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Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds.

APPLICATION OF THE TRAVELING SALESMAN PROBLEM HEURISTICS TO THE REALLOCATION OF EQUIPMENT IN A SMALL-SIZE BAKERY AIMING AT MINIMIZING BREAD PRODUCTION TIME

Shih Y. Chin Anselmo R. P. Neto Eduardo V. G. Filho School of Engineering of São Carlos 400 Av. Trabalhador-São Carlense, University of São Paulo São Carlos, CP: 359 CEP: 13566-590, Brazil

ABSTRACT This paper presents a case study of a small-size bakery whose problem is the reallocation of production equipment. The owner of the establishment intends to modify the current position of the warehouse, since raw material must be handled along the production system in order to be stored, jeopardizing the motion of bakers. This modification, though, would affect the location of the remaining equipment. The reallocation of the warehouse and equipment, obtained via the application of the travelling salesman problem heuristics, will reduce the total distance covered by the bakers, thereby avoiding the flow of raw material throughout the production system, and possibly increasing throughput. Before the implementation of the solutions generated by the heuristics, two simulation models will be created in Arena software 5.0, one representing the current configuration, and the other representing the proposed configuration, so as to validate the results. 1 INTRODUCTION The increasing number of companies that provide similar products and services is resulting in customers with more knowledge in choosing. It makes the companies be flexible to adapt constantly according to the new requirements of competitors and customers. Currently a company does not work in a lonely way but integrated, which group of companies supports each other to available product and services in a right time, quality and quantity. In the machine (or automotive) parts segment, for instance, this situation can be commonly found. The raw material supplier sends the unprocessed steel to the other factory, which is responsible for the melting process. Following, it sends the melted material to the other company which is responsible to produce screws. In summary, relationships among companies make possible to

accomplish a certain goal and several authors name it as a chain. A chain can be described as a set of relationships (information or flow materials and so on) and these shall be managed in a integrated way aiming at reducing the costs (in a several aspects) to make a chain more competitive. Similarly, if one of the participants of this chain works under the market expectation, it may affect all chain (Hutt and Speh 2001). The synchronization is extremely important, mainly when is related to raw material acquisition, due to it may affect the quality of the final product. The bread-making chain, subject of this paper, is also very huge and complex. For some extra information for the reader, according to ASN (2005), there are 52 thousand baker´s and hire more than 2 millions people and attend an average of 40 millions customers per day. According to ABIP – Brazilian Association of BreadMaking (2002), the most consumed type of bread is popularly called “French-bread”. And according to Silva et al. (2003), this is already considered as daily menu of Brazilians, which makes important to realize researches in how to improve methodologies making enhancement in productivity and low cost of final product. One side there is the paste fermentation, which makes the bread-making process be slowed, but fortunately there are several researches concerned in reducing the paste fermentation time through the alteration of the temperature (Silva 1990). Moreover, several new advanced chemical ingredients are being introduced all days aiming at increasing the time which the paste remain in high quality. On the other side, improvements can be done in the production time. Owing to the most part of sales is in the morning shift, it is extremely difficult to acquire a qualified employees to work in the early morning. According to Ferreira et al. (1999), several steps involved in French bread production, beyond of to be freshly consumed makes the bread-making process be slowed, resulting in lack of production flexibility (due to the paste) and in early morning

Shih, Neto and Filho activity. However it is possible to reduce the routing time spent in production. In 1974, Francis and White describe the possibility to reduce the production total time altering the facilities position. This alteration can also reduce costs since in worst case 75% of product cost could be attributed to material motion (Heragu 1997). Costs related to material handling increases according to the frequency and distance of the product motion, which are considered as parameters to obtain the total time (Askin and Standridge 1993). Unfortunately the disposition (position) of facilities does not define the best sequence which material may route in equipments, only which one should be closer to the other. Doing this, it is expected that total routing cost would be reduced. This is particularly true for the production of only one product. If there are several types of products involved, the disposition can not be adequate. This means the best disposition does not imply necessarily in the best sequence. In logistics, for instance, it is a common issue to be treated is to define the best sequence of routing for employees, parts, or trucks to reach the destiny place. The use of the heuristics Traveling Salesman Problem (TSP) has been successfully applied, such as shown in Renaud et al. (2000). These authors presented a typical application in the transportation area which the travelers should deliver products from the origin to a destiny. Once is defined the routes, it is recommended to not implement immediately in companies, due to the necessity to verify if the proposed routing would result in benefits, such as shown in Roodbergen and De Koster (2001). These two authors realized an application of that heuristics in a parts distribution center aiming to minimize the routing. To check the solution performance, the authors modeled and simulated the company operating under several conditions. When simulation technique/tool is not being used, several parameters that influence in the system can not be visualized. Due to this, simulation tools should be used initially before of implementing any proposals. Jansen et al. (2001), for instance, constructed a simulation model to test their reduction proposal for the product delivering time. Garcia et al. (1999) also presented a simulation research to a newspaper company, which the goal is to improve the delivering time. Indeed, the use of modeling and simulation is not a new issue, since it is considered as a part of the planning and decision process of manufacturing systems (Meyers and Stephens 2000). Some generations of the software can support to solve easily sophisticated mathematical equations, which permit to comprehend better the complexity of the manufacturing systems. In summary, the minimization of the total time can be obtained from the expression (1), which is the composition of the processing time in equipments plus the number of times that the employee routes between each pair of equipments. When the processing time is extremely high,

bakers can realize other activities routing to other equipments (which is the second part of the expression 1). Min(Pr oduction _ total _ time) 

Min(Units(m)*Pr oces sin g _ time(m)) 

Minnumber _ of _ times(m)* Routing (m) / Routing _ velocity) ,

where m represents the component m of the product (1) 2 DEFINITION OF THE PROBLEM AND GOAL The owner of the establishment intends to alter the position of the storage due to the possibility of increasing the production in a near future to attend the demand but the enhancement of the raw-material, currently located in sales area, may jeopardize the throughput. The issue is where to allocate the storage in a way to reduce that impact? Is there any possibility to increase the early morning production modifying the storage position? To answer these questions, the main goal of this paper is to evaluate the performance redefining the disposition of equipments through the application of the heuristics traveling salesman problem. To obtain data in both cases (current and proposal from the heuristics), it will be constructed two simulation models in Arena 5.0. This software is used due to simplicity and flexibility features in modeling production systems. 3 BIBLIOGRAPHY REVISION The high level of demand requires from the baker´s to have a sophisticated and flexible production systems. For these reason Singh and Rajamani (1996) comment that one of the most important challenges in the production system is to operate according to the customers requirements. Such as described earlier, it may be possible to improve the production making some adjustments in the physical disposition of equipments involved in the process. In 1978, Muther already suggested that the routing realized by the raw-material should be progressively, avoiding any returning, bending or even crossing. Depending on how is realized the such allocations, the routing can be incremented than it should be, increasing the time. Before any physical installation, it is important to have an accurate research of layout analyzing on how the transformed resources (materials, information, customers) flow through the production system (Filho 2005). The layout design issue has been studying for a few decades. Muther (1978), for instance, described a layout design heuristics procedure based on the score according to proximity (closeness rating), to allocate departments. That is, as higher the necessity of one department to be closer to the other the higher is the score. Seehof and Evans (1967) present a software named ALDEP (Automated Layout Design Program). This choose a department randomly. Following, other departments are allocated from the first allocated department according to the closeness

Shih, Neto and Filho rating. Lee and Moore (1967) present CORELAP (Computerized Relationship Layout Planning) also works with the closeness rating, but the choice of the first department is based on the higher value of the closeness rating TCR. Armour and Buffa (1963) present CRAFT. This consists in analyzing the allocation of departments according to the motion cost among departments. Thus, as higher the flow among departments, higher the routed distance and, consequently higher the costs associated to this motion. There are also several recently developed software, but it work essentially based on the knowledge provided by the literature. Meyers and Stephens (2000), for instance, exemplify software that works based on the departments allocation in a certain area. FactoryPlan is a planning software based on the closeness rating. Promodel is a simulation software available to designers, since it permits to analyze the current plant performance or even to develop a new one. The Arena 5.0 is also considered by several researches as a great tool and extensively employed to support in design installations. In this paper will be used the simulation software Arena 5.0.

5 CURRENT LAYOUT OF THE BAKER´S The baker´s where the case study was conducted is a small size and located in the interior of São Paulo State. Itself is not responsible by the sale. Therefore it is responsible only by the production and distribution of breads, while the sale is realized by located shops in districts. In relation to the making-bread sector, is estimated that there are around 20 types of breads, but this baker´s produce only the popular, cheese and sweet bread. There are four employees. Around 85% of total production is of the French bread. This way, the research of layouts was based on it. This percentage coincides to the statistics realized by Nutrinews in 1999. Currently the production begins at 1:00 a.m. and finishes at 6:00 a.m. and produces around 450 breads of 50g/h per day. As shown in the Figure 1, the production varies everyday. This Figure shows the relationship between production and demand through a month period and note that the production is inferior to the current demand. Relationship between demand and production

4 HYSTORICAL AND TYPES OF BAKER´S Quantity in five hours per day

2450

According to the information of ProgeFood (2006), company that offers food products of FoodService, estimated the human being had already knowledge in mixing flour with water and baking in fire around twelve thousand years ago. There are historical registers of yeasted bread was discovered around three thousand years B.C. by Egyptians. Nowadays the concept “producing bread” is not related only to the self consuming (familiar), or even to small business, but all are influenced even by multinational companies. Due to this, it is required high level of quality control. Moreover the bread diversity, of recipe, of rawmaterial do require new researches to guaranty the high quality of products. According to the Brazilian Association of BreadMaking Industry (2002), around 580 thousands of employees are hired in several steps of the productive chain in Brazil. To available the final product, there are basically four types of making-bread, similar to shops´ profiles: Boutique: Located in areas where people have more financial condition, selling self or imported products; Services: Located in central regions, where exists crowd roads and commerce; Convenience: Selling self products and of convenience; Hot point: is a branch that receives wrapped and frozen breads to be baked in the hot points. It is important to give a distinction of small sizes (named as “district”), due to these cover certain areas not reached by the big size bread-making and there are more than 38 thousands bread-making of this type.

2400 2350

Production

2300 Demand

2250 2200

Average of production (2195,27 units)

2150

Average of demand (2301,4 units)

2100 2050 2000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Days of the month

Figure 1: Relationship between production x demand of French bread through the month Figure 2 presents the physical disposition of equipments in the production area. In the right side of the Figure, storage, there are shops connected to it. There are shops also connected in the left side of this Figure. This means that trucks cannot parking at both sides and unloading of raw materials is in the bottom side. In the bottom side of this Figure, selling area (distribution), is an area where final products are dispatched to shops. The accessing door dimension can not fit high level of routings (from selling area to the storage) and it can also affect the throughput. However altering the storage position to minimize this problem require to alter also other equipments´ positions.

Shih, Neto and Filho Routing 2: When the yeast is of the type “fresh yeast”, it should be dissolved in the lukewarm water. Consider that this type of water can be obtained closer to the dosage water machine; Routing 3: Put the dissolved yeast, sugar in a mixing machine, located to the scales; Routing 4: Put the flour in the punching machine. Turn on the equipment (in velocity 1 for 3 min, then change to velocity 2) and increment the mixture (from mixing machine) with salt. Add frozen water slowly, until the paste acquires cohesion (around 30 min). Routing 5: Generally the employee can handle around five kg of the paste. Due to the capacity of each kneader be 25kg, he travels to the storage to prepare the ingredients repeating up to complete 25kg (five batches); - Turn off the punching machine, plus grease in the paste homogeneously. Turn on the equipment in velocity 2 for 10 more minutes; - Then turn off it; Routing 6: Conduce the paste to the partition equipment, where the paste are separated in parts according to the capacity of partition equipment (rounding process); Routing 7: Lead those rounded parts to the table making it a new shape, approaching to a spherical. This process is realized manually. After that, it remain on the table, covered by canvas of cotton, where the bunching process (relaxing) occurs (40 minutes); Routing 8: Put it in the panning (shaping) machine, where the shape of bread is formed. This equipment determines the weight through the adjustment of tooling to produce 50g. Routing 9: Grease trays where separated paste are put and lead it to proofing (rising) machine (26°C) for one hour period; Routing 10: While separated paste are put in the it, the employee travels to the storage and prepares other paste´s batch (parallel activity); Routing 11: Lead separated paste to oven (180°C to 200°C), where it remain for 10 minutes; Routing 12: Lead to shelves (where cooling process occurs before the distribution); Routing 13: Turn to the storage.

Figure 2: layout of French bread production. The dimension is in a scale of 1:100 6 EQUIPMENTS AND DESCRIPTION OF FRENCH BREAD PRODUCTION Teed (1983) comments that paste is obtained from mixing four basic ingredients: Flour, water, yeast and salt. Depending on the baker´s, other ingredients can be addicted improving the paste feature and, therefore, in the quality, such as shown in the Table 1. Table 1: Ingredients of French bread (%) Ingredients Composition (%) Flour 100 Water 60 to 65 Biological yeast 1 to 6 Sugar 2 to 6 Salt 1.5 to 2 Vegetal fat 2 to 6 Source: Adapted from McWilliams (1989) The French bread production process, which consists of mixing ingredients to the final product, is described as follows. -

Scaling the flour in the storage; There is a routing time from the storage to scales to be implemented in the model, and this can be obtained if it is known the routing velocity of the employee. Colmanetti (2001) modeled a Parts Distribution Center, and one of the input data to his model is the information about employees´ routings. This author considered as reasonable a human´s velocity as 0.75m/s. This velocity, therefore, will be used in this work for all routings among equipments. Routing 1: To weigh all ingredients separately. This step is realized in the scales;

7 CURRENT – OBTANTION OF TOTAL FLOW DISTANCE AMONG EQUIPMENTS There are some procedures which need to be defined to obtain the total distance, such as shown in item 1 to 4. 1.

Define locals by numbers or letters;

Shih, Neto and Filho 2.

Define distance between equipments and construct a Table From-To such as shown in the Table 2; 3. Draw all possible flows that employee can realize among equipments; 4. Construct a table similar to Table 3, where is shown the number of flows. The distances among locals where equipments are located can be seen in the Table 2. These data were obtained from the Figure 2, which is similar to the real. Table 2: Distance From-To (in meters)

8 VALIDATION OF THE SIMULATION MODEL It is possible to realize the modeling after defining the sequence of the production. The model is important to test any proposal before implementing in the real system. For that, it is necessary for the model to operate according to the current situation, that is, validated, to be possible to test new proposals obtained of the topic 9.4. Figure 4 presents the model of baker´s modeled in Arena 5.0. The number of replications is 30 and the size of replication is five hours. After 30 replications, 66800 units are produced (that is 66800/30=2226.67 2226.67 units/5h=445.33 units /h. 9 TRAVELING SALESMAN PROBLEM-TSP

9.1 Figure 3 shows all possible flows of the current situation (based on the information of production process). Full capitals of this figure represent locals where equipments are located. Remind also that some routings are realized more than once. Note also that employees realize some returning flows making resulting in extra routings. There are two reasons. First is because of the difference between the current position of equipments and production sequence. And second, to the difference among the production capacity of equipments.

Reason for the Use of Heuristics

There is an issue between the arrangement of equipments and sequence of production when the system deals with several types of products, due to the sequence in which each product travels among equipments is different. In the period of the physical installation, the owner realized by feeling, which means that probably the current arrangement can not be adequate. According to Francis and White (1974), it is common to alter the position of equipments, also called rearrangement problem. Rearrangement problems can be solved by Traveling Salesman Problem – TSP, since equipments can be represented by nodes. Some applications of TSP are shown in Gademann et al. (2001) and Goetschalckx and Ratliff (1988), for instance. 9.2

Sequence of the Production x Routing obtained by TSP

The resources remain in the same place and can be seen as nodes. The employee routes all nodes to complete the production. In fact, nodes are considered independent in TSP, since it defines only the best routing but not analyzing the precedence activity issue. That is, in the case of baker´s, there is a pre-defined sequence of nodes since it represent steps of production. Figure 3: Current production routing

Table 3: Routing Times in a Period of Five Hours

Shih, Neto and Filho

Figure 5: Flow obtained from TSP Figure 4: Flowchart of the production model 9.3

Definition

This is a nodes covering problem, also named as Traveling salesman problem, in which is desired to obtain an routing with the lowest length beginning in a certain node of the graph and passing all nodes “at least” once, and return to the initial node. Nevertheless to solve it, it is necessary to define TSP1. TSP1 is a particular TSP, since nodes should be visited exactly one time. According to Morábito (2004), the algorithm TSP1 works with a completely connected graph and it is necessary to analyze the triangular dissimilarity. The result of the TSP1 is TSP. Step 1: Find the Minimum Spanning Tree, connecting n nodes. Name it as T. Step 2: Be m the number of nodes of T with odd degree. (remember that m is always even). Find the “minimum-length pairwise matching” of these m nodes, and identify m/2 minimum routings of optimum matching. Call it as M. Step 3: Construct new graph H from adding T and M. Note that H does not contain nodes of odd degree. Find an Euler circuit in H – This circuit is an approximated solution of TSP1. Step 4: If nodes are visited “more than once” in the Euler´s circuit, improve the routing considering the triangular dissimilarity, to obtain Hamilton´s cycle to H. This cycle is also an approximated solution for TSP1. The result of the application of TSP can be seen in Figure 5.

The first strategy suggests lower distances be routed much more times making total distance ∑ (Routing (i)* number of times (i)) be reduced. But this strategy does not ensure there is not flow of return due to the sequence of production, which means there is possibility to have an increased total distance. There is an other possibility to evaluate the impact of the relation “routing x number of routed times” on the total distance remaining the layout of the current flow, however altering initial nodes. For instance, in the example above, it is considered that employee begins at local A to realize the production 1. Now equipments have their position changed, making that production 1 be realized in local E, for example. In other words, locals remain unchanged, but machines not. It is necessary, therefore, to define the local for the operation 1 (initial position of storage). Depending on where initial node begins, it may obtain great reductions in the total distance. Figure 6 shows flows and its respectively routings when the storage is changed from node A to node C.

Figure 6: Example in which the initial node becomes node C 1. Define initial node, that is to define where the storage is located. For instance, in the case where

Shih, Neto and Filho

2. 3.

the initial node be A, the employee travels to node B, then node E, and so on. If node C were initial node, then this will become node A and B becomes D and Node E becomes G and successively. Redesign flows and calculate routed distance (special attention for the number of routed times). Compare distances and analyze the production for each obtained distance.

production and consequently could be enhance the total production. But it does not occurs. Some analysis suggest to increment the employee´s velocity could possibly reduce the routing time. New experiment results are shown in Figure 7. This Figure shows the relationship between two parameters (employee´s routing velocity and production). Making initial node be in all ten nodes, it present the same results. Daily production (5h) x Employee´s velocity(m/s)

Reallocation of the storage to all 10 nodes, can be obtained the total distance through five hours, such as shown in the Table 4.

Node in

Distance (m)

Current

A

319.7

Best Situation

B

312.5

C

329.8

D

409.35

E

504.91

F

573.54

G

628.85

H

603.5

I

483.09

J

416.05

Worst Situation

9.4

Application of Suggested Alteration by TSP and Analysis of the Results

The main idea was, with reduction of the total distance, employee could reduce the spent time in routing to increase the production, but executing the simulation model for ten different initial nodes, it provide a production quantity of 445,33 units/h. Note even growing machine demands one hour, it is not considered as bottleneck due to processing times of previous production steps equilibrates with the relaxing times in the growing machine. The research shows that the initial disposition, based on the owner feeling is considered efficient, since the total distance of current routing is closer to the lower distance of Table 4. The variation of distances, such as shown in Table 4, does not affect the produced quantity. In theory, as lower routing distance, the time spent in routing is now spent in

Daily production (units)

Table 4: Reallocation of Nodes and Its Routing Distances Respectively Initial Routing

2228 2227 2226 2225 2224 2223 2222 2221 2220 2219 0

0,2

0,4

0,6

0,8

1

1,2

Velocity (m/s)

Figure 7: Variation of production in relation to the velocity When the employee´s velocity is reduced the production is not affected, but it is not true when the velocity is lower than 0.2 m/s. In this case, the employee becomes a bottleneck. It is obvious that can be also adopted an alternative strategy by increasing the shift of the work to six hours or more. However it should not be considered since the reduction of the working shift is just the goal of most baker´s. The results show also that altering storage position to the current position E, F or G (due to the dimension can fit high volume routing) not increase or reduce the production, however the distance to be routed increases. This means that unloading activity of ingredients can be realized in one of three nodes (top of Figure 2). 10 CONCLUSION This research shows the physical disposition of equipments based on employees knowledge usually works well, due to usually the allocating equipments step is based on the analysis of closer distance x number of times. That is, when more times, allocate equipments with lower distances. However reducing this distance not implies in increasing the production, since the production time can be superior to the routing. Reallocating equipments does not avoid returning flows because the production sequence can be require the same machine more than once. Moreover, the processing capacity of an certain equipment may make employee to

Shih, Neto and Filho realize several previous steps to reach such capacity. This means that allocating lower distances to be routed several times does not guaranty distance flow reduction. The impact of routing velocity affects the total production, but this can be insignificant since the total time is composed also by the processing time. That is, the processing time can be so superior that routing becomes insignificant. REFERENCES Armour, G. C., and E. S. Buffa. 1963. A heuristic algorithm and simulation approach to relative location of facilities. Management Science 9 (1): 294-309. Askin, R. G., and C. R. Standridge. 1993. Modeling and analysis of manufacturing systems. New York: John Wiley & Sons. ASN (2005) – Agência Sebrae de Notícias: os pequenos negócios em pauta. Distrito Federal. Available via [accessed July 24, 2006]. Brazilian Association of Making-Bread Industry. 2002. Produção de pão no Brasil: Perfil de mercado. . São Paulo, 2002. Available via [accessed August 12, 2003]. Colmanetti, M. S. 2001. Modelagem de sistemas de manufatura orientada pelo custeio das atividades e processos. São Carlos: Dissertation – School of Engineering of São Carlos, University of São Paulo. Ferreira, P. B. M., E. Watanabe, and V. T. Benassi. 1999. Estudo do processo de produção de pão francês préassado. Brazilian Journal of Food Technology 2 (1-1): 91-95. Filho, E. V. G. 2005. Sistemas de manufatura - projeto de arranjo físico. São Carlos: Class notes, School of Engineering of São Carlos, University of São Paulo. Francis, R. L., and J. A. White. 1974. Facility layout and location. New Jersey: Prentice-Hall. Gademann, A. J. R. M., J. P. V. D. Berg, and H. H. V. D. Hoff. 2001. An order batching algorithm for wave picking in a parallel-aisle warehouse. IIE Transactions 33: 385-398. Garcia, M. L. et al. 1999. A simulation of the product distribution in the newspaper industry. In Proceeding of the 1999 Winter Simulation Conference, 1268-1271. Goetschalckx, M., and H. D. Hatliff. 1988. Order picking in an aisle. IIE Transactions 20(1). Heragu, S. 1997. Facilities Design. Boston: PWS Publishing Company. Hutt, M. D., and T. W. Speh. 2001. Business Marketing Management – a strategic view of industrial and or-

ganizational markets. USA: Harcourt College Publishers, 2001. Jansen, D. R. et al. 2001. Simulation model of multicompartment distribution in the catering supply chain. European Journal of Operational Research 133(1): 210-224. Kusiak, A. 1990. Intelligent manufacturing systems. New Jersey: Prentice-Hall. Lee, R. C., and J. M. Moore. 1967. CORELAP – Computerized relationship layout planning. Journal of Industrial Engineering 18(3): 195-200. Mcwilliams, M. 1989. Food Experimental Perspectives. New York: Macmillan Publ. Com. Meyers, F. E., and M. P. Stephens. 2000. Manufacturing facilities design and material handling. New Jersey: Prentice-Hall. Morábito, R. 2004. Pesquisa operacional aplicada a logística empresarial: class notes. São Carlos: Ufscar. Muther, R. 1978. Planejamento do layout: sistema SLP. São Paulo: Edgard Blucher. Neghabat, F. 1974. An efficient equipment layout algorithm. Operations Research 22: 622-628. Nutrinews. 1999. Semi assados: a nova era da panificação. Ed. 161. Available via [accessed January, 2002]. ProgelFood. 2006. Available via [accessed July 17, 2006]. Renaud, J., F. F. Boctor, and J. Ouenniche. 2000. A heuristic for the pickup and delivery traveling salesman problem. Computers & Operations Research 27: 905916. Roodbergen, K. J., and R. De Koster. 2001. Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research 133: 32-43. Seehof, J. M., and W. O. Evans. 1967. Automated layout design program. The Journal of Industrial Engineering 18(12): 690-695. Silva, M. E. M. P., G. H. Yonamine, and L. Mitsuiki. 2003. Desenvolvimento e avaliação de pão francês caseiro sem sal. Brazilian Journal of Food Technology 6(2): 229-236. Silva, R. 1990. Phospholipids as natural surfactants for the cereal industry. Cereal Foods World 35(10): 1008 – 1012. Singh, N., and D. Rajamani. 1996. Cellular manufacturing systems: design, planning and control. New York: Chapman & Hall. Teed, A. R. 1983. A look at French “French Bread”. Cereal Foods World 28(27): 397-399.

Shih, Neto and Filho AUTHOR BIOGRAPHIES SHIH Y. CHIN is a Doctoral degree student by the Mechanical Engineering of São Carlos (USP) – Brazil. He received his MSc. in 2005. His researches are focused on modeling, optimization, improvement and scenario evaluation of manufacturing systems. He is graduated in Production Engineering in 2002 at the same College. His e-mail address is . ANSELMO R. P. NETO is a graduate degree student by the Mechanical Engineering of São Carlos (USP) – Brazil. His researches are focused on modeling, optimization, improvement and scenario evaluation of manufacturing systems. He is graduated in Production Engineering in 2005 at the Federal University of Ceará. His e-mail address is . EDUARDO V. G. FILHO received his PhD degree in Mechanical Engineering in 1989, and since 2004 is a Professor in the School of Engineering of São Carlos of University of São Paulo. His researches are focused on design, optimization, improvement, and evaluation of manufacturing systems. His e-mail address is .

Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds.