposed [START_REF] Laporte | The Vehicle Routing Problem: an Overview of Exact and Approximate Algorithms[END_REF]. This restriction determines that the sum of the demand of all customers of a route does not exceed the capacity of vehicle used to execute that route.

Let G=(V, A) be a graph in which V=0.....,n is the set of vertices that represent the customers and A the set of edges, representing the connections among the customers and the distribution center.

Each edge (vi,vj) is associated with a cost, Cij, representing the cost of the connection between the vertices i and j. When Cij = Cji, the problem is recognized as symmetrical, otherwise the problem is identified as asymmetrical. A set of K identical vehicles with capacity cv is allocated to the distribution center. For each customer vi a demand di is associated, and for the distribution center is defined d 0 = 0.

In summary, the CVRP consists of finding a set of routes, where each route is traveled by a vehicle, with the objective of minimizing the total cost of the routes (tc), respecting the following restrictions: (i) Each route must start and finish at the distribution center; (ii) Each customer must be visited just only time and (iii) The sum of the customers' demands included in a route can not exceed the vehicle's capacity. The mathematical formulation of CVRP, adapted from Vieira [START_REF] Vieira | Metaheuristica para a Soluc ¸ão de Problemas de Roteamento de Veículos com Janela de Tempo[END_REF], is expressed as follows:

Minimize tc = nc i=0 nc j = 0 j i K k=1 c ij x ijk (1)
Subject to

K k=1 nc j = 1 x 0jk ≤ K (2) nc j=1 x 0jk = nc j = 1 x j0k = 1, k = 1, ..., K (3)
K k=1 nc j=0 x ijk = 1, i = 1, ..., nc (4) nc j=0 x ijk - nc j=0 x ijk = 0, k = 1, ..., K i = 1, ..., nc (5)
K k=1 i∈S j∈S x ijk ≤ |S| -v(S), ∀S ⊆ V \ {0}, |S| ≥ 2 (6) nc i=1 d i nc j = 0 j i x ijk ≤ cv, k = 1, ..., K (7)
x ijk ∈ {0, 1}, i = 1, ..., nc, j = 1, ..., nc, k = 1, ..., K

where: di: demand of customer i; k: vehicle; K: set of vehicles; S: Set of customers; nc: number of customers; v(S): Minimum number of vehicles to attend S; cv: Capacity of vehicles; cij: cost of the connection from vertex i to vertex j; tc: is the total cost of routes (Equation 1); xijk: connection from the vertex i to vertex j with vehicle k; Equation 2 ensures that K vehicles will be used starting from the distribution center, while the Equation 3 guarantees that each route has its beginning and ending at the distribution center. Equation 4 defines that customers must be attended exactly one time and the equation 5 keeps the flow ensuring that the vehicle arrives at a customer and out of it, preventing that a route ends prematurely. The Equation 6 prevents the formulation of routes which do not include the distribution center. In this restriction, v(S) represents the minimum number of vehicles required to attend a set of customers S.

To ensure that the number of vehicles used to attend the set customers of S is not less than v(S), the restriction 6 establishes, indirectly, that the capacity of the vehicle is not exceeded. However to let explicit, the Equation 7 is used to formulate the capacity restriction. The Equation 8 is related to the dimensions and allowed values of matrix x.

The Equation 9 is used to evaluate the solutions generated by the proposed strategy. It reflects the value of the objective function (OF) or fitness and involves the number of vehicles used in the solution, the violated restrictions (Equations 2 to 7) and the total cost of routes.

OF = f itness = (K * W v ) + (nr * W r ) + tc (9)
where: Wv is the weight assigned to the number of vehicles used in the solution; nr is the Number of violated restrictions and Wr is the weight given to the violated re-strictions.

Heuristics and Metaheuristic Techniques Employed by Proposed Approach

Genetic Algorithms (GA) are probabilistic search methods that are based on the evolutionary process, in other words, based on the theory of species evolution proposed by Charles Darwin [START_REF] Bjarnad Óttir | Solving the Vehicle Routing Problem with Genetic Algorithms[END_REF]. GA present the following operational steps: starts a population containing a predefined number of individuals, in which each individual represents a possible solution. For each individual is attributed a value of aptitude or fitness, in our case represented by Equation 9, which indicates how good is the individual in relation to the population. Thereby, individuals are selected based on their aptitudes and pass their characteristics to the next generation. To this end, it is applied the crossover and mutation methods between individuals, aiming the development of a new generation with fittest individuals (best solutions). This iterative process is repeated until the stopping criterion is reached [START_REF] Holland | Genetic Algorithms in Search, Optimization and Machine Learning[END_REF]. The algorithm of Gillett & Miller [START_REF] Gillett | A Heuristic Algorithm for the Vehicle-Dispatch Problem[END_REF] is based on the notion of economies and is part of the group of two stages heuristics (or constructive heuristics). The strategy consists in dividing the problem in two steps to solve it. In the first step, the vertices (points of demands) are grouped according to some criterion of proximity. Then, the routes are obtained by solution of CVRP for each of the formed groups of vertices [START_REF] Goldbarg | Otimizac ¸ão Combinat ória e Programac ¸ão Linear: Modelos e Algoritmos[END_REF].

The Hill Climbing algorithm is a local search technique based on depth [START_REF] Engelbrecht | Fundamentals of Computational Swarm Intelligence[END_REF]. It adjusts the position of a candidate solution (state) only if the new position (neighbor) is better than the current state. In other words, the optimization of this algorithm occurs by evaluating neighboring solutions of current state, based on their objective function values. At each step, the solution generated by the current state is replaced by the solution represented by the best neighbor and the algorithm terminates when it reaches a peak (or a valley when it is trying to minimize a fitness function), that is, when it did not find better solution in the neighborhood [START_REF] Russell | A Modern Approach[END_REF]. Due to the characteristics of this algorithm, it has been used for refining of solutions generated by metaheuristics, in solving several problems. In additon, when this algorithm is applied in a minimization problem, as CVPR addressed in this work, it is also referred as "downhill".

Methodology

For development of proposed strategy, the programming language C/C++ and GAlib library 1 were used. The GAlib is a free library widely used in solving combinatorial optimization problems. To evaluate the strategy, experiments were performed and the results obtained were compared with the best results found in the literature, for the sets of instances from Christofides [START_REF] Christofides | Vehicle Routing. A Guided Tour of Combinatorial Optimization[END_REF] and TSPLIB2 with up to 30 customers. In the experiments, the following hardware configurations were used: Intel Celeron 2955U, 1.40 GHz processor; 4 GB of RAM; operating system Windows 7 Ultimate 32-bits.

Proposed Strategy for CVRP Optimization

In the proposed strategy (illustrated in Fig. 1), it was decided to represent the chromosome by a binary matrix with dimensions nc x nc x K, in which rows represent the number of customers to be visited, columns represent the order that customers will be visited and K, the number of vehicles (fleet). A chromosome is a solution contemplating customers information which will be attended by some vehicle, including customer visiting order. This representation facilitates the control of the restrictions imposed on the problem an also the execution of GA operations such as crossover and mutation.

In the refinement process by HC algorithm, the neighborhood of a current state (solution) is generated by shifting the elements of chromosome's matrix in a circular manner along the two dimensions indicated by nc (rows/columns), considering four directions (up, down, left and right). The parameters used by GA were defined (empirically) as follows: Chromosome encoding=Binary Matrix; Population Size=1200; Population subset for refinement (R) =1/3 of population; Number of generations without improvement (gw)=30; Number of Generations (used as stop criterion)=5000; Population rate of replacement=80%; Elitism=20%; Crossover=80%; Selection Method=Roulette; Rate of Mutation= 1%; Type of Mutation=Flip Bit.

Results and Discussion

In the evaluation of the proposed strategy, we performed ten tests for each of 16 considered instances and the results obtained were compared with the best solutions in the literature. For instances of Christofides [START_REF] Christofides | Vehicle Routing. A Guided Tour of Combinatorial Optimization[END_REF], the optimal solutions presented by Reinelt and Wenger [START_REF] Reinelt | Maximally Violated Mod-p Cuts for the Capacitated Vehicle-Routing Problem[END_REF] were considered and for TSPLIB instances, the optimal solutions indicated by Ralphs et al. [START_REF] Ralphs | On Capacitated Vehicle Routing[END_REF] were adopted.

To evaluate the goodness of the solutions obtained, a measure known in the literature as GAP was used. In our case, GAP=(OF Med -OF Best) OF Best, being OF Med the average of the objective function (OF) values obtained in the 10 tests (using Equation 9) and OF best the OF value of the best solution found in the literature. In addition, the standard deviation of OF values (OF σ ), the execution time and the best solution (OF*) obtained by our strategy, for each instance, are presented in Table 1. Nevertheless, we present in Table 2 As can be seen in Table 1, the GAP in most cases (exception of instances "E-n13-k4", and "E-n23-k3") remained less than 10% and, for 38% of instances, the strategy found the best known solution (OF Best). In addition, for 69% of instances the obtained GAP was less or equal than 5%.

The Table 1 also shows that the average GAP considering the 16 instances did not exceed 5%, showing the good performance of the strategy with respect to the quality of the solutions. It is also possible to observe that the standard deviation (OF σ ) in 81% of cases is less than 10 and, in 50% of the cases, it was obtained OF σ less than 5, indi-cating good stability strategy with respect to the exploitation of the search space. Concerning the computational cost, the processing time ranges from 80 seconds to smaller instance (Eil7) to 250 seconds for larger instances (Eil30 and E-n30-k3). Thus, this strategy showed the relatively low computational cost. However, this processing time could be improved minimizing the sparsity of chromosome representation.

Table 2. Results of the experimental strategy with and without the application of heuristics.

Source Instance OF Best GM GA GA + GM GA + HC GA + GM + HC TSPLIB Table 2 shows a comparison of the results obtained by the proposed strategy with and without the application of considered heuristics (GM and HC). Note that, in general, the results are better when these heuristics are embedded in GA. These results suggest that the use of heuristics Gillett & Miller and Hill Climbing helps the GA to converge for promising points in the search space, creating solutions with good quality.

Conclusions and Outlook

In this work, a hybrid strategy for solving the Capacitated Vehicle Routing Problem (CVRP) using Genetic Algorithms (GA) combined with the heuristics of Gillett & Miller (GM) and Hill Climbing (HC) was presented. From the computational experiments, it was possible to conclude that the proposed strategy achieved good results with re-spect to quality of solutions with low computational cost, considering the instances extracted from Christofides and TSPLIB libraries. The experiments also demonstrate that the use of heuristics Gillett & Miller and Hill Climbing helped the GA to converge on promising points in the search space with a smaller number of generations.

Fig. 1 .

 1 Fig. 1. Sequence of operations employed by the proposed strategy.

Table 1 .

 1 the results of proposed strategy evaluation, with and without the use of heuristics Gillett & Miller and Hill Climbing were performed in a combined way, as follows: GA+GM+HC=Genetic Algorithms + Gillett & Miller + Hill Climbing; GA+GM=Genetic Algorithms + Gillett & Miller; GA+HC=Genetic Algorithms + Hill Climbing; GM=only Gillett & Miller; GA=Only Genetic Algorithms (without the use of heuristics). Results of experiments with the proposed strategy.

	Source	Instance	nc K OF Best	Proposed strategy OF* OF Med GAP % OFs Time (s)
		Eil7	7 2	114	114 114	0	0	80
	TSPLIB	Eil13 Eil22 Eil23 Eil30	13 4 22 4 23 5 30 3	290 375 875 545	290 290 377 385 902 902 545 548	0 3 3 1	0 6 36 6	140 210 220 250
		P-n16-k8 16 8	450	450 464	3	8	150
		P-n19-k2 19 2	212	215 224	6	7	160
		P-n20-k2 20 2	216	220 226	5	2	190
		P-n21-k2 21 2	211	216 223	6	6	200
		P-n22-k2 22 2	216	220 235	9	11	210
	Christofides	P-n22-k8 22 8 P-n23-k8 23 8 E-n13-k4 13 4 E-n22-k4 22 4 E-n23-k3 23 3 E-n30-k3 30 3	590 529 247 375 569 534	590 590 529 544 290 290 387 390 625 649 545 549	0 3 17 4 14 3	0 3 0 4 22 4	210 220 140 200 220 250

http://lancet.mit.edu/ga/ dist/galibdoc.pdf

http://comopt.ifi.uni-heidelberg.de/ software/ TSPLIB95/vrp/

Acknowledgments

The authors would like to thank UNINOVE by financial support.


João Roberto Maiellaro
email: joao.maiellaro@fatec.sp.gov.br

João Reis

Alexandre Formigoni

Dos Robson

Marcos Santos

Celso Oliveira

Jacubavicius

João Gilberto

M Dos Reis

Robson Dos Santos

Marcos A M De Oliveira

Celso Jacubavicius

An Application of Operations Research for Reducing Fuel Costs

Keywords: Fueling, Operations Research, Cost Reduction, Gas Stations

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Consistently searching for cost reduction in all stages of the logistic process is a goal to be reached. Freight movement absorbs between one-third and two-thirds of logistics costs, and that is exactly why improving efficiency using the most of the transport equipment and personnel is one of the greatest concerns of the segment [START_REF] Ballou | Business Logistics/Supply Chain Management: Planning, Organizing, and Controlling the Supply Chain[END_REF].

In Brazil, the current scenario of inflation, economic stagnation, and poor economic growth has been causing increased fuel prices, and eventually impacting the transport companies' operating costs, as well as other services relying on fuel and oil derivatives.

Based on this scenario and on the high fuel costs observed, we attempted to strategically evaluate how to reduce these costs in order to achieve profitability without affecting the operation and customer service.

To avoid any significant impact on the increased operating costs, it is necessary to get adapted and prepare a plan while seeking lower costs at the same time. To cut down operating costs, an organization has to adapt itself to the country's economic situation and prepare its operations aiming at the lowest costs possible. The Vehicle Routing and Scheduling (VRS) method is an important tool that many organizations that have been implementing this method through software applications in order to reduce the operating costs. The organization object of this study is engaged in transportation services and uses the VRS method to support the route planning process.

The objective of this work is to detect gas stations out of the route predetermined by a transport company based both on refueling feasibility and lower fuel costs.

Methodology

Stage one consisted of detecting the problem by observing transport related operations. To this end, we used reports extracted from an automated fueling control website, as well as dynamic spreadsheets and tables that are typically used to measure KPIs (Key Performance Indicators) used to assess a driver's award (prepared by the organization).

We also used reports extracted from the ERP system. Stage two consisted of collecting data from the organization regarding the management of financial costs with fuel, organizing such data into tables, and evaluating them. We used an online fuel management system that keeps record of the amount refueled and fuel price on a realtime basis, as well as of the location of each station on the route. For stage three, we assessed prices and refueling feasibility at stations where drivers are not authorized tore fuel their vehicles. To select these new stations, we used criteria such as local topography, infrastructure, and accessibility of the station. Stage four consisted of structuring the data into a mathematical model, taking into account the total kilometers of the route, tank capacity, average kilometers run per liter, and the location and prices of the selected stations.

Case Study

The organization object of this study has been present in the Brazilian market for 75 years providing road transportation services. Organizations must focus on providing its services with excellence, efficiency, and effectiveness and meeting their customers' requirements at low operating costs, for it sees both increased earnings and reduced costs as profit [START_REF] Slack | Administração da Produção[END_REF].

One of the operating costs comes from the fleet, that is the essence of a road transport organization, thus requiring full attention and dedication. The main fleet-related costs include tires, fuel, IPVA [Motor Vehicle Ownership Tax], driver's salary, lubricants, maintenance, tolling, and overheads. In this study, we focused on the fuel factor.

Transport sector is involved in a very competitive market and besides, it has been changing due the technological development e economic [START_REF] Novaes | Gerenciamento de Transporte e Frotas[END_REF].

Among other factors, the amount of fuel to be consumed on a certain route depends on the distance, traffic, load weight, and km/l.

Defining the Problem

The problem consists of determining the best option to refuel the vehicle during the trip, taking into account the cost and distance variables and the feasibility of deviating from the route to refuel at a station where the price per liter is cheaper, due to the fact that, by failing to look at these factors, usually only the liter price alone is taken into account, while the kilometers run and respective consumption associated with the deviation are left aside.

Main aspects of a problem are the exact description of the study objectives, identification of possible decision alternatives, and confirmation of the system limitations, restrictions, and requirements [START_REF] Hillier | Introdução à Pesquisa Operacional[END_REF].

Within these aspects, we have as exact description of the study objectives the cheaper fuel costs. The possible decision alternatives would be the methods to solve this problem, while the confirmation of the limitations would be the vehicle and its capacities.

Our findings suggested that there is a need to study the causes of the high prices left aside and to propose solutions to cut down these costs. We collected the following data: routes used, load weight, vehicle model and capacity, location of each station and diesel price per liter.

The organization currently uses an automated fueling control website to determine what stations their drivers are authorized to stop by and refuel. This authorization is granted on a real-time basis, and the diesel price can be negotiated through the system. As a consequence, only the agreed-upon prices can be carried out by the parties.

We identified that many stations that are close to the routes currently used offer diesel at a more affordable price, but drivers are not authorized to refuel at these stations.

We selected the route from Campinas/SP to Catalão/GO because it has the highest diesel consumption and worst consumption performance of liter per kilometer run. The route holds over 200 trips every month. With the help of Google Maps -Figure 1 -we could see that the way to this route, through the BR-050 highway, is 601 km long.

Based on Table 1, it is concluded that a truck with full tank can run at most 392.6 km. This means that it will need to refuel during the trip. To avoid any incidents, the organization requires drivers not to use a vehicle with less than 1/4 of the tank, thus making it necessary to scheduler fueling at intervals of approximately 300 km.

The load being carried consists of automotive parts, and we considered the vehicle operating at its maximum capacity. The customer from Catalão wanted the transportation to be carried out by a Mercedes-Benz truck Axor 2644 model. Hence, it was possible to address the study as a function of the truck functional characteristics [START_REF] Brasil | [END_REF], as listed in Table 1.

The value of (t x km)/l was obtained by evaluating the fuel consumption history of this type of truck in February of 2015, where it showed a general average of t x km /liter = 1.963, according to Table 2. Based on intervals of 300 km, with the help of Google Maps, we can see that on this route, the vehicle will pass through region of Guará and Ituverava -São Paulo's inland cities, and the organization has agreements with gas stations of both these cities located on the highway. The methods described in this work were applied to the city of Guará while trying to respect the most of the interval limits.

Building the Model

To build the model, we needed details on the data used, an oriented graph, and mathematical modeling using linear programming principles.

We took infrastructure and accessibility into account to select the stations in a maximum distance of 4km from the road. Table 3 shows the data defining the actual price (AP) for each leg of the trip, i.e., the address of each station, its distance from the route (each way), and the fuel price per liter at each station. The Santo Expedito gas station is the one currently used, so there are no route deviations to get to it. The branch of Campinas starts the trip after filling up at the Frango Assado gas station, at BRL 2.999 per liter. By applying these data to the formulas, the AP can be obtained, as listed in Table 4. The linear programming model considered as the objective function the minimization of fuel cost, and constraints were related to origin and possible destinations. Constraints ensured that vehicle had only one origin for each destination and ensured that vehicle had not more than one destination for each origin on the route.

Results and Discussion

To solve the problem, it was used the software LINDO 6.1, due its nature, which consists of a minimum path problem. This group involves combinatory analysis problems, and the number of possible solutions is 5040 combinations and only one is optimal.

Station P2 showed a total cost of BRL 545.91 with fueling to the route/type of distribution being studied, road topography restrictions, bridges with height restrictions, narrow streets where big vehicles cannot running time, i.e., 4.86% less than the cost of the current station. This method had a positive impact on the decision of the best option to refuel.

In another application, the percentage reduction will subsequently vary according get past. As previously stated, this method serves to evaluate and support the decision, thus, occasionally a station with which the organization already has an agreement may lead to greater cost-effectiveness. However, we concluded that the proposed scenario, with routes from Campinas/SP to Catalão/GO, suggested a significant reduction, considering that this trip is repeated 200 times/month in average, i.e.: 200 x 573.80 = 114,760 -4.86% = 109,182.67; a reduction of over BRL 5.5 thousand.

Conclusions

This study presents a study to identify new partner ships between the organization and its potential suppliers. Therefore, this study should be extended to other routes. The model will be subsequently adapted for Road Distribution operations, where the route goes from the city of Duque de Caxias, in Rio de Janeiro State, to the city of Campina Grande, in Paraíba State, in addition to several other routes that need to be evaluated.

The study shows that, with correct evaluation, a small deviation from the route to refuel at a lower price may have a big impact on the reduction of fuel costs. The proposed method is based in a traditional Operations Research' tools in the sense that it can be adapted to different situations, to longer or shorter distances, to any demographic region, and to any type of fuel and vehicle.

Fig. 1 .

 1 Fig. 1. Route from Campinas to Catalão

Table 1 .

 1 Truck Specification: Mercedes-Benz/Axor 2644

	LOAD	TANK	MAX.	Tons X Km / l
	CAPACITY (l) LOAD (tons)
	EMPTY		200	40	2.3
	FULL		200	40	1.963
			Table 2. (t x km)/liter
	Manuf. Year	Model	Branch t x km / l Fuel
	2014		AXOR 2644 CATAL ÃO	1.99	Diesel
	2014		AXOR 2644 CATAL ÃO	2.15	Diesel
	2014		AXOR 2644 CATAL ÃO	1.75	Diesel
	2014		AXOR 2644 CATAL ÃO	1.60	Diesel
	2014		AXOR 2644 CATAL ÃO	1.97	Diesel
	2014		AXOR 2644 CATAL ÃO	2.09	Diesel
	2014		AXOR 2644 CATAL ÃO	1.86	Diesel
	2014		AXOR 2644 CATAL ÃO	1.89	Diesel
	2014		AXOR 2644 CATAL ÃO	1.95	Diesel
	2014		AXOR 2644 CATAL ÃO	2.38	Diesel

Table 3 .

 3 Adresses, distances and prices

	Trading Name Gas station	Address	Way In (km) Out (km) Way	Diesel BRL/l
	Santo Expedito Anhanguera, Km 395	0	0	2.869
	Maranatha	Voluntários de Guará, 597	2.2	2	2.699
	Guara	Duque de Caxias, 746	2.9	1.3	2.728
	Avenida	Quinze de Setembro, 31	3.1	1	2.800

Table 4 .

 4 Ap structuring

		Tank BRL Deviation	Liter		Return
	Trading	(l) per	to the	Price in AP1 to the AP2 TOTAL
	Name	liter Station the Last		Highway
			(km) Refueling		(km)
	P1 Santo Expedito 200 2.86	0	2.999	573.8	0	0.0 573.80
	P2 Maranatha	200 2.69	2.2	2.999	543.1	2	2.75 545.91
	P3 Guara	200 2.72	3.1	2.999	550.3	1.3	1.81 552.14
	P4 Avenida	200 2.80	3.4	2.999	565.1	1	1.43 566.62
						(t x km)/l	1963


Geraldo Cardoso
email: geraldo.prod@gmail.com

Oliveira Neto

Henrricco Nieves

Pujol Tucci

Luiz Fernando

Rodrigues Pinto

Ivanir Costa

Roberto Rodrigues Leite

Economic and Environmental Advantages of Rubber Recycling

Keywords: Rubber recycling, Cleaner production, Mass Intensity Factor methodology

This research aims to evaluate the economic and environmental advantages of the adoption of Cleaner Production practices in a rubber products manufacturer. The inappropriate waste disposal of rubber was an issue to be solved, so recycle this material was the way to mitigate environmental impact. The economic gain was evaluated by calculating the return over investment and the environmental benefits through the methodology of mass intensity factor. The results showed a return over investment in 17 months, however the most expressive advantage was at the environmental perspective.

Introduction

Most of the companies are oriented to generate profit, but this management style can blind them to the sustainability trends. As a consequence those companies can't keep up for a long time. Their operations have to be sustainable with initiatives such as reducing resources consumption and the reuse or recycle of materials waste, in order to get economical and environmental benefits [START_REF] Jabbour | Non-linear Pathways of Corporate Environmental Management: A Survey of ISO 14001-Certified Companies in Brazil[END_REF]. Actions focused on the reduction of the generation of waste and the consumption of energy and raw material contribute to increase productivity, efficiency and profit. Those initiatives are aligned with the Cleaner Production (CP) concept [START_REF] Almeida | Ecologia Industrial: Conceitos, Ferramentas e Aplicações[END_REF].

The Brazilian government has established a Solid Waste Policy that aims to promote the adoption of the CP practices by the companies. This policy covers actions such as reduce, reuse and recycle, as well the solid waste treatment, final waste disposal, environmental education and other guidelines [START_REF] Joinhas | Logística Reversa, Sustentabilidade e Educação[END_REF]. The CP intends to link the environmental impact reduction and the cost saving, in order to improve human health, safety and increase the process efficiency [START_REF] Limpas | [END_REF].

Environmental initiatives are not adopted by most of the companies due their old mindset, that believe that environmental practices are too complex, few efficient and don't return their investments [START_REF] Gombault | Cleaner Production in SMEs Through a Partnership with (Local) Authorities: Successes from the Netherlands[END_REF].

The literature review about rubber and the CP showed three alternatives about waste: (i) mix rubber and concrete to improve durability [START_REF] Richardson | Freeze/thaw Protection of Concrete with Optimum Rubber Crumb Content[END_REF]; (ii) mix rubber with a compound of glass and plastic [START_REF] Osmani | Innovation in Cleaner Production Through Waste Recycling in Composites[END_REF]; and (iii) mix rubber and asphalt [START_REF] Oliveira | Use of a Warm Mix Asphalt Additive to Reduce the Production Temperatures and to Improve the Performance of Asphalt Rubber Mixtures[END_REF]. The CP practices results in economical and environmental benefits and improves the image of the company [START_REF] Gombault | Cleaner Production in SMEs Through a Partnership with (Local) Authorities: Successes from the Netherlands[END_REF]. In China, half of the generated rubber waste is forwarded to automotive industry, as an alternative to recycle this material [START_REF] Liu | The Impact of the Chinese Automotive Industry: Scenarios Based on the National Environmental Goals[END_REF].

Manufacturing rubber products requires additional substances to achieve the optimal point in terms of manufacturability and composition. In order to mitigate environmental impacts, Brazilian government created laws to the companies incorporate good practices like recycling and the correct disposal of lubricants. Thus, companies have been acting to contribute on the environmental performance improvement [START_REF] Ghosh | Recycling of Silicone Rubber Waste: Effect of Ground Silicone Rubber Vulcanizate Powder on the Properties of Silicone Rubber[END_REF][START_REF] Nabil | Fatigue Life, Thermal Analysis and Morphology of Recycled Poly (ethylene terephthalate)/Commercial Fillers Hybrid Filled Natural Rubber Composites[END_REF]. Despite that, many companies consider that investments on those initiatives are a barrier on the CP practices implementation [START_REF] Van Berkel | Development of an Industrial Ecology Toolbox for the Introduction of Industrial Ecology in Enterprises-I[END_REF].

There are many papers related to the CP initiatives, however as a quantitative assessment, none measured the economic and environmental benefits. This indicates the gap to be explored in this research. Then, how to evaluate economic and environmental advantages with the adoption of the CP practices in the rubber recycling? Thus, this research aims to evaluate economic gain by calculating the Return Over Investment (ROI) and the environmental gain through the Mass Intensity Factor (MIF) methodology in a rubber products manufacturer.

Methodology

This case study was performed in a rubber products manufacturer. It has an exploratory purpose with both qualitative and quantitative approaches, and is a research strategy focused in comprehending the scenery to be analyzed. Evidences were gathered by interview, questionnaires and observations, in order to create the adequate conditions to understand the studied object of this research [START_REF] Yin | Case Study Research: Design and Methods[END_REF]. The data collected were obtained through interview with experts and through observation.

To evaluate the economic and environmental advantages with adoption of the CP practices, four steps were realised: (i)data collect; (ii)economic assessment; (iii)environmental assessment; (iv)comparison between economic and environmental gains.

In the first step, it was possible to measure the total waste and the emissions generated by the process, in order to estimate the mass balance. It was focused on the recycling of the rubber waste from the internal process and external sources through reverse logistics. In addition, it was analysed the total of saved raw material (MTE) due to the rubber recycled.

The second step is related to the economic assessment. It was calculated the Economic Gain (GE) and the period to Return On Investment (ROI), as seen in the equation 1.

ROI =

Investiment P rof it in the period

In the third step, environmental impact was measured in abiotic, biotic, water and air compartments through the Mass Intensity Factor (MIF) methodology, that considers Mass (M) and Intensity Factor (IF), as seen in the equation 2.

M IF = M ×IF (2)
After that, it was calculated the Mass Intensity per Compartment (MIC), to measure theenvironmental impactreduction per compartments: abiotic (w), biotic (x), water (y), air (z) and other (n). In the equation 3is shown how calculate MIC for (w), the same way for othercompartments.

M IC = IF W asteA (W ) + IF W asteB (W ) + IF W asteC (W ) + ... (3)
Then, it is possible to calculate the Mass Intensity Total (MIT), as seen in the equation 4.

M IT = M IC (w) + M IC (y) + M IC (z) + M IC (n) (4)
In the fourth step, it was compared both economic and environmental gains to get the Economic Gain Index (IGE) and the Environmental GainIndex (IGA), as seen in the equations 5 and 6 respectively:

IGE = M T E GE (5)
IGA = M IT GE (6)
Now it's possible to compare whichgain is more representative, economic or environmental [START_REF] Neto | Cleaner Production Associated with Financial and Environmental Benefits: A Case Study on Automotive Industry[END_REF].

Values in the table 1 refers to the Material Intensity Factors (MIF) of rubber.

Results and Discussion

In the oldest process, all rubber waste was forwarded to the final disposal without any treatment. After process improvements, all rubber waste are now grinded and returns to the process where it is mixed with virgin raw material. It works in a closed cycle to preserve the same chemical composition and physical properties. This result is aligned with the Solid Waste Policy that aims the recycling in a closed cycle, in according to [START_REF] Joinhas | Logística Reversa, Sustentabilidade e Educação[END_REF].

Further important aspect observed in this study is recycling and reuse of rubber waste that is provided as a filler charge in the rubber products manufacturing system. Thus, it can be used for production of vehicles components, in according to [START_REF] Liu | The Impact of the Chinese Automotive Industry: Scenarios Based on the National Environmental Goals[END_REF]. This finding also contributes to the literature, because cases researched addressed the use of rubber waste in other applications instead of reuse in the internal process of the company. For example, use the recycled rubber in asphalt [START_REF] Oliveira | Use of a Warm Mix Asphalt Additive to Reduce the Production Temperatures and to Improve the Performance of Asphalt Rubber Mixtures[END_REF], use the recycled rubber on concrete [START_REF] Richardson | Freeze/thaw Protection of Concrete with Optimum Rubber Crumb Content[END_REF] and add the recycled rubber on the glass and plastic mixture [START_REF] Osmani | Innovation in Cleaner Production Through Waste Recycling in Composites[END_REF].

Results showed that the adoption of the CP practices contributed to save costs with waste disposal and at raw material acquisition. This evidence is in according to [START_REF] Almeida | Ecologia Industrial: Conceitos, Ferramentas e Aplicações[END_REF] that aims to maximize the reuse of waste through recycling in the production system.

In the first step, it was collected the production data, as shown at table 2. The average output of this company are 580 ton per month. Recycling station demands 4 operators, 2 on the first shift and 2 on the second shift. The recycling station capacity are 55 ton per month. In addition, the company acquires monthly 52 ton of virgin raw material. This finding indicates similar characteristics with other recycling and reuse processes in a production system, in according to [START_REF] Gombault | Cleaner Production in SMEs Through a Partnership with (Local) Authorities: Successes from the Netherlands[END_REF][START_REF] Richardson | Freeze/thaw Protection of Concrete with Optimum Rubber Crumb Content[END_REF][START_REF] Osmani | Innovation in Cleaner Production Through Waste Recycling in Composites[END_REF][START_REF] Oliveira | Use of a Warm Mix Asphalt Additive to Reduce the Production Temperatures and to Improve the Performance of Asphalt Rubber Mixtures[END_REF][START_REF] Liu | The Impact of the Chinese Automotive Industry: Scenarios Based on the National Environmental Goals[END_REF]. This company spent around USD 13,650 per month with virgin raw material acquisition. In addition, there were costs in order to dispose the generated waste, around USD 1,444 per month. So, the total costs in previous scenery were USD 15,094 per month. Recycling came up as an alternative, the operational costs to recycle the waste were around USD 7,150 per month, meaning USD 7,944 of saving per month.

Investments in equipments acquisition, infrastructure, and other expenses to build the recycling station were USD 128,750, resulting in a ROI in 17 months, as seen in table 3. This result contributes to the literature since it demonstrates by quantitative calculations the opportunities of economic gains with the recycling and reuse of rubber in a production system. The qualitative approach is usually adopted in the researches without maths methods as cost saving, investment calculation and return on investments. This evidence is reinforced when analyzed studies of [START_REF] Gombault | Cleaner Production in SMEs Through a Partnership with (Local) Authorities: Successes from the Netherlands[END_REF][START_REF] Ghosh | Recycling of Silicone Rubber Waste: Effect of Ground Silicone Rubber Vulcanizate Powder on the Properties of Silicone Rubber[END_REF][START_REF] Nabil | Fatigue Life, Thermal Analysis and Morphology of Recycled Poly (ethylene terephthalate)/Commercial Fillers Hybrid Filled Natural Rubber Composites[END_REF][START_REF] Van Berkel | Development of an Industrial Ecology Toolbox for the Introduction of Industrial Ecology in Enterprises-I[END_REF].

The process generates 52 ton of rubber waste per month. The inappropriate disposal results in 296,400 kg in the abiotic compartment, 7,592,000 kg in the water compartment and 85,800 kg in the air compartment. Then, it was identified environmental benefits with the CP practices implementation, as seen at table 4. Thus, the total reduction of environmental impact was 7,974,200 kg. This result contributes to advance the current literature due to demonstrate the reduction of environmental impact in the abiotic, biotic, water and air compartments through the adoption of environmental assessment methodology, in according to [START_REF] Neto | Cleaner Production Associated with Financial and Environmental Benefits: A Case Study on Automotive Industry[END_REF]. The qualitative approach is usually employed in researches to show the potential environmental gain or demonstrated by the percentage of mass balance, as verified in studies of [START_REF] Gombault | Cleaner Production in SMEs Through a Partnership with (Local) Authorities: Successes from the Netherlands[END_REF][START_REF] Ghosh | Recycling of Silicone Rubber Waste: Effect of Ground Silicone Rubber Vulcanizate Powder on the Properties of Silicone Rubber[END_REF][START_REF] Nabil | Fatigue Life, Thermal Analysis and Morphology of Recycled Poly (ethylene terephthalate)/Commercial Fillers Hybrid Filled Natural Rubber Composites[END_REF][START_REF] Van Berkel | Development of an Industrial Ecology Toolbox for the Introduction of Industrial Ecology in Enterprises-I[END_REF]. The Economic Gain Index (IGE) and the Environmental Gain Index (IGA) were compared, as seen at table 5. It was demonstrated by calculating IGE that corresponds to 6.54 kg/USD of goods saved.

When considered the Mass Intensity Total (MIT) by each USD, there is a benefit of 1,003.80 kg of goods that are not modified nor taken from the ecosystems. So, this case study has demonstrated that it's possible to get economic and environmental gains through adoption of the CP practices. Finally, the comparision of the economic and environmental gains also contributes to the science.

Conclusions

The present research demonstrated that the adoption of the CP practices in a rubber products manufacturer, resulted in economic and environmental gains, that was this research objective. The environmental gain was very expressive, 153 higher than the economic gain, justifying the adoption of the rubber recycling. In addition, it was verified an economic gain with the return over investments in 17 months. This results contribute to increase the knowledge about this subject, as well to encourage other companies to the preservation of the natural resources.

The environmental impact due the inappropriate waste disposal was known by the company, however the costs saving and comply with the ISO 14000 requirements were the main reasons to implement the rubber recycling.

This research limitation consists in the application of a unique case, so it's not possible to generalize the results. It's suggested to perform new researches to allow comparisons.

Table 1 .

 1 Material IntensityFactors

	Substance Specification Abiotic Biotic Water Air
	Rubber	SBR	5.70	-146.00 1.65

Table 2 .

 2 Data collection

	Description	Amount
	Average output per month	580,000 kg
	Recycling station capacity per month 55,000 kg
	Employees	4 (two shifts)
	Rubber consumption per month	52,000 kg

Table 3 .

 3 Economic assessment

	Description	Costs (USD)
	Monthly raw material acquisition (52 ton)	13,650.00
	Monthly waste disposal	1,444.00
	Monthly cost Virgin raw material + waste disposal	15,094.00
	Monthly rubber recycling (52 ton)	7,150.00
	Monthly economic gain with recycling	7,944.00
	Cylinder 1500 x 500 mm	45,000.00
	Rubber mill 1100 x 800	55,000.00
	Investment Vibration equipment for 30 mash	21,250.00
	Transport and other expenses	7,500.00
	Total investment	128,750.00
	Return On Investment (ROI)	17 months

Table 4 .

 4 Environmental assessment

	Rubber Abiotic Biotic Water Air
	IF	5.70	-	146.00 1.65
	Mass	52	-	52	52
	MIC	296,4	-7,592,000 85,8
	MIT		7,974,200 kg

Table 5 .

 5 Comparison between economic and environmental gains

	IGE	IGA
	Mass 52,000 kg	7,974,200 kg
	GE USD 7,944.00 USD 7,944.00
	6.54 kg/USD 1,003.80 kg/USD


Matti Majuri
email: matti.majuri@tut.fi

Hasse Nylund
email: hasse.nylund@tut.fi

Minna Lanz
email: minna.lanz@tut.fi

Analysis of Inter-firm Co-operation in Joint Research and Development Projects

Keywords: Networks, Renewal, Innovation, R&D

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Rapid changing markets and increasing competition have led to a situation where ability for renewal has become one of the dominant capabilities in the pursuit of competitive advantage [START_REF] Jovane | The Manufuture Road towards Competitive and Sustainable HAV Manufacturing[END_REF]. The renewal may focus on processes, offering or business concepts and can be radical or incremental by nature. Challenges related to renewal are somewhat different between large companies and SMEs. SMEs have typically less bureaucracy and more concentrated ownership, which in general support agility. Additionally, with fewer resources, the personnel of SMEs' are used to work in wider scope of processes, which in turn supports flexibility. On the other hand, compared to the large companies, SMEs' have typically weaker risk tolerance, smaller knowledge base, fewer sales channels and narrower offering. All of these factors can be considered essential for successful renewal, development and innovation. Inter-firm co-operation creates a major opportunity to tackle these challenges and altogether enables firms to achieve stronger position than they could alone [START_REF] Boddy | Implementing Co-Operation between Organizations: An Empirical Study of Supply Chain Partnering[END_REF].

In general, deep inter-firm R&D co-operation between SMEs in the Finnish technology industry is rare [START_REF] Parhaat | Valmistavan teknologiateollisuuden tutkimusagenda 2020[END_REF]. On the other hand, several public funding instruments require this co-operation and multiple organizations have been established to support the cooperation. Focus of the paper is on collaborative R&D projects that received funding from Tekes, Finnish Funding Agency for Innovation. The paper increases understanding on the challenges and opportunities the companies are facing in their co-operation attempts during these projects. Research data was collected with eight semi-structured company interviews in two separate R&D projects.

Joint Research and Development Projects

The research concerning inter-firm R&D co-operation is fragmented. Several frameworks and concepts from multiple fields of science have been presented to model the dynamics of inter-firm co-operation. Majority of the studies concerning inter-firm cooperation seem to be quantitative by their nature, although this was not systematically studied. In recent years concept of open innovation has got a lot of attention. Open innovation processes includes outbound, inbound and coupled processes [START_REF] Enkel | Open R&D and Open Innovation: Exploring The Phenomenon[END_REF] from which coupled processes are closest to the topic of this study. However, the research focusing on coupled processes does not seem to include inter-firm joint R&D projects in which the companies are from different value chains.

Hagerdoorn [START_REF] Hagedoorn | Organizational Modes of Inter-Firm Co-Operation and Technology Transfer[END_REF] has created classification for co-operative agreements based on the amount of the organizational interdependence, which can be considered to be a central dimension defining the mode of co-operation. The classification has four main modes:

(1) Joint R&D ventures, (2) Joint R&D and technology exchange agreements, (3) Equity investments and (4) Customer-supplier relationships and one-directional technology flows. The second mode is divided into three categories: joint research pacts, joint development agreements and technology sharing agreements. Study presented in this paper contributes to the areas of joint research pacts and joint development agreements.

Barnes et. al. [START_REF] Barnes | Managing Collaborative R&D Projects Development of a Practical Management Tool[END_REF] have identified 40 success factors for inter-firm co-operation which they have divided into six categories: choice of partner, project management, universal success factors, ensuring equality, monitoring environmental influences, project manager and choice of partner. Lee et. al. [START_REF] Lee | Open Innovation in SMEs -An Intermediated Network Model[END_REF] have summarized intermediary's role in SME innovation networks based on literature review into framework that consists of five. Categories: Network Database, Network Construction, Network Management, Culture of co-operation and Facilitation of co-operation. Frameworks that illustrate the characteristics and dynamics of R&D inter-firm network are presented by e.g. Kirkels [START_REF] Kirkels | Brokerage in SME Networks[END_REF], Esterhuizen et. al. [START_REF] Esterhuizen | Knowledge Creation Processes as Critical Enablers for Innovation[END_REF], Möller et. al. [START_REF] Möller | Strategic Business Nets -Their Type and Management[END_REF], and Dasgupta and Gupta [START_REF] Dasgupta | Innovation Organizations: A Review of the Role of Organizational Learning and Knowledge Management[END_REF].

The frameworks have plenty of similarities but they are still different. A conclusion can be made, that the inter-firm co-operation as a phenomenon is heavily case dependent. None of studies dealt with the same situation as ours. Therefore it is justified to increase the understanding on inter-firm R&D co-operation from perspective of this paper. At least the following features can be considered to be defining in our study: public funding, significant role of research institute, project-centeredness, formality, non-equity, non-value chain and technological orientation of R&D topics. Considering the high volume of joint R&D projects receiving public support they are surprisingly rarely in the focus of the qualitative studies, which could provide in depth knowledge on the phenomenon.

Method

Priority of our research was to deepen the understanding on actual experiences that companies had on inter-firm co-operation in publically funded join R&D projects. Research approach was qualitative and inductive. This approach was chosen due to fragmentation of the previous research and case dependence of the phenomenon dynamics.

The data was collected by interviewing eight persons from eight companies that had been participants in collaborative R&D projects funded by Tekes. Interviews were semi-structured and they lasted 75 minutes on average. Central answers concerning project phases, quantity and quality of co-operation, goal achievement and co-operation characteristics were collected into an excel sheet that was visible for the interviewee during the interview. The interviewees were asked to define the project phases freely according to project goals they found relevant. Illustration of the excel sheet is presented on Table 1. There was also data collected concerning the co-operation with research institutes, but it is not in the focus of this paper.

All interviewees were the major participants in the projects from their companies. Introduction for the interviewee about research objectives was kept short to avoid inducement. All interviews were recorded and transcribed. Atlas TI computer program was used for qualitative coding.

Table 1. Excel sheet base filled with the interviewee

Aim of the paper is to answer the following research questions:

1. What characteristics companies associate with high and low quality inter-firm cooperation? 2. What reasons explain the realized amount of co-operation? Additionally, the paper gives suggestions for new project structures that would increase the quality and quantity of inter-firm co-operation in R&D projects. There is also data presented on the amount of co-operation between companies and also between compa-

Number of the project phase 1 … n

Goal of the project phase

The amount of the collaboration with research institutes 0=none, 1=little, 2=moderately, 3=plenty

The quality of the collaboration with research institutes 0=poor, 1=moderate 2= high The amount of the collaboration with other companies in the project 0=none, 1=little, 2=moderately, 3=plenty

The quality of the collaboration with other companies in the project 0=poor, 1=moderate 2= high Goal achievement 0=not achieved, 1=achieved partially, 2= achieved well nies and research institutes. However, this data cannot be generalized due to small sample size, but it can be considered to be useful for planning future research and also to stimulate the public discussion on the matter. The research presented in this paper is part of the ongoing research project called Renaissance of the Regions (ReRe) -Challenging the Status Quo of Innovation Policy Implementation in Regional Manufacturing Networks. Project goal is to create improved methods and means for public or private network coordinators to support and develop the innovation process in SME networks. To be able to work efficiently it is necessary that the coordinator has an understanding on what characteristics high quality co-operation consists of. Understanding on the mechanisms how to influence those characteristics and on the mechanisms how those characteristics influence the co-operation is also needed. Additionally, this understanding is valuable also for the companies. With deeper understanding they are able to manage their co-operation activities and capabilities more coherently to support their innovation co-operation with other companies.

Tampere University of Technology's research goal in the ReRe project is to create generic model for evaluating the status of prioritized co-operation characteristics in DIR network (Development, Innovation, Renewal). Table 2 explains the research phases. The results and analysis presented in this paper contributes to the current state analysis. They also create valuable information for planning a web survey on phase 2.

Interview Outcomes

Table 3 presents the amount of co-operation the interviewed company had with other companies and with the research institutes during the different phases of the project. It also presents the quality of the co-operation in each phase and how well the goals were achieved.

Phase No. Phase Methodology

Current state analysis

Literature review and qualitative study (interviews)

Generalization

Quantitative study (web survey)

3.

Deepening the understanding Qualitative study (intreviews)

Synthesis

Combining and evaluation of the knowledge from phases 1 to 3 (expert workshops)

Verification

Testing the model by using it to analyze 3 to 5 DIR networks Table 3. Amount and quality of cooperation.

Although the study sample is small and as such not generalizable, it was still surprising to notify how little inter-firm co-operation existed. With further analysis on the transcribed interviews we formed Table 4, which includes explanations the companies gave for the low amount of inter-firm co-operation.

Table 4. Explanations for the lack of inter-firm co-operation.

Due to small amount of inter-firm co-operation it was natural that the data did not offer much information on the co-operation characteristics. Since all inter-firm co-operation was considered by the interviewees' to be of high-quality (Table 2

.) only characteristics describing high-quality can be listed. Characteristics describing high-quality

The amount of the collaboration with research institutes 0=none, 1=little, 2=moderately, 3=plenty 0 3 3 0 0 2 3 1 1 0 3 1 0 1 3 0 2 0 3 0 2 1 2 2 2 2 2 1 1 0 3 2 1 2 0 3 0 1 0 The quality of the collaboration with research institutes 0=poor, 1=moderate 2= high 2 2 2 2 2 2 2 2 0 1 2 2 2 2 2 2 2 2 3 1 1 2 1 0 2 1 1

The amount of the collaboration with other companies in the project 0=none, 1=little, 2=moderately, 3=plenty 0 0 1 0 0 0 0 0 0 0 3 1 0 1 3 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

The quality of the collaboration with other companies in the project 0=poor, 1=moderate 2= high 2 2 2 2 2 2

Goal achievement 0=not achieved, 1=achieved partially, 2= achieved well

2 1 1 2 0 1 2 2 2 1 2 2 2 0 0 1 2 0 0 2 0 2 2 2 2 2 2 1 1 1 2 1 2 2 2 1 2 1 2 2 C6 C7 C8 C1 C2 C3 C4 C5 C1
Company with synergetic business interests left the project in early stage. This was possibly due to financial challenges.

Other companies were distant.

C2

Other companies had different focusses in their R&D.

Company, that was considered as a potential partner, focused on different technology.

C3

Big customer, that encouraged C3 to participate, did not participate the "group project". The customer was not able to reach an agreement with other participating companies.

Other companies were already in their own networks, and P3 was not able to fit into them.

C4

Other companies were interested in C4 part, but did not want to allocate resources to collaboration. From business perspective the times were difficult and this affected the resourcing.

One potentian company to do collaboration with left out just before project started C5 Project topic in C5 was different. Other companies focused on product development when C5's aim was to develope ther risk management processes.

Insufficient resources in C5.

C6

R&D subjects were close, but not close enough to do collaborative development C7 Lack of resourcing in C7. Collaboration would have required human resources from wide range of functions in C7)

Scope of R&D was such that it did not lead to collaboration.

C8

Desired results were delivered with very little collaboration. Knowledge exchange between companies happened through research organization.

Conclusions and Discussion

In this paper the characteristics of inter-firm R&D co-operation in the Finnish technology industry was discussed. The explanations for the lack of inter-firm co-operation in a joint research project, presented in Table 4, can be summarized with the following three categories:

1. Own R&D goal was different from the ones other companies had 2. The project consortium changed into less synergetic 3. Qualitative of quantitative lack of resources

The projects that we studied were put together mainly by research institutes which had their own research projects going alongside the companies' R&D projects. The construction phase of the projects, including putting the consortium together, is usually carried out fast. This means, that not much resources are allocated into finding companies with similar or synergetic R&D goals. Sherer [START_REF] Sherer | Critical success factors for manufacturing networks as perceived by network coordinators[END_REF] divides the critical success factors for manufacturing networks into trust, commitment, selection choice, information technology, and intermediary. The role of an intermediary, which in our study was a research institute, is important at the construction phase of a project. An intermediary can facilitate networking both by selecting potential participants and aiding companies interested in participating in a R&D project. Barriers, such as conflicting goals and expectations can be eliminated by an efficient facilitation [START_REF] Halme | Small and Medium-Sized Tourism Enterprises in Sustainable Development Networks[END_REF].

One challenge in the forming of the R&D project is, in addition to the putting the consortium together is a short time, is the small amount of potential companies that can be contacted. Research institutes typically rely on their existing contacts, which narrows down the selection choice of companies. Thorgren et al. [START_REF] Thorgren | Designing Interorganizational Networks for Innovation: An Empirical Examination of Network Configuration, Formation and Governance[END_REF] state that larger number of companies relates to a greater innovation. The greater number of companies increases the selection choice, which can lead to more synergetic R&D projects. In the forming phase, trust and commitment is mainly focused on the intermediate in that the potential companies are confident with the intermediate and are more willing to participate in a R&D project. Potential companies should have clear vision of the R&D project to invest into it [START_REF] Ahlström-Söderling | SME Strategic Business Networks seen as Learning Organizations[END_REF].

Trust and commitment is essential in both forming and during a R&D project. During a R&D project the role of an intermediate changes more on supporting the participating companies in the trust and commitment issues between the participating companies [START_REF] Hanna | Small Firm Networks: A Successful Approach to Innovation[END_REF]. The trust and commitment issues between the participating companies emphasizes during a R&D project. A company should have clear image of the benefits it can gain from the project. In the inter-firm co-operation an important issue is how the co-operation with other companies enhances their own objectives [START_REF] Wincent | An Exchange Approach on Firm Cooperative Orientation and Outcomes of Strategic Multilateral Network Participants[END_REF]. This requires open sharing of information and resources in that opportunities are clear, which is an evident benefit of a functional network [START_REF] Fuller-Love | Networks in Small Manufacturing Firms[END_REF].

The lack of resources can be explained with both lack of needed skills and lack of time dedicated to the project even when personnel with required skills exist. This can be explained with the short time of construction of a project. It is important that the participating companies are devoted to the ongoing R&D project. In addition to the role of the intermediate, top management of the companies should recognize the importance of the co-operation [START_REF] Huggins | The Success and Failure of Policy-Implemented Inter-Firm Network Initiatives: Motivations, Processes and Structure[END_REF]. Two relatively different solutions can be identified to tackle these challenges.

The first solution we propose is that there should be knowledge available on the R&D goals of large amount of companies for the person who is coordinating the project planning, e.g. in some kind of database solution. This would allow contacting widely potential participants that have similar or synergic R&D interests. When companies with similar genuine R&D needs would be identified in early stages of the project planning, commitment inside the consortium would rise and fewer changes in the consortium would occur. It should also be noted, that in the studied cases the company participants did not interact together in planning phase and the first face to face meetings were organized after the projects had already started. It is quite likely, that the presented solution would also increase pre-project co-operation, besides increased co-operation during the projects.

In projects with public funding involved, the companies tend to define their R&D goals with narrow scopes and have often very little resources allocated into free innovative renewal. This is partly because the public funding system requires coherent narrowly defined plans and partly because of small R&D resources due to hard competition situation in many business fields.

The second solution we suggest would be a kind of hybrid model for the project plan. There would still be straightforward plans with narrow scopes but the project plan would also include resources for loosely defined collaborative R&D and interaction, e.g. topical workshops. This would create opportunities and space for new ideas that were not possible to be identified in the planning phase of the project. In the current model the project participants become sort of prisoners of the project plan. This leads often to minimal or zero slack and weak co-operation. This is problematic, because slack and co-operation can be considered to be cornerstones of innovativeness [START_REF] Facó | Innovativeness of Industry Considering Organizational Slack and Co-operation 109[END_REF].

  inter-firm co-operation are: (i) Common vision; (ii) Partner was showing trust; (iii) Encouragement by the partner; (iv) Desire to help; (v) Honest and straight dialogue; (vi) Good relations; (vii) Openness Experienced appreciation; (viii) Similar commercial goals.

Table 2 .

 2 Research phases.

Acknowledgements

The research presented in this paper is co-financed by Tekes, the Finnish Funding Agency for Technology and Innovation, and Tredea Oy, the Tampere Region Economic Development Agency.


Helton Silvia H Bonilla
email: shbonilla@hotmail.com

Helton R O Silva

Robson P Faustino

Irenilza De

Alencar Nääs

Nilsa Duarte

Irenilza De Alencar Nääs

Environmental Support for Dilution of Pollutants from Broiler Production and Aquaculture in Brazil

Keywords: Poultry, Aquaculture, Emergy, Environmental services

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Livestock production contributes significantly to the anthropogenic loading of the biosphere. As the world's demand for food increases, the increasing emissions to land, water and atmosphere from intensive livestock production is worthy of concern.

Poultry and fish from intensive operations are an important source for global human food consumption [START_REF]Food and Agricultural Organization of the United Nations: Global Agriculture Towards[END_REF]. Aviaries are a source of ammonia emissions that are a function of NH3 concentration inside the housing. It depends on flock density, litter, diet and ventilation systems. Some of these parameters and consequently the ammonia emission can be controlled through proper management [START_REF] Gates | Method for Measuring Ammonia Emissions from Poultry Houses[END_REF]. On the other hand, concerns are expressed about the discharge of wastes, increasing the concentration of nutrients, pathogens and chemical such as drugs and pesticides from aquaculture operation [START_REF] Naylor | Aquaculture and Ocean Resources: Raising Tigers of the Sea[END_REF].

The environment can act as a source of resources and a sink of emissions. This might be accomplished by taking advantage of the current cycles and processes of the biosphere, and free environmental services. In this context, emissions from production process would rely on an amount of available resources (land, water, air) which acting as a sink will, at least, dilute them down to the biosphere background. In this way, the "support area", would act as a buffered piece of land, with the capacity of providing the natural services necessary to dilute or abate emissions derived from any producer system. The problem arises with the establishment of the "support area" since natural services have to be quantified. The quantification of natural services taking into account their function and the involved support of the biosphere is not trivial, and the need for a well-established and well-sustained tool becomes evident.

This approach is provided by the emergy environmental accounting [START_REF] Odum | Environmental Accounting: Emergy and Environmental Decision Making[END_REF], an assessment framework that evaluates resources based on the environmental work required to generate them and make them available. The methodology, based on thermodynamics and systems theory, enables accounting for all natural and economic inputs entering a system in a common metric. It provides indices capable of quantifying environmental performance as well as monitoring improvements and it is a useful tool for decision making.

By quantifying the "work" of the biosphere involved in pollutants dilution (sun, wind, rain, deep heat, gravitational energy) in emergy terms, it is possible to make the conversion into the area of land demanded to supply those environmental services by the local renewable resources of the region [5; 6].

This paper addresses the use of emergy to explore the role of natural services from a land area to act as a sink for ammonia emissions from a poultry production housing and phosphorous from an aquaculture farm. The environmental work done by the biosphere intending to dilute the considered emissions is accounted regarding emergy and converted to the "support area" concept, the land demand responsible for acting as a buffer.

Methodology

Emergy Environmental Accounting

Emergy accounting methodology [START_REF] Odum | Environmental Accounting: Emergy and Environmental Decision Making[END_REF] was developed over the last decades as a tool for environmental policy and to evaluate quality and distribution of resources in the dynamics of complex systems. A complete assessment of the methodology cannot be provided here, but the reader may refer to published reports [4; 7]. Briefly, emergy is defined as the sum of all inputs of energy (directly or indirectly) required for a process to provide a given product, when inputs are expressed in the same form (or type) of energy, such as solar energy [START_REF] Odum | Environmental Accounting: Emergy and Environmental Decision Making[END_REF].

The emergy flows are classified into three categories of resources: R as renewable resources, N as non-renewable resources and the inputs provided by the economy, F. All three categories are fundamental to the emergy accounting and the understanding of the system interactions with the environment. R and N flows are provided by the environment and are economically free.

While renewable resources can be replaced at least at the same rate as they are consumed, the non-renewable resources are depleted faster than their ability of recuperation. The economic inputs, F, are provided by the market and are related to flows supplied by the economy.

Every input has to be inventoried and multiplied by its correspondent "transformity" to calculate the emergy flows. "Transformity" (expressed in seJ/J) is the factor to convert each kind of energy in emergy, and it represents all past environmental work necessary to obtain one joule of a given resource. When inputs are expressed in units others than energy ones (mass or money), the UEV (unit emergy value) is used to convert values into solar energy joules (seJ). To carry out the emergy flux determination, the planetary baseline of 15.83x10 24 seJ/year was adopted [START_REF] Odum | Introduction and Global Budgets[END_REF]. The transformity values that were calculated using another baseline were corrected and properly informed during calculation.

Accounting for Environmental Services and Support Area

The energy system diagram is described in Fig. 1. Inputs directly required to develop livestock production are aggregated into types, classified as R1, F and N, converging towards the inner frame. The outputs are food and waste by-products. The larger frame contains the environmental systems which act as the sink of the by-products also generated in the production system. The environmental services required to drive the dilution process are expressed as R2. The following steps were adopted to account for the environmental services: (a) Determination of mass of air required to dilute NH3 and mass of water required to dilute phosphorous emissions for poultry production and tilapia aquaculture, respectively, up to acceptable concentration levels: (i) close to average values of NH3 in the biosphere, and (ii) phosphorous concentration in water below the critical threshold of eutrophication.

(b) Determination of the emergy flow of the required environmental services (noted as R2 in Fig. 1) by calculating the kinetic energy of the mass of dilution air (measured as the wind power needed to spread and dilute pollutants) and the chemical energy of the mass of dilution water (Gibbs energy necessary to dilute pollutants). Procedures were adopted from [5; 9], respectively.

(c) Calculation of the "support area" (As), known as the area of land necessary to balance the emergy of the environmental services (R2), and calculated from the average annual flux of renewable emergy per year per unit area of landscape (EmdR), As = R2/EmdR [START_REF] Ulgiati | Quantifying the Environmental Support for Dilution and Abatement of Process Emissions[END_REF]. The value of EmdR was calculated from the results of the annually renewable emergy flows of Brazil, (extracted from [START_REF] Demétrio | Emergy Accounting of Brazilian States and Regions[END_REF]), divided by the Brazilian continental area and it accounts for 3.66x10 11 seJ/m 2 .

Systems Under Study

The present study was carried in broiler houses located inland Mato Grosso do Sul state and a tilapia farm inland São Paulo state, Brazil. Necessary data to accomplish the study for both production systems were extracted from [11; 12], respectively.

Average values of four commercial broiler houses (tunnel system ones) at Itaquiraí -MS county, were used to carry out the study. There were 25,200 broilers in each house from the Hubbard' genetic strain reared for 42 days until they reach the commercial weight of 2 kg.

The tilapia (Genomar Supreme Tilapia) farm is located in Iperó city, distant from São Paulo city 128 km. The installation is comprehensive of a total of 200 fish cages, 40 of 4 m3 and 60 of 6 m3 each. The pond where the cages are placed covers an area of 30 ha with an average depth of 2.10 m. The pond is fed by a water course which pours a water supply of 150 l/sec in average. Fingerlings are placed into the cages and after a cycle of approximately 180 days, the fish reach 484 g. The proper chemical analysis shows no eutrophication of pond water. The annual production is 320,000 fishes. Ammonia emission values were obtained from the application of a predicting model confirmed by measurements in loco [START_REF] Lima | Model-predicted Ammonia Emission from two Broiler Houses with Different Rearing Systems[END_REF]. Phosphorous release due to fish metabolism was estimated according to the conversion factor in biomass and the content of phosphorus in the food. Additional data were retrieved from literature.

Results and Discussion

Table 1 shows the results of the environmental services involved in dilution of emissions accounted for both the enterprises. Table 1. -Mass of air required to dilute ammonia emissions from the poultry house and mass of water required to dilute phosphorous released from the tilapia farm, on a yearly basis; energy values and corresponding annual emergy flows of the environmental services R2 expressed in seJ and seJ by kg of food produced.

Poultry production

Tilapia aquaculture

Mass of air (g/y) 6.20x10 14 (1) Mass of water (g/y)

2.23 x10 07 (5) Energy (J/y) 7.75x10 12 (2) 1.06x10 14 (6) Emergy from environmental services R2 (seJ/y) 1.95x10 16 (3) 1.17x10 19 [START_REF] Brown | Emergy-based Indices and Ratios to Evaluate Sustainability: Monitoring Economies and Technology Toward Environmentally Sound Innovation[END_REF] Emergy from environmental services R2 by mass (seJ/kg y) 7.70x10

"Emergy eco-efficiency" (kg y/seJ 10-10) 1.3 0.0013

The OECD (OECD-Organisation for economic cooperation and development) has called eco-efficiency "the efficiency with which ecological resources are used to meet human needs" and defines it as a ratio of an output (the value of products and services produced by a firm, sector, or economy as a whole) divided by the input (the sum of environmental pressures generated by the firm, the sector, or the economy)." [START_REF]OECD: Eco-efficiency[END_REF]. The concept was reformulated in emergy terms, and it was expressed as units of mass produced by seJ of environmental services committed to dilute pollutants (kg/seJ). Values can be seen in Table 1, calculated as the inverse of the emergy flows corresponding to the environmental services normalized by mass. At a first glance, poultry production seems to be a thousand times more "eco-efficient", according to the definition adopted here. However, although all the environmental services are accounted for by using a common metric, the present approach only takes into consideration dilution of pollutants in a simplified way. The inclusion of pollutants into biogenic cycles occurs, and kinetics of the diverse reaction should be considered. Even so, present results serve as a starting point to evidence that real efficiency of production systems are not adequately measured without taking into consideration the externalities caused by the use of resources, and release of by-products, even in compliance with legal threshold concentrations. Moreover, the degree of eco-efficiency attained is not just limited to an absolute value obtained by the index calculation but to a holistic enterprise management towards environmental improvement.

If it is assumed that the whole emergy required to dilute pollutants is derived from renewable resources supplied by region, the calculated "support area" will act as a sustainability predictor of long-term sustainability. Table 2 shows the values of "support area" and the ratio between them and the direct areas (real area of the enterprise being 1800 m 2 for poultry production and 30 ha for aquaculture farm). When As by mass produced is considered differences between both systems are evidenced. Each kilogram of tilapia produced relies on almost a thousand times more support area than poultry. According to emergy theory, transformity related to water capacity for diluting solutes are higher than transformity for the wind, the environmental service involved for ammonia dilution. The hierarchies of environmental processes that originate both services are different, and comparison should be made carefully. Even so, the As/DA ratio evidences that the direct area is not a good indicator to attain the real contribution of the biosphere when acting as source and sink of resources. In this way, areas 29 times and more than hundred times greater than the direct areas respectively are need to support the enterprises just from the emissions, without taking into account resources side (or inputs).

Concluding Remarks

Accounting for the environmental services required to dilute emissions is shown to be as a necessary procedure towards the proper evaluation of the sustainability of processes and quantification of externalities.

The challenge is to fit humans' production patterns to the biosphere's capacity to absorb waste by-products, without overload. For this purpose, services provided by natural capital have to be adequately evaluated and finally quantified in terms comparable with the economy.

Fig. 1 .

 1 Fig. 1. -To drive production (poultry or aquaculture), flows of renewable (R1), nonrenewable (N) and purchased resources (F) are needed (represented by -) Emissions are diluted by nature through renewable services (R2). Flows of energy dispersion is represented by -

Table 2 .

 2 -The support area (As), the specific As (by mass) and the ratios between As and direct areas (DA), calculated for Poultry production and Tilapia aquaculture.

		Poultry production	Tilapia aquaculture
	As (m 2 )	5.30x10 04	32.0x10 06
	As (m 2 /kg)	0.21	206.5
	As/DA	29	107