Solving the log-truck routing problem while accounting for forest road maintenance levels: a case study of Oregon

Solving the log-truck routing problem while accounting for forest road maintenance levels: a case study of Oregon Amin Keramati 1 1 Transportation& Logistic Department, North Dakota State University, Fargo, ND, Amin.Keramati@ndsu.edu Abstract Transportation expenditures play a key role in timber studies, which is why there is much of research associated with log-truck problems. There are a substantial number of criterions that effect timber transportation costs and the most important one is distance; the reason is that most other criterions, like pollution and travel time relied on distance. This study is conducted based on the effect of forest road maintenance levels on travel time. There are two types of maintenance levels, operational maintenance level and objective maintenance level. Forest roads with different maintenance levels can have different speed limitations or can be blocked temporarily and decommissioned. In this study we assess the travel time between wood mills and different timber areas while considering the impact forest road maintenance levels. We also conduct analysis of the objective maintenance level and improvement measures impacts on travel time and optimized routes. The analysis is done by SAS 9.2 based on the analysis of variance. The results indicate that objective maintenance policies cause an increase in travel time of log trucks to timber areas. Key Words: Log transportation, routing, economic application of GIS, maintenance level 1

Solving the log-truck routing problem while accounting for forest road maintenance levels: a case study of Oregon 1. Introduction The forestry industry manufactures goods from timber grown in forests. It provides various types of products including paper, wrapping, building materials, and furniture. As the wood harvesting industry has advanced over the years, public concerns and ancillary uses of the forest have developed in parallel with the development of the forestry industry. As a results of the state s soil and climate creating specific conditions to grow commercially viable species such as Douglas fir and ponderosa pine, Oregon is one of the largest tree-growing areas in the world. According to the State of Oregon Employment Department [1], forests cover almost half of the state s landmass which is more than 30 million of Oregon s 62 million acers. The estimation of Oregon Department of Forestry (ODF) indicates logging totaled 3.8 billion board feet in 2015. The main three industries associated with forestry and logging subsector are timber tract operations, forest nurseries and gathering of forest products, and Logging. In terms of employment, according to the OED s (Oregon Employment Department) covered employment statistics, the subsector s 829 firms employed 9,501 people statewide and added about $500 million in payroll to Oregon s economy in 2012. Moreover, employment was in slow decline between 2005 and 2009 and has since leveled off and begun a recovery, varying seasonally in a band between 8000 and 10000. Moreover, 6057 are employed in the private sector while 3,444 are employed in government. Most of the government employment is at the federal level at 3,249 while the rest is at the state level. Forestry operations consist of many different activities such as planting building access to forest locations, and transporting the harvest to wood mills. In Oregon, the distances between forest locations and wood mills are generally large. Furthermore, backhauling, which is usually performed empty, represents a waste of resources (time, fuel, etc.). It is thus important to reduce unproductive activities during transportation, for both economic and environmental considerations. The purpose of this study is assessing the impact of the maintenance level of forest roads on the log truck routing problem based on minimizing the travel time between wood mills and timber areas. In this study we use network analysis and closest facility solver in Arc GIS in order to find the best routs and timber areas associated wood mills in Oregon. 2. Literature Review In order to improve the efficiency of forestry industry, several forestry projects have been conducted since the mid-1990s. We can mention studies of Weintrub et al. [2] with the ASCAM project, and the EPO system developed by Linnainmaa et al. [3]. A column generation scheme for tackling the Log Truck Scheduling Problem was proposed by Palmgren et al. [4]. 2

Generally we can define different methods for solving the log truck scheduling problem, the first one is creating a mathematical model and solving the model by different methods such as heuristic algorithm, using GIS estimations tools, and the combination of cited methods. In terms of the first main method, Gronalt and Hirsch [5] applied Tabu Search (TS) heuristics in order to solve a restricted variant of the LTSP; in their study the destination of each load is given a prior. Moreover, El Hachemi et al [6] solved LTSP restricted variant by proposing a two-step hybrid solution procedure. In El Hachemi et al. [7], authors solved this problem again through constraint-based local search procedure and a mixed integer programming model to create a weekly LTSP, in which the assigned load between forest areas and mills can be optimized. Flisberg et al. [8] and Andersson et al. [9] proposed a two-phase approach to solve the daily problem. In order to compute the wood flow which is loaded from supply store to demand area, an LP model is used; then, the result is passed to a second model sequencing the transportation points into completed routes using a standard TS. Ronnqvist and Ryan [10] and Ronnqvist et al [11] proposed the procedure of building a route one trip at a time. Beside, Rey et al. [12] addressed the problem of forest products delivery at different destinations by assigning limited number of trucks daily. The second main method for LTSP is using GIS tools for estimation and finding the best out and scheduling for trucks. For forestry companies, plan and follow up transports play the key roll. Each transport compromises information about assortment, volume, departure starting point in the forest, ending point at industries, contractual distance, time stamps, carrier, wood owner, etc. One key issue is how to compute this contractual distance. Moreover, the agreed distance is very important as this forms the basis for invoicing and payment. For example, it is easy to compute the shortest path or the fastest path. To find such a distance between a pair of nodes in a network, it is possible to solve a shortest path problem [13] using, for instance, Dijkstra s Algorithm. Efficient implementations of Dijkstra s Algorithm in geographical information systems (GIS) are described in Zeng and Church [14]. A major problem is that a shortest path or quickest path may not be the best path between the forestry company and the transporter. Akay et al. [15] states an application in which the distance is based on distance and safety. The application arises when there are fires and there is a need to combine safety with route length. In Verter and Kara [16], the application is to move hazardous material, and there is a need to find routes such that any impact of an accident is minimized. In Apaydin and Gonullu [17], the purpose is to minimize emissions and not distance. Devlin et al [18] analyzed a set of routes with GPS and compared its results with the shortest path data found in a GIS by using four attributes including road length, road class, road speed, and journey duration. Their results showed that the shortest path distance determined by GIS did not replicate the actual GPS routes. Eventually, Martin et al [19] used the ArcView network analyst program to estimate shortest timber transportation path. In this study the main problem is finding the best routes and the best harvesting areas for optimizing the log trucks travel time between mills and timberlands. According to the cited literature, there are not studies considering the impact of forest route maintenance levels on log-truck routing problem. Since forest routes with different maintenance 3

levels have different corresponding speed and vehicle type limitations, it can significantly impact on route travel time. In addition, by considering maintenance levels we can consider that some forest routes are blocked and trucks are not able to use them; it can also restrict trucks from accessing to part of timberlands. Which is why, in this study we solve the log-truck routing problem while accounting for forest road maintenance levels. 3. Model Development/ Methods As the main focus of this study is on optimization of log-truck scheduling in Oregon, the first data is related to the Oregon State boundary and its counties which both are polygon with geographic coordinate system of GCS_North_America_1983 and its datum is D_North_American_1983 which datum and coordinate system we use as default coordinate system. Cited data are obtained from Oregon Spatial Data Library website [20]. The main problem is finding the best timber area and also the best route to arriving there; forests cover more than 30 million of Oregon s 62 million acres with various species of trees and applications. The transportation of logs can be applicable between mills and the forestry areas that are suitable for timber activities, so we use a forest layer of Oregon within its attribute table including the usage of different parts of forests. Thereby, we can select just the forestry areas that are suitable for timber production; this layer is obtained from the forest service part of the USDA (U.S. Department of Agriculture) web site [21]. Different saw mills can meet their log demands by transporting logs from timberlands with different locations, so we consider more than one origin for each mill and carry out the analysis of selecting the best origin (timberlands) based on the travel time. In terms of saw mills we use data related to all western U.S saw mills with point shape from the USDA web site [22]. This layer included mills that purchase logs or chips and excludes secondary processors of wood, such as paper mills that buy market pulp or rely entirely on recycled fiber. This layer attribute table includes different information of saw mills such as, longitude and latitude, street address and type including Sawmill, Composite, Plyven, Postpole or Pulpmill as well as others. In terms of roads, two separate layers are used, the first one is the layer of the national forest system roads [21], and the second one is the U.S highway network [23]. The forest roads layer includes roads that connect different spots of forests, these are the main part of our research because logtrucks use them for log transportation and these roads connect mills and timberlands. In some cases mills are far away from timber lands and trucks also use interstate highways. The attribute table of forest roads includes lots of information including each road segment length and both the operational and objective maintenance level of each segments. We also join the other forest road table layer to the main forest roads layer in order to consider that forest roads are open for trucks. The Timber Points are created through clipping network junction points by the Timber forest area; the resulting points of the roads located in timber forest area. 3.1. Framework The operational maintenance level is the maintenance level to which the road is currently being maintained, but the objective maintenance level is the desired level of maintenance with 4

consideration for future needs, budget constraints, and environmental concerns [21]. According to the USDA web site Forest service [21], there are 5 levels of maintenance: a) Maintenance level 1 includes roads that are inactivated and managed in a stored or closed category for more than one year, primarily for resource protection and safety reasons. b) Maintenance level 2 includes roads that are suitable for high clearance vehicles and are not maintained for passenger car use. c) Maintenance levels 3, 4 and 5 include roads that are suitable for use by low clearance vehicles (passenger cars); those in levels 4 and 5 provide a higher degree of user safety, comfort and convenience. In order to calculate the travel time of each road segment, we need the length of each segment and its speed limitation, and for estimating the speed limit of forest roads other than realizing the maintenance level of each segment, we need to define road classifications including primary, secondary and so on. Based on the USDA-website, we can define roads including high standard through-routes maintained for standard passenger cars as primary roads and secondary roads are those which include key inter-forest connections maintained for high clearance vehicles. The third class is other roads including candidates for review under area watershed analysis for reduction of maintenance standards or decommissioning. In order to assess the impact of operational and objective maintenance level on travel time and routing, we define three scenarios: Scenario 1. If the road operational maintenance level is 1, it indicates that the road has been closed for more than one year, so we consider the roads with operational maintenance level 1 as barriers when solving the problem of finding the best route with shortest travel time. Scenario 2. If the road objective maintenance level is 1, it indicates that according to the future plans, the road will be closed for maintenance processes; moreover, objective maintenance information also defines if the road will be decommissioned or not. Therefore, we consider the roads with operational maintenance level 1 and decommissioned situations as barriers when solving the problem of finding the best route with shortest travel time. Scenario 3. In this case we solve the problem with considering the fact that routes with maintenance level one would be improved. Analysis of the results associated to each Scenario can indicate how the impact pattern of current, objective, and final maintenance policies would be on selecting the best rout and timber area. Other than barriers related to each maintenance level, maximum speed is the other criteria that impacts travel time. We define the speed limits of each route based on the different maintenance level. Firstly, we obtain the speed limits based on different road classifications from a number of different sources including the insurance institute for highway safety [24] and the American Tracking Association website [25] (see Table. 1). According to the definition of maintenance levels (2, 3, 4, and 5), secondary roads and speed limit of different classification (Table. 1), the maximum speed associated to routes with maintenance 5

level of 2 can be 50 mph, and that of routes with maintenance level of 3, 4, and 5 can be 55 mph. The maximum speed for Oregon highways is 65 mph. Based on cited speed limits, we estimate the travel time of each route segment, then the road network was created according to the cited route segments travel times. Road Classification Table 1. Speed Limit based on Road Classification Road Description Speed limit (mph) A1 Limited access highways, usually interstates. 65 A2 Primary road, usually State Highways with some access. 55 A3 Secondary Highways 50 A4 Streets 30 A5 Trails. 20 A6 Special roads, commonly on/off ramps 10 A7 Other roads, including private roads like those in big condo complexes, etc. 15 Route assignment was done by using the closest facility solver in ArcMap 10.3. The closest facility solver finds the cost of traveling between incidents and facilities and determines which are nearest to one another. In terms of this study we introduce the timber areas as the facilities and wood mills as incidents while minimizing travel time (impedance). Moreover we consider the route segments with maintenance level 1 or decommission situation as barriers of model. The output of this solver is defined timber areas and routes with the shortest travel time to connect all wood mills to cited timber areas. 4. Case Study 4.1. Scope of the study As it was referred in introduction part we consider Oregon State as a case study due to having power full log industry and huge areas of forests and forest roads (Figure 1). By spatial analysis tools of ArcMap, we define which forest are only useable for timber activities. Therefore we filter timber areas from other forest and the results is 3.3 million acres timber area. 6

Figure 1. Oregon State Highways and Forest Roads. The attribute table of wood mills indicated that wood mills have three situations of open, closed, and without situation. Therefore we decided to consider only the wood mills have open situations. All of the open wood mills are located in the west part of the Oregon, so our study was focused on the west properties of Oregon. We used extract tools and separate the west timber areas and west network roads. After extraction of west state roads, 5548.356488 mile highways and 38006.783 mile forest roads are remained. There are 13 wood mills with clear open situation. We conduct the analysis of closest facility for three region of North West, West, and South West (Figure. 2). There are 5, 6, and 2 open wood mills in northwest, west, and southwest region respectively. The separate network analysis is conducted for each region and through closest facility solver the best routes and forest areas is allocated for each wood mill of cited regions. Figure 2. Study Area of Oregon State 7

4.2. Results of GIS Applications In order to analysis of the three scenarios effects on travel time of each region, we create three separate network associated to each region, then we use closest facility solver three times for each network in order to finding the best routes related to each scenario. Following illustration and explanation can refer to model outputs. 4.2.1. North West network analysis Figure 3 illustrates the solution of closest facility solver related to the northwest network; the red spots indicate the forest roads which are closed as a result of operational maintenance policies; in other words, these roads have operation maintenance level of 1. The legend shows the travel time of each selected route in hour. Moreover, this figure illustrates that considering barriers related to the first scenario (closed route), the closest timber area to the northwest wood mills is the 753 timber area. The path connecting Georgia Pacific mill to the selected timber area have the longest travel time of 2.69. Figure 3. North West Counties, Scenario1 Routing Solution. 8

Figure 4. North West Counties, Scenario1 Statistical Routing Information. In terms of northwest area routing solution associated to scenario 2, solver could not find the best path because all of the roads ended to each timber area in northwest region is closed or decommission. For solving this problem we expand the road network to the middle region (west region) in order to finding the closest timber area in that region (Figure. 5). Note that in the expansion policy, condition of scenario 2 is still applied. Figure 5. Northwest Log-truck Alternative Routing Solution in Scenario 2 condition. Figure 6. Statistical info Associated to Northwest region Alternative Routing Solution in Scenario2 condition 9

Figure 5 obviously indicates that the solver selects the timber area 9541 which is located in west region. By comparing the average travel times of the assigned routes in Scenario 1 (1.86 hour) and 2 related to the alternative solution (4.5 hours), we can conclude that after applying objective maintenance policies, the travel time would significantly increase by %141 (See Figure 4 & Figure. 6). It is rooted in the substantial numbers of blocked routes making trucks use far timber area. After improvement condition (scenario3), the average travel time can be decreased to 1.27 hour (figure. 7). It is logical that after improving all roads, the number of barriers significantly would be decreased and the solver have more alternatives to find the route with shortest travel time. Based on third scenario the closest forest area in northwest wood mills would be timber area 626 (Figure. 8). Figure 7. North West Counties, Scenario3 Statistical Routing Information. Figure 8. Northwest Log-truck Routing Solution in Scenario3 condition 10

4.2.2. West Network Analysis (Middle Region) In terms of west network, more than one timber area is assigned based on all scenarios. As can be seen in Figures 10 and 12, based on first and second scenario, there are two timber areas which are assigned to the wood mills. However, the average travel time between mills and timber areas associated with the first scenario (0.94 h) is less than the travel time to areas assigned based on second scenario condition (1.017h) (Figures 9 and 11). Figure 14 indicates that west region mills access to the timber areas is improved; the reason is access to the new timber area of 12821 as a result of improving the forest roads with maintenance level 1. Using this new timber area on average decreases the travel time by 0.7 h (Figure. 1). Figure 9. West Counties, Scenario1 Statistical Routing Information. Figure 10. West Counties, Scenario1 Routing Solution. 11

Figure 11. West Counties, Scenario2 Statistical Routing Information. Figure 12. West Counties, Scenario2 Routing Solution. Figure 13. West Counties, Scenario3 Statistical Routing Information. 12

Figure 14. West Counties, Scenario3 Routing Solution. 4.2.3. South West Analysis (Middle Region) In south west network, objective and operational maintenance policies have similar effect on travel time and selected timber areas, so in both scenarios 1 and 2, the same routes and timber areas are selected (figures 15, 16, and 17). However, according to the figures 18 and 19, if roads with maintenance level 1 become accessible, trucks whose destination is Collin Products wood mill can use timber area 1640. This accessibility declines the average travel time by %59. Figure 15. South West Counties, Scenario1&2 Statistical Routing Information. 13

Figure 16. South West Counties, Scenario1 Routing Solution. Figure 17. South West Counties, Scenario2 Routing Solution. 14

Figure 18. South West Counties, Scenario3 Statistical Routing Information. 5. Results and Discussion Figure 19. South West Counties, Scenario3 Routing Solution. In order to conducting the analysis of three scenarios effecting travel times, we use analysis of variance (ANOVA). ANOVA can used in the same condition as two-sample t-test. When independent variable has two levels, both two-sample t-test and ANOVA can be used, but when independent variable has three or more levels, only ANOVA can be used. Therefore, as the travel time between wood mills and selected timber area(s) is independent variable which is categorized into three groups (three scenarios), ANOVA can be a suitable analysis. The question is to test whether the three scenario makes any difference to the travel time between mills and timber areas. The set up model related to this problem is: Travel_ Time (continuous variable) ~ Scenarios (categorical variable with 3 levels) Table 2 indicating statistical information of each assigned route and its scenario. All of the statistical estimations are programmed in SAS 9.2. The ANOVA is conducted based on the 15

assumptions that our data are normally distributed and have similar standard deviation (σ1 = σ2=σ3). Obs Timber_ Area ID Table 2. Statistical Information of GIS Results Name Incident ID Total_ Length Total_ Time Scenario 1 626 Blue Heron Paper Co. 1 59256.45 0.64380 3 2 753 Blue Heron Paper Co. 1 105678.33 1.23527 1 3 216 Blue Heron Paper Co. 1 379166.93 4.02448 2 4 626 Boise Cascade Corp. 2 115635.15 1.22558 3 5 753 Boise Cascade Corp. 2 162057.04 1.81704 1 6 216 Boise Cascade Corp. 2 410237.38 4.38206 2 7 1640 Collins Products 1 46638.79 0.44381 3 8 2514 Collins Products 1 193672.47 2.07835 2 9 2514 Collins Products 1 193672.47 2.07835 1 10 12821 Evanite Fiber Corp. 1 59761.98 0.66600 3 11 12685 Evanite Fiber Corp. 1 84485.62 0.98565 2 12 12802 Evanite Fiber Corp. 1 83219.56 0.97266 1 13 12821 Georgia-Pacific Inc. 2 83279.48 0.90336 3 14 12685 Georgia-Pacific Inc. 2 155834.57 1.80689 2 15 12685 Georgia-Pacific Inc. 3 50640.03 0.58087 2 16 12802 Georgia-Pacific Inc. 2 154568.51 1.79390 1 17 12802 Georgia-Pacific Inc. 3 49373.97 0.56788 1 18 13139 Georgia-Pacific Inc. 3 49373.97 0.56788 3 19 626 Georgia-Pacific Inc. 3 191901.12 2.09890 3 20 753 Georgia-Pacific Inc. 3 238323.00 2.69037 1 21 216 Georgia-Pacific Inc. 3 486503.34 5.25539 2 22 2514 SierraPine Ltd. 2 71332.82 0.73192 2 23 2514 SierraPine Ltd. 2 71332.82 0.73192 1 24 2514 SierraPine Ltd. 2 71332.82 0.73192 3 25 626 SP Newsprint Co. 4 103357.48 1.13291 3 26 753 SP Newsprint Co. 4 149779.37 1.72438 1 27 216 SP Newsprint Co. 4 405419.58 4.35353 2 28 626 Stimson Lumber Co. 5 112959.18 1.27311 3 29 753 Stimson Lumber Co. 5 159381.06 1.86458 1 30 216 Stimson Lumber Co. 5 414115.19 4.48586 2 31 10759 Weyerhaeuser Co. 4 72049.43 0.84738 2 16

32 12821 Weyerhaeuser Co. 6 75008.19 0.83972 3 33 12685 Weyerhaeuser Co. 5 76961.29 0.86842 2 34 12802 Weyerhaeuser Co. 5 75695.23 0.85543 1 35 7011 Weyerhaeuser Co. 5 48598.24 0.55179 3 36 6771 Weyerhaeuser Co. 4 51525.48 0.58112 1 One way ANOVA is based on F-distribution and according to the ANOVA results (Table 3). The F-test statistics value is 6.22 with a P-value of 0.0051. Since the p-value is less than 0.05, we reject the null hypothesis and conclude that the impact of three scenario on routes travel_ time were not all the same. Moreover, the ANOVA indicates that scenarios have significant effect on log-truck routing. In other words, objective and operational maintenance levels of roads play the key role in finding the routs with shortest travel time between wood mills and timber area. Table 3. Analysis of Variance Results Source DF Sum of Squares Mean Square F Value Pr > F Model 2 16.36948454 8.18474227 6.22 0.0051 Error 33 43.39019935 1.31485453 Corrected Total 35 59.75968389 5 Distribution of Total_Time F 6.22 Prob > F 0.0051 4 Total_Time 3 2 1 1 2 3 Scenario Figure 20. Assigned routs Travel Times Classified by Scenarios The statistical results shows that 50 percentage of routes assigned by third scenario have travel times almost between 1 to 4 hours, but in terms of the first scenario, that is between around 1 and 2 hours. If we compare the median of assigned routes in three scenario, we can conclude that 50 percent of second scenario assigned routes have almost more than 2 hours travel time, but in terms of first scenario and third one, that is 1.48 and 0.78, respectively (Table 4). Distribution of the total time based on 3 scenario clearly illustrates that not only objective maintenance policies (Scenario 3) could not be useful for decreasing the travel time of log trucks, but also it would cause increasing in travel time (Figure. 20). 17

Level of Scenario Table 4. General statistical information of travels time classified by Scenarios N Total_ Time (hour) Mean Std. Dev Median 1 12 1.40940633 0.68023378 1.480 2 12 2.53340008 1.80851505 1.942 3 12 0.92323142 0.45947675 0.785 6. Conclusion In this study, we solved the log-truck routing problem with considering the operational and objective maintenance levels of forest roads. There are several of number of papers about solving the log-truck routing and scheduling problems; the vast majority of these studies use mathematical models for solution. This study solved log-truck problem by using network analysis programming of GIS and closest facility solver. The closest facility solver was used due to its conformity to the log- truck problem. The proposed model is designed to solve two main problems of log-truck routing; the first one is finding the path with shortest travel time which is able to connect mills to timber point(s), and the second one is finding the closest timber area. Both cited main problems can be solved simultaneously by closest facility solver of ArcMap. In order to assess the effect of objective and operational maintenance levels on log-truck routing, we solved the problem based on the three scenarios. The first scenario causes model to solve the routing problem based on operational maintenance conditions including barriers and speed limitations. According to the second scenario the model was solved based on the objective maintenance conditions, and finally in the third scenario, model was solved while routs condition was assumed to be improved. Results of closest facility solver indicated that the average travel time of second scenario is around 1.80 hours which is significantly longer than that of other scenarios. Such results can be rooted in the huge number of barriers related to the first maintenance level and decommission roads considering through objective policies. In order to decreasing the travel time of the log truck and costs associated to that, the objective policies should be improved, and roads with the first maintenance levels should be opened as soon as possible. For instance, wood mills of northwest part would use west timber areas because all of the roads in northwest area will be closed based on objective maintenance policies and it can incurred substantial expenditures related to the long travel time. Table. 4 clearly indicates that after improving the forest roads the average travel time of log tracks can be decreased around 34%. Finally, in order to assess if the three defined scenarios have significant effect on the routing and travel time, we used analysis of variance based on the statistical data resulted by ArcMap; results showed that these scenarios have significant effect on routing problem. All in all, what sets apart this study from previous ones is considering the maintenance level of forest roads on solving the log-truck routing problem. However, there is substantial numbers of indexes can effect log-truck routings, for instance costs of each road maintenance level, and building new forest routes could be effective. 18

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