Harvesting System Simulation Using a Systems Dynamic Model

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1 Harvesting System Simulation Using a Systems Dynamic Model Kieran D. McDonagh, Department of Forestry, Virginia Tech, Blacksburg, VA 24061; Russell D. Meller, Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, Rien J.M. Visser, Department of Forestry, Virginia Tech, Blacksburg, VA 24061; and Timothy P. McDonald, USDA Forest Service, Auburn, AL ABSTRACT: To compete in the current work environment in the southeastern United States, forestry companies and logging contractors must manage their harvest systems to ensure efficient production. Efficiency in this case can be defined in terms of tons of wood produced per unit of system cost. Site and system factors affect the output from a stand and affect the efficiency of different harvesting systems. A model was developed that can simulate harvesting system production efficiency with changing stand and terrain parameters. Four common southeastern US harvesting systems were included in the model. The model includes functions, adjustable by the user, that quantify the efficiency of each harvest system is measured with respect to terrain parameters (slope and average extraction distance), as well as stand parameters (average tree diameter, stocking intensity, and harvest intensity). By adjusting any of these parameters, it is possible to compare the four harvest systems and to perform various sensitivity analyses. Result examples presented in the article include change in productivity with increasing tree size, productivity changes of a cut-to-length system in varied silvicultural conditions, as well as the associated evaluation of the cost of inefficiency of each system. South. J. Appl. For. 28(2): Key Words: Timber harvesting, logging models, system dynamics, simulation models. Owning a timber harvesting (logging) contracting service is big business, and a typical contractor will have over US$1.5 million invested, with operating costs over US$500 per hour. Such businesses often work independently and are loyal to a single wood purchaser, either a larger timber-consuming company or wood dealer. The tracts they harvest are assigned to them by this organization, although a number of contractors purchase at least some of their own timber (Forest Resources Association 2002). A recent survey of loggers, wood dealers, and industry personnel indicated that logging contractors were typically assigned tracts of timber to harvest on a first-come, firstserved basis (Rodgers et al. 2002). Mismatching of the harvesting system to tracts was estimated to be a significant problem for 40% of all logging contractors. A timber harvesting operation will have four basic steps, with the wood being the product output at the fourth step. For the purpose of this article, the product starts as a standing tree and ends after being loaded onto a truck for transport. The optimization of truck schedules, truck routing, and mill inventory management are outside the scope of our study. The four basic timber harvesting steps are: (1) NOTE: Rien Visser can be reached at (540) ; rvisser@vt.edu. Copyright 2004 by the Society of American Foresters. felling the trees; (2) extracting the stems to a central landing area; (3) delimbing and bucking the stems; and (4) loading the stems onto trucks. The traditional method of chainsaw felling is quickly being replaced by mechanized felling, including feller-buncher machines and excavator-based machines with harvester heads. Extraction of stems is accomplished with the use of a skidder, which can either use rubber tires or tracks, with the timber either picked up using a grapple or using a winch and a set of wire rope chokers. An alternative to the skidder is the forwarder, which loads the stems onto a trailer, thereby carrying them out instead of dragging them out. Stems can be delimbed and bucked (cut-to-length) using a chainsaw, a separate machine with a processing head, or, more typically in the southeastern United States, using a trailer-mounted loader and a hydraulic buck-saw. The productivity and efficiency of each individual step is highly dependent on the stand and terrain parameters. Key stand parameters include the tree size and the volume of timber to be harvested (Gingras 1988a, Visser and Stampfer 1998). Key terrain parameters include the slope, soil strength, terrain roughness, and the extraction distances (Davis and Reisinger 1990, Epstein et al. 2001). Appropriate matching of both the overall system and the individual SJAF 28(2)

2 components that comprise the system is therefore critical in determining the overall efficiency and wood flow. Typical levels of desired knowledge about a system range from simply estimating the potential production per unit of time (Reisinger et al. 1986, Dremann 1986) to understanding the cause behind fluctuations in the estimated production as it varies over time (Baumgras et al. 1993, Aedo-Ortiz et al. 1997). Estimation of the output of a system can be applied to allocate systems to stands, schedule harvests based on estimated residency of a system in a stand, and for the costing of operations (Reisinger et al. 1986). At more detailed levels of interest, system production and knowledge of the variation of the production over time can help estimate the likely flows of wood to markets over planning horizons of days to weeks. Attributing variation or low production periods to causes can assist the user in identifying areas of the system where improvement could be made and how to achieve that improvement. For users desiring a more detailed level of knowledge, the system must be specified and modeled in a way that facilitates the identification of inefficiencies. Inefficiencies may be a suboptimal allocation of machinery to a type of stand or harvest (Gingras 1988a), poor work procedures in the operation of machinery (Gingras 1988b), or an imbalance within a system leading to a significant bottleneck (McDonald et al. 2001). To unlock the production potential, these fundamental issues need to be addressed at various levels of the decisionmaking process. Existing models are reviewed in the next section. In doing so, we can see that there is no model currently available that shows the response of stand and terrain parameters on productivity and production efficiency of a range of harvesting systems. Thus, the primary contribution of our research is to provide a model (which we call the Harvest System Assignment Model) to facilitate such detailed system analyses. Previous Types of Harvesting Models Harvesting models can be categorized into four general groups: (1) a single machine; (2) a group of machines/inwoods system; (3) the transportation system; and (4) treeto-mill systems. Single-machine harvesting models focus on the activity of a machine and how the activity of the machine is influenced by the environment and stand in which it is working. The application of single-machine simulations is typically detailed activity modeling to determine changes in productivity efficiency (Eliasson 1998, Eliasson and Lageson 1999), operator performance (Greene et al. 1987, Block and Fridley 1990), and the effect of the machine on the stand (Bragg et al. 1994, Wang et al. 1998) for operational planning. The expansion of a one-machine model to include two or more machines results in a situation where one machine can influence the operation of others (Randhawa and Olsen 1990, Aedo-Ortiz et al. 1997, Wang and Greene 1999, McDonald et al. 2001), adding a whole level of complexity. This is the focus of the grouped machines/in-woods system category of models. Simulation of a group of machines and in-woods systems is typically thought of as a chain of activities occurring sequentially with no feedback. However, rates and the distribution of inventory flows for one activity in the chain should be tracked and balanced against the rates and distribution of inventory flows for other activities immediately preceding or following. This allows a more complete coverage of a system to be made, including some level of interaction among machinery to be represented (Randhawa and Olsen 1990). The modeling of machine combinations in a system began in the 1960s, primarily using Fortran programming language that used flexible user-defined harvesting systems for determining productivity (Goulet et al. 1979, Goulet et al. 1980, Reisinger and Davis 1987). Harvesting system simulations were developed further by Randhawa and Olsen (1990) using network simulation. Their model provided a more detailed system specification capability and modeled machine interactions by utilizing buffer inventories between harvest sequence components to simulate machine blocking and starving. Developments in the 1990s have concentrated on isolating machines within the system in determining the effect of the stand, terrain, and system set-up on individual machines and the system. Baumgras et al. (1993) explored system configurations such as the number of fellers and skidders in manual felling, cable skidding systems. Aedo-Ortiz et al. (1997) focused on developing a simulation using a two machine cut-to-length system to examine the relationship between the simulation output and field study data. This study also touched on the opportunity to include more machines in the system to further evaluate system potential by adjusting the combination of machinery. Wang and Greene (1999) approached system simulation using interactive graphics to allow the user to control the operational procedures of machinery. This approach evaluates operator decisionmaking and allows procedural decisions, such as the spacing between bunched trees and felling patterns, to be evaluated in terms of feller and skidder productivity. The interactive nature of this simulation means that this simulation does not determine real-time machine interactions, such as blockages, starvation, and congestion. Interactions between extraction machines and loaders are considered in a detailed simulation study by McDonald et al. (2001). This study addresses machine blockages, starvation, and interaction, and determines the importance of factors such as bunch assignment in determining productivity of machinery and influencing interaction delays between skidders and loaders based on skidding patterns. Methodology A descriptive model was developed to meet the primary objective of aiding in the assignment of the correct harvesting system to a given stand. The main methodology used in 92 SJAF 28(2) 2004

3 this study is the application of system dynamics simulation models. The system dynamics simulation models for this study were created using the STELLA system modeling software package (High Performance Systems, Inc., Lebanon, NH). The STELLA system model has been implemented as a computer-based analysis tool and decision support system. A user operates the model in an iterative process to evaluate all combinations of interest ( what-if analyses ). At this point in time, the STELLA model is purely descriptive in nature. That is, the user specifies the values of the decision variables (i.e., which equipment to use) and the model provides the value of the objective function and lets the user know if any constraints are violated. As a result of user-defined iterations, the user can choose the best combination subject to his/her criteria. The STELLA modeling environment uses five basic building blocks to create more complex models. The five basic building blocks are stocks, flows, converters, connectors, and decision-process diamonds. These elements can be combined in a simulation model in ways that allow changes in the system state at either continuously-represented or discrete points in time. Stocks, represented in Figure 1 by Forest Stand and Landing Inventory are accumulations of flows into them less any flows out. Stocks allow the flow of material at continuous, discrete, or a combination of continuous and discrete points in time. An example of a flow that occurs between stocks is shown in Figure 1 by the item Harvest System. Depending on the type of stocks they are connected to and the method of calculation for the flow value, flows can be continuous or discrete. In this example the value of the flow rate is based on a relationship to the two converters Extraction Distance and Terrain Slope Converters have a wide-ranging role in STELLA. They can be constant or be external input functions that perform calculations. The relationship of the converters Extraction Distance and Terrain Slope with the Harvest System in Figure 1 is denoted with the use of connectors. Connectors link items that influence the behavior of other model elements. The fifth model element represented in Figure 1 is a decision-process diamond (labeled Landing Full ). These can contain submodels, which allow simplification of model structure while maintaining the necessary complexity in a simulation. For example, a landing storage limit can be set within the decision process diamond, and when that limit is reached, the Harvest System rate will be set to 0. These model elements are used in this research to create a continuous-time simulation model to evaluate harvesting system alternatives. This approach uses a systems dynamic model instead of a discrete-event simulation model. We chose such an approach even though discrete-event simulation can provide more accurate results (since, in our case, a discrete-event simulation model would model the system at the level of individual trees, thus, tracking each tree through each operation). However, this level of accuracy comes at a steep cost in terms of model development and the level and amount of data required from the user. We believe that any Figure 1. Basic example using STELLA building blocks. accuracy that is lost in our systems dynamic model is minimized with a small time interval and yet we still require a minimal amount of data from the user. Harvest System Assignment Model The Harvest System Assignment (HSA) model is intended primarily as an initial system assignment decision support tool. [A limited version of the HSA model is available through the Internet at: Although not fully functional, we believe the online model can be used to assess the potential applicability of our work.] A user will enter information for a specified stand to be harvested and can then compare how each system will perform. This information from the decision support tool, combined with knowledge about expected site impacts, can be used to assist the decisionmaker about assigning a system to the stand. The HSA model considers one harvesting system in isolation (i.e., we do not consider how multiple harvesting systems of the same type of different type interact with one another). Four common southeastern US harvesting systems were chosen (Sloan 2001): (1) manual felling with cable skidder most appropriate for difficult terrain and large timber; (2) mechanical felling with grapple skidder most appropriate for more uniform, plantation-like stands; (3) shovel operations variation of (2) above that uses a grapple on an excavator base to help prebunch the timber for the skidder, primarily used in swamp stands where skidders must remain on designated trails; and (4) cut-to-length harvester and forwarder combination used primarily in thinning operations with higher value timber. The concept chosen for this model was to assign a maximum productivity to each harvesting system and to then modify this value using multiplicative adjustments based on the value of stand and terrain parameters. The key stand parameters chosen were the piece size (diameter at breast height, or dbh), the distribution of the tree size (stocking per ha), and the harvesting intensity. The key terrain parameters included in the model are slope and average skidding distance. The final adjustment to the maximum productivity is based on delays, represented in both SJAF 28(2)

4 Figure 2. Example of productivity efficiency as a function of piece size. At each iteration the value for (Dia(t)) determines the productivity efficiency at time t. frequency and duration for typical operations to provide a more realistic wood flow output (production). Graphical Input Functions The STELLA graphical stand and terrain functions work in the following manner. For each iteration, a random number is drawn for each parameter from a normal distributions, with distribution limits specified by the user before the simulation begins. The random number is then input to the respective graphical function for that parameter returning a net decrease in productivity for the system of interest based on the value of that parameter. Two examples of how these functions operate are presented next. Illustrated below in Figure 2 is a graphical function for the effect of dbh on the productivity of a mechanized felling harvesting crew. The important features of this function are the effect of small piece size on production rate, the maximum production rate, and the machine capacity limitation. The left-hand section of the curve in Figure 2 that is highlighted represents values of piece diameter that result in the productivity of machines being restricted. The associated proportional decrease in productivity is available in the table alongside the graphical function on the right. The maximum productivity for this system was set to dbh of inches as shown on the graphical function. Production efficiency quickly reduces to zero beyond this point when the dbh becomes too large for machinery to handle. Figure 3 presents the graphical function for the effect of slope on mechanized felling harvesting systems. At a neutral slope of 0%, the system is considered to be operating at its designated system maximum. At intermediate slopes, the slope effect creates a decrease in productivity along a continuum. At the extremes of this graphical function, beyond slopes of 80%, the system is limited to such an extent that the productivity is reduced by one hundred percent. These graphical functions are specific to the mechanized felling system and each individual system has its own graphical function for each parameter. Users have the capacity to edit the graphical functions based on their own knowledge so that the functions may be more appropriate to the user s interests. For our purposes, initial functions to compare alternatives were based on studies from the literature. Delay Functions Figure 4 displays an example delay-length distribution for a system. The frequencies of delay lengths for harvesting systems are assumed to have a gamma distribution, which results in many short delays and a few long delays. The exact dimensions of this distribution are estimated based on intuition from our literature review and field experience. The x-coordinate on the scale is determined through a uniform random function (as a result, the probability of selecting any point along the x-axis is the same). When the system enters a delayed state, through breakdowns or system delays, the model determines the delay length from this function. Once the required delay time has been completed, the system returns to a working state. Complete HSA Model In our model, we let the final system productivity be represented by SysProd, which is obtained by averaging the system productivity over time, SysProd(t). SysProd(t) is a function of the maximum production rate (MaxProd) and the multiplicative (downward) adjustments of piece size 94 SJAF 28(2) 2004

5 Figure 3. Example of productivity efficiency as a function of slope at time t (Slope(t)). Figure 4. Example delay time distribution function (Delay(t)). SJAF 28(2)

6 Figure 5. Schematic diagram of the HSA model with the mechanical felling and grapple skidding system selected. (Dia(t)), stand density (Stock), harvesting intensity (HarvInt), slope (Slope(t)), and average skidding distance (Dist(t)). SysProd(t) is then multiplied by a (0, 1) variable denoting whether the system is down at time t, Delay(t). Note that Dia(t), Slope(t), Dist(t), which vary with each time interval, and Stock and HarvInt, are all less than or equal to 0. Also note that each realization of Dia(t), Slope(t), Dist(t), and Delay(t) are independent. The function for SysProd is shown below: SysProd t SysProt(t) t MaxProd * 1 Dia t))*(1 Stock) * (1 HarvInt) * 1 Slope t))*(1 Dist(t)) * Delay(t) (1) A schematic for the HSA Model is shown in Figure 5. Figure 6. Coefficient of variation at different simulation lengths for the average output per time unit from a manual system with site, stand, and system parameters set at default values. CV constructed from 10 runs at each simulation length. Simulation Length The HSA model uses hours as units of time and has 60 iterations per hour, or one iteration per minute. This level of resolution is necessary to accurately capture the material flows and delay states. The importance of choosing the correct simulation run length is illustrated in Figure 6. In Figure 6 we present the coefficient of variation (CV) for the output of 10 simulation runs at four simulation lengths. CV is a relative measure of data dispersal, where the CV of a set of data is the standard deviation divided by the mean of the data. The CV for the output of a simulation study has a direct impact on the confidence interval produced. In general, a low CV will produce a narrow confidence interval (i.e., an accurately representative time-average output) with a low number of runs, whereas a high CV will imply that many runs are needed to produce a narrow confidence interval. The benchmark CV decided on for the HSA model was 0.05 (a very low CV that leads to a very narrow confidence interval). One can see from Figure 6 that any simulation length greater than 200 SMH would be acceptable. Since the model is likely to be used by harvest planners on an ad hoc basis by making one run at a time to evaluate harvesting systems, we used one run length of 500 SMH in the simulation experiments reported in the next section. Model Validation The purpose of the HSA model is to assist harvest planners in assigning systems to a stand. The validation test compared the HSA ranking accuracy with the absolute measurements of productivity and efficiency in published productivity studies. This included different harvesting systems on the same site (Anderson 1997, Spinelli et al. 2002, 96 SJAF 28(2) 2004

7 Table 1. HSA input parameters for example 1. Max Productivity (t/pmh) Manual fell cable skid 40 t/pmh Mechanized fell grapple skid 150 t/pmh Shovel bunching grapple skid 14 t/pmh CTL harvester/forwarder 50 t/pmh Stock(t) (mean, SD) 500, 20 trees/ac HarvInt 1 (clear-cut) Slope(t) (mean, SD) 0, 5% Dist(t) (min, max) 0, 800 ft Delay(t) (probability of delay, max delay) 0.2, 300 min Kluender and Stokes 1994) and the same system on different sites (Lanford and Stokes 1996, Klepac and Rummer 2002, Hartsough et al. 1997, LeDoux and Huyler 2000, Visser and Stampfer 2003). The complete validation procedure and results are presented in McDonagh (2002), which illustrate that the model produces near-identical ranking of alternative harvesting methods as compared to the absolute measurements published in the literature. HSA Output Examples In our simulation experiments, the output measure (dependent variable) is the productivity of the operation. This output measure is reported in tons per productive machine hours (t/pmh). Example 1: Productivity Analyses (System Response to Tree Size) Types of analysis that can be performed using the HSA model include: (1) system comparison over site and stand parameter range; (2) determination of the best operating range for a system for site and stand parameters; and (3) determination of the cost of operating a system on an inefficient site. The HSA model (1) was initialized with the parameters provided in Table 1. Figure 7 illustrates the effect of piece size on the productivity of the four system types we chose to examine with the HSA Model. The magnitude is dependent on the production potential specified for each system in the simulation. The user can control the production potential for each system and the associated parameterized production functions to customize the simulation to a system of particular interest. Example 2: System Inefficiency Analyses: CTL Response to Silvicultural Treatment System The following is a scenario example using a cut-tolength system on four possible types of harvest that could occur in the southeast United States: (1) plantation clearcut for pine pulp and chip-n-saw products; (2) plantation fifth row thinning for pine pulp; (3) hardwood clearcut for hardwood pulp and sawlogs; and (4) hardwood 50% shelterwood for hardwood sawlogs. Figure 7. Piece-size productivity curves of the four harvesting systems in the model. Table 2. HSA input parameters for four different scenarios. Scenario Plantation clearcut Plantation 5 th row thinning Hardwood clearcut Hardwood 50% shelterwood Max CTL Prod. (t/pmh) HarvInt 1 (clear-cut) Dia(t) (mean, SD (in.)) 11, 1 7, 3 18, 8 18, 8 Stock(t) (mean, SD (trees/ac)) 400, 0 500, 0 450, , 150 Slope(t) (mean, SD (%)) 0, 0 0, 0 0, 15 0, 15 Dist(t) (min, max (ft)) 0, 500 0, 500 0, 800 0, 800 SJAF 28(2)

8 Figure 8. Productivity of cut-to-length system over a range of harvest types. The values of the input parameters that were varied to create the different scenarios are shown in Table 2. All other parameters remain constant (see also Table 1). In this example, the plantation harvests occur on flat terrain, while the hardwood cuts are on sites that have slopes up to 15%. The clearcuts have a larger piece size than partial cuts and the hardwood harvests have a larger average piece size than the plantations, but with much greater variability. Maximum skid distance is also greater for the hardwood harvests. Figure 8 illustrates the effects on productivity of a cut-to-length system of different harvest types. There is inefficiency inherent in each of the example harvests because of the associated nonoptimal parameter values. The HSA model presents information identifying inefficiencies in terms of production and also translates this inefficiency into a parameterized cost per ton. The utility of the HSA model is that it attributes the effect on efficiency to each site, system, and stand parameter. In this example, piece size, stocking, slope and harvest intensity change depending on the harvest type. Between harvest types, the Figure 9. Cost of inefficiency of site and stand parameters for a Cut-to-Length system. absolute effect on productivity changes, along with the proportionate contribution of each site and stand parameter to the total inefficiency. The cost of inefficiency for each parameter is graphed in Figure 9. Overall, piece size is the dominant factor causing inefficiency. The presence of other parameters in causing inefficiency can be noted, such as the contribution of harvest intensity in the plantation thinning and shelterwood cut. Conclusions and Future Research This article has presented an overview of a simulation approach to address a critical issue in the field of forest harvesting: selecting an appropriate harvesting system for a given stand. This is important because stand and terrain parameters can significantly affect the operational efficiency of the system. The simulation predicts the production potential of the system and highlights inefficiencies that might be mitigated through better management of the system assignment. Our HSA model can combine many harvesting systems under the same set of modeling assumptions to facilitate meaningful comparisons. In this article, we have described the conceptual model, defined the parameters of interest, and provided examples of various mechanisms to model the systems in a systems dynamic simulation environment. Complete details on example problem data and validation of the simulation model output can be found in McDonagh (2002). Although this research represents a contribution to the field of forest operations, there is further research outside the scope of this article that needs to be conducted. The inclusion of real-time information technology into harvesting operations is a developing research area in forest operations (Taylor et al. 2001, Holley 2001). It is anticipated that the level of detail in this model will suggest avenues through which real-time information technology can be incorporated into the harvesting system. Literature Cited AEDO-ORTIZ, D.M., E.D. OLSEN, AND L.D. KELLOGG Simulating a harvester-forwarder softwood thinning: a software evaluation. For. Prod. J. 47(5): ANDERSSON, B Harvesting coastal second-growth forests: loader-forwarding of hand- and machine-felled timber. Tech. Note 261 For. Eng. Res. Inst. of Canada. 11 p. BAUMGRAS, J.E., C.C. HASSLER, AND C.B. LEDOUX Estimating and validating harvesting system production through computer simulation. For. Prod. J. 43(11/12): BLOCK, W.A., AND J.L. FRIDLEY Simulation of forest harvesting using computer animation. Trans. Am. Soc. Ag. Eng. 33(3): BRAGG, W.C., W.D. OSTROFSKY, AND B.F. HOFFMAN, JR Residual tree damage estimates from partial cutting simulation. For. Prod. J. 44(7/8): DREMANN, A.P TREESIM: a new analysis tool for harvest system evaluation. P in Proc. of the 9 th Annual COFE Meeting. Mobile, AL. DAVIS, C.J., AND T.W. REISINGER Evaluating terrain for harvesting equipment selection. J. For. Eng. 2(1):9 16. ELIASSON, L Simulation of thinning with a single-grip harvester. For. Sci. 45(1): ELIASSON, L., AND H. LAGESON Simulation study of a single-grip harvester in thinning from below and thinning from above. Scandinavian J. For. Res. 14: SJAF 28(2) 2004

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