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Article

Contingency Plans for the Wood Supply Chain Based on Bottleneck and Queuing Time Analyses of a Discrete Event Simulation

Institute of Production and Logistics, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180 Vienna, Austria
*
Author to whom correspondence should be addressed.
Forests 2020, 11(4), 396; https://doi.org/10.3390/f11040396
Received: 13 March 2020 / Revised: 29 March 2020 / Accepted: 31 March 2020 / Published: 2 April 2020

Abstract

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Wood supply chain performance suffers from risks intensified by more frequent and extreme natural calamities such as windstorms, bark beetle infestations, and ice-break treetops. In order to limit further damage and wood value loss after natural calamities, high volumes of salvage wood have to be rapidly transported out of the forest. In these cases, robust decision support and coordinated management strategies based on advanced contingency planning are needed. Consequently, this study introduces a contingency planning toolbox consisting of a discrete event simulation model setup for analyses on an operational level, strategies to cope with challenging business cases, as well as transport templates to analyze outcomes of decisions before real, costly, and long-lasting changes are made. The toolbox enables wood supply managers to develop contingency plans to prepare for increasing risk events and more frequent natural disturbances due to climate change. Crucial key performance indicators including truck to wagon ratios, truck and wagon utilization, worktime coordination, truck queuing times, terminal transhipment volume, and required stockyard are presented for varying delivery time, transport tonnage, and train pick-up scenarios. The strategy BEST FIT was proven to provide robust solutions which saves truck and train resources, as well as keeps transhipment volume on a high level and stockyard and queuing time on a low level. Permission granted for increased truck transport tonnages was evaluated as a potential means to reduce truck trips, if working times and train pick-ups are coordinated. Furthermore, the practical applicability for contingency planning is demonstrated by highly relevant business cases such as limited wagon or truck availability, defined delivery quota, terminal selection, queuing time reduction, or scheduled stock accumulation. Further research should focus on the modeling and management of log quality deterioration and the resulting wood value loss caused by challenging transport and storage conditions.

1. Introduction

Wood is the only sustainable natural resource available in Austria [1]. Consequently, the forest-based industry is a crucial economic sector profiting from Austria’s abundant forests, well-developed infrastructure, highly skilled workers, and a rich research environment, which enables export rates of 87% in the paper industry [2] and 70% in the wood industry [3]. For every additional 100 m3 of wood harvested, a new green job is added to the 300,000 existing ones (i.e., 1/10 of Austria’s working population: 175,700 forestry, 40,000 joineries, 27,900 wood industry, 23,000 timber trade, 11,400 timber construction, 8100 paper industry, 6000 forest management) [4]. To ensure economic success and sustainability and to secure the existing jobs, the industry is dependent on a stable wood supply. The current challenges of the Austrian wood supply chain include decreasing numbers of both crane-truck drivers and train terminals, rapid market price fluctuations, as well as long lead and queuing times. These challenges are reinforced by supply chain risks that may be technical (e.g., machine and truck breakdowns), managerial (e.g., delivery stops at mills and reliability of rail wagon delivery), or inclement weather (e.g., high snow cover, heavy rain, and low temperature).
Climate change increases the frequency and impact of extreme natural calamities which results in high volumes of salvage wood (more than 50% of the harvested wood in Austria in 2018 [5]) and an intensification of risk in the wood supply chain. The Austrian government in its Forest Strategy 2020+, recognized the risks to productivity and the economic deployment of Austria’s forests and set the strategic goal of building and developing resilient risk management instruments and contingency plans [1]. Natural calamities such as windstorms, bark beetle infestations, and ice break treetops produce high volumes of salvage wood, which have to be quickly transported out of the forest to limit further damage or wood value loss. Train terminals have proven to be effective in securing a stable wood supply to the industry as they provide the high transport capacity of railroads and connected storage areas. In Austria there are 153 active train terminals (i.e., 60 wood industry terminals, 65 wood shipping terminals, seven private terminals, 12 temporary terminals, nine terminals with special status), and a considerable number of inactive but recoverable terminals, where wood can be transhipped from truck to train [6]. The management of such a multimodal wood supply chain is more challenging than that of a unimodal supply by utilizing trucks only. However, it reduces the effects of climate changes (e.g., CO2 emissions), supply chain risks (e.g., buffer capacity to supply industry when harvesting is not possible), and supply chain challenges (e.g., reducing the bottleneck of crane truck capacity by limiting their operation to unavoidable short distance wood transport by trucks to terminals).
To provide decision support for the management of a multimodal supply chain, many companies in the forest-based industry have been trying to digitalize. Concepts such as Industry 4.0 and the internet of things (IoT) have inspired companies to collect large amounts of data, but in most cases they are not analyzed, shared, or used for the decision making process required to mitigate risks. For this reason, industry representatives are considering the development of digital twins of their supply chains. The term digital twin is used as an umbrella term and can be further divided in a wide range of maturity levels. Based on an earlier framework [7] steps for a virtual factory were defined [8], which are also generally appropriate for virtual supply chain models. They define a digital model as a virtual representation that reaches a connected model state (also designated as digital twin), if it is supplied with real-time data. Others define a digital twin as a “virtual representation of a real-world system and its status”, distinguishing it from simple simulation models by “the ability to determine the state of a specific object”, which is “achieved by combining current data from the subject with its simulation model” [9]. Based on these definitions, Austria’s forest-based industry is a long way from creating a real digital twin or virtual supply chain. However, the first step in this direction can be made by creating digital models, which reduce uncertainty at a reasonable cost. This leads companies away from educated guesses and gut decision making based on rule-of-thumb estimates to decision making based on data already collected but not properly analyzed.
In the literature, digital models for multimodal wood supply chains including terminals have been delivered in the form of discrete event simulation (DES) models. DES fits perfectly for modeling the wood supply chain in a dynamic (i.e., variables change over time), discrete (i.e., system changes occur at specific events), and stochastic (i.e., random observations) way [10]. The wood supply chain covers growing, harvesting, extraction, transporting, storing, (pre-)processing, (re)using, and recycling of wood. Wood supply chain management deals with relevant decisions to plan, design, operate, control, and monitor material-, service-, financing-, and information flows within and between various actors [10]. Appropriately, the wood supply chain can be represented by standard DES elements such as entities or resources (e.g., wood, trucks, trains), delays (e.g., processes, tasks, service times), queues (e.g., waiting lines to enter terminal or industry stockyards), or system capacities (e.g., transport or stockyard capacity). Furthermore, DES is appropriate for advanced contingency planning because complex interdependencies can be modeled and visually illustrated in animations to demonstrate model internals to stakeholders. DES models for wood transport were reviewed and the suitability of multimodal DES models for building efficient, resilient, green and socially sustainable wood supply chains was confirmed [10]. Existing multimodal DES models including train terminals [11,12,13,14,15] cover timber, forest chip, or biomass transport at an operational level. They use different supply chain network configurations for regional case studies in Austria or Finland. Other multimodal DES models also consider vessel terminals [16,17,18,19]. These multimodal DES studies are important contributions to obtain a better understanding of the complex interdependencies of multimodal wood supply chains, yet none have focused on risk mitigation and generalizable contingency planning.
Especially after extreme natural calamities, when decisions have to be made quickly, there are neither coordinated plans nor elaborated management strategies available. As a result, supply chain performance suffers and will suffer even more due to risks intensified by more frequent and extreme natural calamities driven by climate change. Consequently, a research gap exists to derive concrete contingency plans for wood transport. To help close the current research gap, this study delivers elaborated contingency planning for train terminals based on DES. In particular, this study sets up a DES model to deliver crucial key performance indicators (KPIs) and develops transportation templates for different delivery time, tonnage, and train pick-up scenarios as a basis for contingency planning. Furthermore, contingency planning is illustrated by practical and highly relevant business cases. Consequently, it answers the research questions “Which parameters are critical for multimodal contingency planning?”, “How many trucks and wagons are needed for short-, medium-, and long delivery times, respectively, with one or two train pick-ups to perform best”, and “How many truck trips can be avoided, if the maximal transport tonnage increases and how would this effect the terminal performance”?

2. Method and Model

Simulation models facilitate understanding of complex systems and their behavior in a variety of scenarios. They provide superior benefits for managerial contingency planning in nonstationary systems under uncertainty in contrast to mental, conceptual, physical, or mathematical models. In simulation modeling, methods such as DES, agent based simulation (ABS), and system dynamics (SD) are general frameworks for mapping a real-world system [20]. DES focuses on manmade systems, where large and complex operations can be broken into a sequence of straightforward tasks or processes, which are often illustrated in flowcharts [21]. Moreover, different model configurations and what-if analyses show the effects of decisions before real, costly, dangerous, inefficient, or long-lasting changes are made and therefore provide valuable decision support for today’s challenges.
The applied DES model is an extension of Kogler and Rauch [15] including a new generic model structure enabling generalizable results for various train terminals. The model was sufficiently validated including expert involvement, appraisals, real life case study data, input (e.g., restrictions, decision variables, case study settings), and output checks (e.g., transportation plans, volumes). Moreover, the identification of critical parameters resulted in the design of new scenario settings taking into consideration different delivery times, transport tonnages, and number of train pick-ups. Additionally, refined parameterizations, as well as an enhanced system logic now enable advanced contingency planning. The parameterizations of Kogler and Rauch [15] included only one broad triangular distribution for delivery times, which, for this study, was split into narrow triangular distributions for short, medium, and long delivery times to provide more appropriate configurations for different train terminals with various delivery times. This approach was also used for the parameterization of low, moderate, and heavy transport tonnages to evaluate permissions granted for higher truck transport tonnages. The implementation of a second train pick-up per day expanded the system logic, but required coordinating truck working times with train pick-ups to ensure a solution quality of both a truck utilization rate over 95% and no empty wagons at the time of train pick-up. Comprehensive sensitivity analyses for the decision variables (i.e., number of trucks and number of wagons) provide advanced multimodal transport plans which outperform the simple expert-based heuristic of Kogler and Rauch [15]. Furthermore, this study defines and calculates new KPIs, which are especially relevant for contingency planning.
The model maps the flow of wood entities through the supply chain by facilitating processes for wood harvest, storing at forest landings, truck transport to terminal, storing at terminal stockyard, transhipment to wagon, and train transport to industry (Figure 1).
Trains and trucks move the wood during their working hours through the supply chain. Trucks fulfill the tasks of picking up wood at the forest landing and transporting it either directly to wagons or via terminal stockyards. The processes at the terminal are modeled in detail and close to reality, which enables the tracking of truck queuing times. Thus, the following activities are covered: Queuing in front of the terminal, removing safety belts, loading wagon, securing wagon load, unloading at stockyard, cleaning truck platform, and completing delivery documentation. Consequently, a complex logic controls the transhipment process from trucks to stockyard or wagons, as well as the potential truck queuing at the terminal. Trains pick up fully loaded wagons, transport them to industry, leave empty wagons for loading, and sort wagons according to their loading status at the terminal. For a detailed description of the DES model refer to Kogler and Rauch [15].
Sensitivity analyses of preliminary simulation runs indicated that results are sensitive to changes in delivery time from forest to terminal, number of train pick-ups at the terminal, and the transport tonnages. Consequently, these parameters were critical for multimodal contingency planning. Based on input data analysis (e.g., process times) of Austrian case studies and consultation with experts (i.e., foresters as well as wood, transport, and logistic managers), realistic parameter settings were specified, which lead to the formulation of scenarios to cover small-scale train terminals with similar layouts: One loading siding, no overtaking at the roughly elliptical inbound truck driving route, loading track length of maximum seven wagons, and two truckloads filling one wagon (Figure 2). This represents the majority of Austria’s train terminals for wood transhipment.
Thus, simulating 18 scenario combinations (Table 1) covers a broad range of potential logistic cases and facilitates the generation of generalizable results as a basis for the development of robust transport strategies for contingency planning.
The truck delivery time covers categories representing regions with short-, medium-, and long delivery times between forest landings and terminal. Triangular distributions were used to take into account different street and traffic conditions and possible process delays (Table 2).
Scenarios for low, moderate, and high truck loads were designed to consider actual weight limits (e.g., 44 t in Austria) for forest trucks equipped with a crane, as well as exemption clauses granted by the authorities after massive windthrows with bark beetle burdens or potential future liberalization. For heavier tonnages one additional minute of loading/unloading time per additional ton was assumed. A truck driver cannot exactly estimate the weight of the loaded wood due to natural variations in bulk density and moisture content, as well as a lack of crane scales in the majority of Austrian forest trucks. Consequently, the variation was implemented applying triangular distribution of tonnages and dependent process times (Table 3). General truck tasks at the terminal such as removing belts (i.e., MIN = 7 min/MODE = 10 min/MAX = 12 min), securing wagon loads (i.e., 5/8/10), cleaning loading platform (i.e., 3/5/10), as well as completion of delivery documentation (i.e., 10/13/15) are the same in all scenarios [15].
Once or twice a day, a locomotive picks up full loaded wagons and provides the number of ordered empty wagons. Train pick-up times are fixed by the train carrier at 9 am and 3 pm (i.e., for two pick-ups). The start of truck shifts was coordinated with delivery times and train pick-ups resulting in a high ratio of fully loaded wagons at the time of a train pick-up. This ensures high truck utilization, as well as high terminal handling volume. Trucks start their shift at 7 am (adjusted to 5 am for medium and long delivery time scenarios with two train pick-ups) giving them enough time to fill the wagons before the first pick-up at 9 am. This approach of working time and train pick-up coordination was validated for its practical usability by terminal managers of the Austrian Federal Forests (i.e., largest forest owner in Austria) and Rail Cargo Austria (i.e., main cargo operator on Austrian railways), who confirmed similar strategies, if high terminal handling volume was needed after natural calamities. In accordance with European law, truck shifts were set to 8 h a day for five days a week.
Extensive test runs were performed to understand the interdependencies of the system and to select and track the most important KPIs for contingency planning. The resolution time was set as minutes and the simulation period as one week in order to both match manager’s requirements and follow common scientific practice [10]. To ensure the predefined solution quality necessary for practical usability, all results that satisfy a truck utilization of over 95%, allow no empty wagons at the time of train pick-up and allow fewer than 20 half loaded wagons per week for one train pick-up (i.e., respectively 40 half loaded wagons for two train pick-ups). The simulations were replicated 52 times for every scenario to cover a full year of observation time. This resulted in 936 single simulation runs consisting of 52 weeks for a total of 18 scenarios.

2.1. KPIs and Transport Strategies

Four KPIs were identified as necessary to provide decision support for contingency planning. The KPI “terminal transhipment volume” defines the maximal amount of wood in solid cubic meters, which can be transhipped at the terminal from truck to wagon per week for a given truck and train wagon configuration. The KPI “required terminal stockyard” shows the amount of wood in solid cubic meters which is stored to guarantee a high truck and wagon utilization, as well as smooth wood flow from forest to the industry. The KPI “average queuing time” reports on the average truck waiting time in minutes at the terminal, which consists of the waiting times to enter the terminal, remove the safety belts, load the wagon or unload at the stockyard, and clean the loading platform. The KPI “maximal queuing time” reveals the longest waiting time in minutes for trucks to pass through the processes at the terminal.
Contingency planning requires the consideration of those KPIs, as well as reflection on the different, often competing objectives. In order to provide decision support for different planning objectives, various sets of KPI rankings were developed with stakeholder participation and analyzed for low tonnages and short, medium, and long delivery times. After extreme natural calamities the contingency planners are challenged to transport the wood out of the forest as fast as possible to avoid wood value loss. Consequently, the first strategy MAX VOLUME solely focuses on the maximal terminal transhipment volume. In cases where beneficial solutions had the same maximal terminal transhipment volume, the solution with the lowest number of wagons and trucks (i.e., decision variables) was selected to save resources. In some cases, contingency planners have to deal with terminals which do not provide space for a stockyard. Thus, the second strategy NO STOCKYARD was developed, which selects a solution where no stockyard is needed (i.e., if there are no solutions with no stockyard availability, the one with the lowest stockyard was chosen). From those solutions with the lowest stockyard, the one with the highest transhipment volume was chosen. The resulting solutions performed well according to their main KPIs, but also showed limitations regarding others. Thus, the MAX VOLUME strategy requires high transport resources. This also holds true in some cases for the NO STOCKYARD strategy, which provided comparatively low transhipment volume. Consequently, a third strategy BEST FIT was developed. In order to save both truck and train resources and to simultaneously keep transhipment volume on a high level, solutions with an up to 10% lower maximal transhipment volume were considered. Among all feasible solutions the one with the lowest number of wagons and trucks was selected, and if these were equal the solution with the lowest required stockyard was chosen.

2.2. Business Cases for Evaluating Managerial Impact

In order to evaluate the practicability of simulation results provided as tables as a basis for operational transport planning, three different business cases are formulated: (1) Restricted wagon availability, (2) restricted truck availability, and (3) defined delivery quotas. The first business case discusses the handling of restricted wagon availability. The terminal size limits the number of wagons for simultaneous transhipment and rail carriers define the maximal number of train pick-ups a day. However, after natural calamities or capacity planning errors (e.g., misjudgment of demand), as well as during harvesting periods of other train shipped goods such as beets, the number of available wagons can further decrease and fluctuate on a weekly basis. The transport templates should be used to find the appropriate number of trucks for a given number of train pick-ups, wagons, delivery time, and transport tonnage to guarantee an efficient (i.e., high volume and utilization, low resources and queuing times) wood transport.
The second business case provides a guideline for planning under restricted truck availability. In mountainous regions which have steep and widely ramified forest roads and lack GPS reception, planning should focus on a high utilization of the limited number of local forest truck drivers (= bottleneck), which are able to navigate through the forest road network. Here, the transport templates can be used to find an efficient number of wagons for a given number of train pick-ups, trucks, delivery time, and transport tonnage.
The third business case covers the common issue of defined delivery quotas. Wood based industry factories such as sawmills or pulp and paper mills, depend on a stable wood supply to guarantee smooth-running production. Furthermore, harvesting teams are dependent on constantly available transport to maintain enough space for harvested wood and its extraction (e.g., especially for cable logging to narrow mountain roads). Consequently, fixed delivery quotas are arranged to enable a smooth flow of wood. The transport templates permit the selection of an appropriate terminal, as well as transport configurations and provide KPIs in order to compare the effects of potential exemption clauses for higher transport tonnages after natural calamities.

3. Results

The managerial practice for operational wood transport planning follows a rolling weekly planning horizon. Thus, all results were aggregated to a weekly level and rounded to the nearest ten to provide a clear overview for short-term contingency planning. This approach allows contingency planners to react dynamically to changing conditions and restrictions after natural calamities or other disturbances. The numbers of available trucks and wagons are the main decision variables for contingency planners and thus, define the structure of the resulting templates (Appendix A and Appendix B; Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11; Figure 3 and Figure 4).
For the one train pick-up scenario the BEST FIT strategy provided the lowest number of trucks per wagon, closely followed by the NO STOCKYARD strategy, which performed worse for long delivery times whenever it goes beyond four wagons (Figure 3). Moreover, the BEST FIT strategy reduced the number of trucks compared to the MAX VOLUME strategy and transhipped similar amounts of wood (Table 7). Additionally, both the BEST FIT strategy and the NO STOCKYARD strategy reduced the required stockyard compared to the MAX VOLUME strategy. The BEST FIT strategy also outperformed MAX VOLUME, as well as the NO STOCKYARD strategy with regard to queuing times.
Two train pick-ups show a more diverse picture, because the lowest number of trucks per wagon switches between the BEST FIT strategy with 12 times lowest value and the NO STOCKYARD strategy with 15 times lowest value (Figure 4). If the MAX VOLUME strategy is used as a benchmark, on the one hand, the number of trucks is lower for the BEST FIT strategy and the NO STOCKYARD strategy (Table 11). On the other hand, the transhipment volume is slightly lower for the BEST FIT strategy, but drops sharply for short delivery times for the NO STOCKYARD strategy. Regarding the required terminal stockyard the NO STOCKYARD strategy outperforms BEST FIT strategy. For queuing times, the BEST FIT strategy outperforms for long delivery time, and the NO STOCKYARD strategy for short ones.
A framework for beneficial wagon to truck ratios is provided in Figure 3 for one train pick-up and Figure 4 for two train pick-ups. High quality solutions can be compared and selected according to the main contingency planning objective and strategy. Thereby, the framework is complemented by the transport configuration tables (Table 4, Table 5, Table 6, Table 8, Table 9 and Table 10), as well as transport templates (Appendix A and Appendix B) where KPIs can be compared in detail. For instance, it can be observed that the MAX VOLUME strategy builds up higher stockyards and thus, also more trucks and wagons are needed. Simultaneously those figures and tables are also useful, if contingency planners have other customizable decision variables such as delivery time or to deal with transport capacity limitation such as fewer truck or wagon availability. For example, Figure 4 shows, that if there are only 10 trucks available to supply a terminal with seven wagons and two train pick-ups, only supplying forests with short delivery time to the terminal would enable full utilization of the terminal capacity. Moreover, decision support can be provided regarding terminal selection, if different terminals are available.
In business cases where higher transport tonnages are possible due to legislative changes or exemption clauses invoked by the authorities, the relevant KPIs can be looked up in the complete transport templates (Appendix A and Appendix B). Furthermore, the Appendix shows the potential for truck trip reduction. On average, the number of truck trips can be reduced by 6% for one train pick-up (short delivery time 9%/medium 8%/long 2%) and 10% for two train pick-ups (8%/11%/9%), when tonnages change from low to moderate. If tonnages change from low to high, the number of truck trips can be reduced by 10% for one train pick-up (14%/14%/7%) and 17% for two train pick-ups (14%/19%/16%). The distribution of the number of reduced truck trips per week is shown for one train pick-up in Figure 5 and two train pick-ups in Figure 6. In addition to tonnages and delivery times, the number of trucks in the system (i.e., higher for two train pick-ups), the number of wagons (i.e., from one up to seven), the average queuing times for one train pick-up (minutes: 20/12/74) and two train pick-ups (25/14/35) influence the number of reduced truck trips per week.

3.1. Contingency Planning Under Restricted Wagon Availability

The practical applicability of the simulation model for short-term transport and especially contingency planning is demonstrated by selected realistic business cases. For the first business case, contingency planning under restricted wagon availability, the transport templates can be used to find the appropriate number of trucks for a given number of train pick-ups (e.g., two), wagons (e.g., five), delivery time (e.g., medium), and transport tonnage (e.g., low). If there is no stockyard available, the corresponding transport template (Appendix Table A5) shows for 10 trucks a maximal weekly transhipment volume of 2350 m3 and an average queuing time of 20 min, as well as a maximal queuing time of 120 min. If there are only five trucks available, a switch to only one train pick-up a day (Appendix Table A2) with a maximal transhipment volume of 1170 m3 is the better option. For terminals with stockyards a controlled inventory accumulation at the train terminal (e.g., to prevent bark beetle infestation in the forest) can be achieved with one additional (11 trucks 250 m3) or two additional (12 trucks 490 m3) trucks per week. If truck carriers would not accept an average queuing time of 20 min (i.e., truck carrier paid per transhipped m3 tries to use negotiation power due to limited transport options after natural calamities), the number of trucks could be reduced from 10 to 8 to lower the average queuing time to 10 min (resulting in a transhipment volume of 1890 m3).

3.2. Contingency Planning Under Restricted Truck Availability

The second business case considers contingency planning under restricted truck availability, where transport templates can be used to find an efficient number of wagons for a given number of train pick-ups (e.g., two), trucks (e.g., five), delivery time (e.g., short), and transport tonnage (e.g., low). Without a stockyard available, five wagons can provide a transhipment volume of 1710 m3, an average of 10 min, and maximal queuing time of 90 min (Appendix Table A4). If more wagons (e.g., seven) are available, one train pick-up (Appendix Table A1) may be an alternative (providing 1630 m3, 20 min average, and 130 min maximal queuing time). In order to guarantee supply security from terminal to industry (e.g., restrictions in forest road usability due to snow, rain, or maintenance) buffer inventory at terminals with stockyards can be a strategic advantage. To build up inventory at a terminal supplied by five trucks, the number of wagons can be reduced to one, allowing a weekly stockyard accumulation of 1670 m3 (Appendix Table A1) for one train pick-up and 1430 m3 (Appendix Table A4) for two train pick-ups, respectively. If the queuing time for five trucks and wagons at the terminal needs to be reduced (e.g., because of negotiations or complaints), one train pick-up would lower the average queuing time to 10 min and the maximal queuing time to 80 min (transhipment volume 1130 m3, required stockyard 600 m3).

3.3. Contingency Planning Under Defined Delivery Quotas

The common issue of defined delivery quotas is showcased by the third business case. If a transport quota of 3300 m3 per week is designated, it can be achieved by a terminal with two train pick-ups per working day providing seven wagons each. For short delivery time 12 trucks (Appendix Table A4), for medium14 trucks (Appendix Table A5), and for long 21 trucks are needed (Appendix Table A6). If it is possible to increase the transport tonnage from low to moderate, the quota could be fulfilled with 11 trucks for short, 13 trucks for medium, and 19 trucks for long distances. In case of an increase from low to high, for short delivery time 8 trucks, for medium 12 trucks, and for long 17 trucks would be sufficient. In order to classify the truck savings through multimodal transport, one scenario setting for a similar unimodal supply chain was calculated (i.e., drive time forest MIN = 35 min/MODE = 40 min/MAX = 45 min, drive time industry 145/150/155, unloading and queuing time industry 85/90/95; resulting in one trip per truck per day to achieve an equivalent truck utilization for comparable results). To achieve a unimodal transport quota of 3300 m3 per week for low, moderate, and high tonnages the number of required trucks would be 28, 25, and 22 trucks, respectively.

4. Discussion

Comprehensive transport templates structured by main decision variables were proven to provide contingency planners with decision support for various conditions and objectives. Recommended transport configurations can be further refined by negotiations, legal adjustments, or process optimization that were not evaluated in the simulation model. Refinements by negotions may include modifying industry delivery quota to enable a higher utilization, providing additional transport capacity to fulfill required delivery quota, switching supply to an alternative forest region or adding additional train pick-ups. Legal adjustment could include the targeted use of over-time working to fill all wagons or exemption clauses regarding both worktime or tonnages to prevent bark beetle infestations. Further process optimization could be achieved by shorter process times, business process reengineering, learning curve or staggered shifts.
The results were obtained for rail terminal configurations that are typical for Austria’s mountainous regions. Due to limited space there is usually only a single, short loading track for transhipping wood to few wagons. Therefore, developed measures and strategies cannot be generalized for conditions where rail terminal have more than one loading track and provide space for a whole block train as is common in other countries. Another important restriction is the one-way truck driving route within the rail terminal, which provides no possibility for passing, since this causes trucks to queue up. In order to support a detailed planning for similar rail terminal configurations, main input parameters of the simulation model such as legal payload for trucks and wagons need to be adapted. If these restrictions apply, the general findings can be transferred to provide support for basic contingency planning in other regions of the world.
For the purpose of discussing the findings in a broader scientific context, it is vital to mention that there are also DES studies, which concentrate on specific parts of the wood supply chain such as harvesting [22] and log yard logistics at industry sites [23,24]. These studies consider in greater detail modules for harvesting and industry site management. However, the simulation model of this study concentrates on the logistics of the wood supply chain and thus connects the initial harvesting and final industry consumption of those studies [22,23,24]. Furthermore, impacts of climate change and risks were simulated on a higher abstraction level with other methods for upstream processes such as primal tree planting, forest stand growths, and forest management, but the studies did not focus on supply chain management and wood logistic [25,26,27,28]. Others simulated wood supply chains and pointed out the resulting outcomes of risks such as raw material availability and quality [19], quality loss during storage [29], and oversupply [13,14], but did not focus on concrete contingency strategies and plans to give operative decision support to manage those risks. In the past, many studies observed biomass supply chains and concentrated on logistics for in-wood operations [30,31] and there are also contributions for moisture content reduction during in-wood storage for wood biomass feedstock [32], but they did not focus on discrete event simulation nor on multimodal timber transport.
In order to enable short-term contingency planning for multimodal wood supply chains, the terminal and queuing processes need to be modeled in detail. In a recent review [33], the simulation model of Kogler and Rauch 2019 [15] was described as “perhaps the most detailed railroad terminal study to date for the wood supply chain” (i.e., presumable Acuna et al. [33] accidentally interchanged the references of [10] and [15] in their paper). For this study, that model was further developed to cover identified sensible factors, as well as various scenario designs, KPIs, and strategies to provide robust results for a variety of small scale train terminals with different delivery times, train pick-ups, and tonnages. This was supported by comprehensive business process mapping and reengineering, which was also heavily used for other detailed DES studies in the wood supply chain [34,35].
The results indicate in line with Korpinen et al. [36] that higher truck transport tonnages provide potential to reduce truck trips and thus, transport costs and emissions. However, for political discourse further factors such as potential shifts from rail to road, traffic intensity, social compatibility, technical reliability, and unified competition regulations in the European Union have to be taken into consideration. In accordance with Eliasson et al. [37] emphasis was put on observing the impacts of transport distance, number of trucks in the system, and stockyards. Contrary to Eliasson et al. [37] staggered truck shifts were not implemented in this study, rather, truck shifts were coordinated with train schedules to guarantee high utilization. A potential for improvement could be the modeling of wood value loss during long lead times and the implementation of different delivery strategies [29]. Next to advantages such as buffer capacity and saved emission, terminals also show disadvantages such as higher costs, which were accordingly discussed for the wood assortment chips [38]. Managerial options such as staggered shifts, or targeted use of over-time working were not considered in this study but provide promising opportunities for further research. Another future approach is to focus on the modeling and management of log quality deterioration and the resulting wood value loss, caused by challenging transport (e.g., long lead times), as well as storage (e.g., weather, temperature) conditions.

5. Conclusions

The management of wood supply chains is a complex task facing many challenges such as decreasing forest truck transport capacity, lack of digitalization, and increasing risks of natural calamities due to climate change. The transhipment of wood from trucks to trains at terminals offers important strategy options and operational advantages including additional transport capacity, shorter truck queuing times at industrial sites, and reduced CO2 emissions. Moreover, fewer bottlenecks caused by the limited availability of forest trucks equipped with cranes occur, since trucks are deployed on indispensible short-distance wood transport from forest landings to terminals rather than long trips to industry.
Simulation provides powerful methods to cover dynamic and interdependent changes and analyze bottlenecks and queuing times to support advanced short-term contingency planning. Consequently, this study introduced a toolbox consisting of a discrete event simulation model set up for analyses on an operational level, strategies to cope with challenging business cases, as well as transport templates and tables including critical parameters, decision variables, and KPIs to facilitate contingency planning.
Identified critical factors such as the number of wagons and trucks in the system, terminal transhipment volume, required terminal stockyard, average and maximal queuing times at the terminal, truck and train utilization, as well as worktime coordination provide useful decision support for a variety of objectives. The multiobjective transport planning strategy BEST FIT provides robust solutions which save truck and train resources, as well as keep the transhipment volume on a high and the stockyard and queuing time on a low level.
Furthermore, different planning conditions such as the number of train pick-ups, the delivery time from forest to industry (i.e., resulting in different number of truck trips per day), as well as the truck transport tonnage (i.e., varies between regions or due to exemption clauses) influence contingency plans. Thus, the transport templates presented provide a sound overview of beneficial (i.e., high truck and wagon utilization) solutions to compare alternatives and support developing customized plans. The results supported contingency planning in common business cases such as restricted wagon or truck availability, defined delivery quota, terminal selection, inventory accumulation, and queuing time reduction.
The simulation model provided a variety of supply chain configurations outcomes of decisions before real, costly, and wide-ranging changes have to be made. Consequently, simulation results provided a well performing configuration which can be fine-tuned in real life business and contingency cases. For example, the permission granted for higher truck transport tonnages (e.g., after natural calamities) was evaluated as a potential means to reduce truck trips.

Author Contributions

Both co-authors participated in conceptualization, validation, investigation, resources, data curation, funding acquisition, review and editing; C.K., methodology, formal analysis, software, writing—original draft preparation, and visualization; P.R., supervision and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

The authors gratefully acknowledge that this research was funded within the collective research project THEKLA by the Austrian Research Promotion Agency (FFG) and the forest, wood, and paper industry consortium (FHP). The Austrian Marshall Plan foundation and the Austrian Forschungsgemeinschaft (ÖFG) supported the authoring of the article by sponsoring a research stay at the UC-Berkeley. Open access funding provided by BOKU Vienna Open Access Publishing Fund.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Terminal transport template for one train pick-up, short delivery time, and all tonnages (P1D1 T1/2/3).
Table A1. Terminal transport template for one train pick-up, short delivery time, and all tonnages (P1D1 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required TERMINAL Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
1123027029012013014010006006000047
12230260290590660660101020110120120000811
13240260280950106011102020201201301300001117
142402602801310147015602020301301401300001524
152402602901670187020103030301401401400001833
224505305902402702501000806060000913
23490530570710760750101010110120110000810
244705305901190131012901010201101201300001518
254705305901550171017402020201201301300001826
32710790880000101020707070000714
337307908803604003601000706060000813
3470077088083085089010101011012070000820
357007608801300139013801010201101201200001322
367307908801780194019001020201101201300001823
377107909102140234023602020201201301300002335
387007908802480274027602020301301401400002938
397108208802780307032103030301301401300003350
3107008208803210350036202030301301401400003449
439401060117000010202080110900001019
4494010601170480530440100108060900001416
4594010201180940940100010101011011070000725
4694010201180141014301490101010120120100000827
47940106011801900200020001020201201201200001828
48940106011702300248024902020201301301300002535
49980106011302610283029202020201301401300002538
410940102011802870316032703030301501401400003153
411940109011703350363037302020301301501500003651
541220132014700002020209090110000821
55113013301470600660540100108060700002223
5611701370147010601070109010101012070700001828
57117013201470154015101630101010110110800001033
581180132014201880202020702020201201201200002336
591170132014702320248025502020201201301300002644
5101170132014702670292029603030301301401400003349
5111170132015102920321033003030301401301500003760
5121120132014203340368038503030301301501400004568
641350162017400002020201201401200002333
6514101580170020002020301601301300001323
66141015801770720770620100107060700001822
6714101580183011701200119010101012070700001737
681410158017601560159016902010101301001100001740
69141015801820194019902090202020140140900001847
6101350159016902310245024503020301301401400003240
6111410158017602650289028203030301401501400003443
6121470159017503000330033203030401401401500003550
6131410152017003340373039903030301401301400004278
6141350158017603580389043204030301901401400004596
751630185021200002020301301601500001841
7616401850205050002040301201401300001330
771710185019908308906801001080601100001711
781640191020601230130012701010101301001100002838
791640191020501680164017402010101301201300001939
7101570185020502060205022002020201401301300002352
7111570185020602430247025602020201501401400002752
7121640185020602760291029003030301401401400003047
7131650185019903080334034003030301401401500003855
7141640185020603400375041603030201501401300004798
71516401850213035103890432040403020014014000049108
71616501850205036404020446050504021015014000048102
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.
Table A2. Terminal transport template for one train pick-up, medium delivery time, and all tonnages (P1D2 T1/2/3).
Table A2. Terminal transport template for one train pick-up, medium delivery time, and all tonnages (P1D2 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required Terminal Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half Loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
1123026029000000070707000035
1224026029028027030010101070707000016
132402603005405406001010108013080000210
1423026029080082089010101080150130000413
1525026028010701100119010101012080130000313
212402603000000007007000025
2247053057000000070707000058
2347053059027027024010101070707000058
24470530590530540600101010707070000616
254505305907708108901010107080800001022
3125026030000000070707000014
3248053059000010101070707000049
33680790880000000707080000917
34710790910240230140100070707000068
35710790880530530550101010707080000716
367007908807708108901010108080800001125
377107908801020108011901010108080900001228
38700790880127013501490101010801501500001433
397007908801540163017901010109080900001536
310680820880183019002090102020901401500001838
4250053059000000070707010101038
43750760910000101010808080000113
44940106012100000007070800001023
459401020118022021020100080707000063
4694010601170510510440100107080800001013
47940106011707808008501010108090800001225
489401060117010201070118010101080801500001433
49940106011701280135014701010101401501500001635
41094010601140152016301770102010110901600001938
411940106011701770189020702020201401401600002044
537408008800001000807070101010512
5495010601170000101010808080000918
551170127014700001010101008090000825
561180132014701901300000708010000078
571170127014705004903101001070808000089
581220127014607707807301010108010090000517
5911701320147010201070109010101080140900001731
5101120132014801270134014101010101001001000002342
5111180132014201510162016602020201101401000002133
5121170132015201760189019702020201001401600002347
64950106011700001000808080101010918
651180133014200002020209090900001320
661350165017600002020209090900002534
67141016401770160600001070801000001117
68141015301760500430150101010100909000040
6914101580182077076053010101080100900001314
610135015901750102010509001010101201001000002323
6111350158017701250132012502010201101101100002535
6121470152017601500160015402020201101701800001328
6131410159017601750187018102020201601601800002534
6141410159017601980213020803020301601701700002838
751190132014700001010109080801010101123
761420158017600002020109090900001328
77164018502060000303030110110900001835
7816501850204016020010101080120100000519
791640185020504803802010101010010010000090
710164019101980770710430101010100100150000180
711165018502060102010107601010101101001700001613
7121570185021201260130011002010201101201000002733
7131640178020601490157014302020201701701700001830
7141570185019801740183016902020301601701900003130
7151640185020501970208019603030301701801800002733
7161640185021202200235022203030401801701900003042
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.
Table A3. Terminal transport template for one train pick-up, long delivery time, and all tonnages (P1D3 T1/2/3).
Table A3. Terminal transport template for one train pick-up, long delivery time, and all tonnages (P1D3 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required Terminal Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half Loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
1223026030080301050607011011011000000
1323026029019016015070808012013013000002
1424026029032031030070809012013013000013
1523026029044043045080909012013013000026
2344043045000040506011011011000001
2447053059013050060707011012012000000
2547053059026017016060808012013013000002
3566070074000050607011012012000037
36730790880170501060707012012012000000
3770076088029018015070808013013013000003
3870079085040031030070809013013014000004
3973079088051044045080909013014014000008
310710790880630580600809090140140150000312
457307407400004050601101101100101011
4685086089000050606011012012000013
47930960104000060707012013013000039
4894010601170180601060708012013013000005
499801060117029019015070808014015015000004
4109801020121042032030070809014015015000009
4119401060113054044045080909015015015000028
5691083091000040505011011011010101000
5798010009900005060601201201200101021
5811001140118000050707012013013000037
59117012301320000607080130140140000513
5101170127014701804010708080140140140000011
511117013701470330180150708090140150150000410
512118013101470430320300809090150150160000213
691210123013200006070701401401400101029
610132013601470000607080140140150000313
611140014901620000708080150140140000818
612141015801760180400708090140140140000314
613141015801760330170150809090150150150000114
61414101640177042032030080901001902002000001120
710134013701470000607070140140140101010311
71114901450162000060708014014015001010011
712156016201760000708080140140140000517
7131640176019100007080801401501500001023
7141640185020502205010808090150150150000317
7151640185019903001801508090100210210220000817
71616401850206043032030090100100220220230000824
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.

Appendix B

Table A4. Terminal transport template for two train pick-ups, short delivery time, and all tonnages (P2D1 T1/2/3).
Table A4. Terminal transport template for two train pick-ups, short delivery time, and all tonnages (P2D1 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required Terminal Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half Loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
112402703801201200001050607000032
124905305903603903702010207012012000069
134705305907108008103030301201401300001318
144505305901070120012604040401301301400001828
154505305901430160017105040401301401300002135
22460820910230001010106070700001118
23930880960190320310202020100100100000713
24940106011707107807002010201201201200001618
25900106011701070117011403020301201301300002228
328308608900001020308012012000035
338101090131023020010101070707001010623
3413101260147010026015010202010010010001010918
3513301240147061079066020202011012012001010816
361360153017501070114010002010301201201300002027
371410158017001430152014702020301201301300002228
381410159018201770192018803030301301301300002843
391410153017602070228023203030401301301400002850
3101420159017702500270027203030401301401300003148
4311701230135000020202011080110101010515
4410301470176042020010101070708001010326
451600162018601102301201010101001001001010101223
46177016501820430760650202020110110110010101823
471830169019409301110105020202011012012001010319
481810207023101390145013202020301301201300002736
491830207023401660179017303030301301401400003148
4101820208024301930210021104030301501501400003666
4111880212023502410257025503030301201501400003351
5415301680179000010202080801001010101322
551710193022700001010209070901010101847
561970210022603020101020101001001001010101023
57213020802320320540510202020110120110010101432
58218021602400690870870202020110120120010101333
592270252026901160112011303020301201201200001833
5102260256029101540164014703030301301301500003348
5112390270029401750191018104030401401401500003951
5122360264029402160234023703030301301401300003866
641620175017700002020301001301101010101113
651850206022200002020201301701301010101831
6620902400264000010102080701001010102646
67246025502920000202020110100150101010838
682530262028001702702402020201201201301010101628
69265028703090500450540202020120130130010101440
6102740298035108707707303030301301301300001253
6112640308034401270124010703030401301301300003450
6122780316034001590172015404030301301301400004348
6132830318035401910211022103030301501301300004684
6142820330035302010215023805040401901401400005290
75198020902220000202030120120130101010920
761960222025800002020301801801602020102252
77242028103060000101020701601601010103353
782820312032500002020201001001401010102536
7928203140351013080102020201101101301010102348
7102990316036903803402602020201201301101010101148
71131303400384069065060030303012012013001001952
712330035103780110010309903030301301301300001231
7133200374040101450146013903030401501401400004663
71433003700425017101880208040303015014013000048110
71533003700411016901880208060504019015016000049100
7163280384041101750188020907060602101601600005898
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.
Table A5. Terminal transport template for two train pick-ups, medium delivery time, and all tonnages (P2D2 T1/2/3).
Table A5. Terminal transport template for two train pick-ups, medium delivery time, and all tonnages (P2D2 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required Terminal Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half Loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
112502602900000001010000013
12470530590000101020120110110000510
13470530570240270300202020130110120000813
144705305904805405902030301201201100001019
154705305907208008903030301201201100001224
21250280290000000100000033
2249053057000000020202000037
23710760880000101010110110110000414
2498010601210000202020110110110000719
2594010601170240270300202020801201300001324
312402702900000001010000034
32470550590000000202020000710
33710790880000000202020000714
34940106011400001010101101101001010101017
351180132014700001010101101101100001224
361410159017600002020201101101100001529
371410153017702502703002020201201201200001234
381410159017604805406002020201201201100002039
391410159017707208008902030301201301300002244
310135015901820960107011903030301201301200002958
42470530590000000202010000510
4368080089000000020202010001018
44900106011800000002020200001323
451170133014700001010101101101101010101325
461420159018300001010101201101101010101434
471640186020600002020201101201000001835
481880212023500002020201201201100002039
491800211022702402703002020201301301300002844
4101880219022704805406002020301301301400003143
4111880211022807208008903030301301301300002648
537407909100000002020201000414
54950102011800000002020201000619
551170137014700000002020200001725
561420159017100001010101101101002020201424
571650186020600001010101201101101010101834
581890212023500001020201201201201010101938
592120238025600002020201301201200002237
5102350265028500002020201201201200002542
5112350265029402502703002020201301301300002753
5122350255029404905406002030301301301300002158
64950106011800000002020201000919
651230132014700000002020202000820
661420159017700000002020200001429
671650185020700001010101101001002020201735
681960212023500001010101101201102020201333
692040239026500001010201201201101010102951
6102360265029400002020201301201201010102448
6112590302032200002020201201201200003653
6122820318036500002020201301201300003069
6132820330035202502703002020301401301200004263
6142820317035203704004503030301901901900003265
7511901320147000000040202010001123
761480159017700000002020202000924
771650193020600000002020200002334
781890212023500001010101001001003030301938
792110238025600001010101101001102020202338
7102260265029400001010201101101002020203357
7112600291032400002020201101101001010102653
7122820317035300002020201101101101010102959
7133060331038300002020201201101100002164
7143300384041300002020201201101100004569
7153430356041101301401503030301901901900001258
7163150370041102602703004040402002002100004783
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.
Table A6. Terminal transport template for two train pick-ups, long delivery time, and all tonnages (P2D3 T1/2/3).
Table A6. Terminal transport template for two train pick-ups, long delivery time, and all tonnages (P2D3 T1/2/3).
WagonsTrucksTerminal Transhipment Volume 1Required Terminal Stockyard 1Average Queuing Time 2Maximal Queuing Time 2Half Loaded Train Wagons 3Reduced Truck Trips 3
T1T2T3T1T2T3T1T2T3T1T2T3T1T2T3T2T3
11230230230000001060606000000
1236037041000020202070707000014
1346048053020505020202080909000048
14470510540160170240303030120110120000413
15470490550280350380303040120120130000815
21230220230000001060606000000
224704304500000101060606000000
2358064063000010201070707000054
2471078082000020202080808000069
258209201000000202030808090000815
2694010301160103020303030808090000919
27930990116014017017030303080110110000822
3610101210121000020202080801101010101717
3711801350139000020302080908000101418
38130014501590000303030901201100001324
3914701580176010100303030909090000923
3101410156017601301701503030301101201200001631
49151016201810000303030160160100101010925
410165018302000000303030160160110010101529
4111760198021800003030301601601000001835
4121880219024300003030301601601600002646
4131880204023601201401504040401701601600001543
5122000223023900003030301801701201010101933
513203023702590000403030170170160010102847
5142240252026900004030301701601700002338
5152360265029400004040401701701600002448
5162360255029301201401505040401801801700001850
6152480276028800004030302401701601010102333
616259028103270000403030180180180010101857
6172710305033600005040302601801700002854
6182820305035100005040401801801800001958
619270032903410120130906050502301802300005057
7183060329035700005040401901901801010101943
719305034203740000504040250190190010103158
7203170358039300005040401901901900003463
7213300383040400006050502501802200004462
72232903690405012010006060602702502200003253
7233300369040502402601507070602602202200003455
7243290370040803604103707070702502602400003867
7253290369042004805406008080702502502200003886
1 Average per week in solid cubic meters (rounded to the nearest ten). 2 In minutes (rounded to the nearest ten). 3 Average quantity per week.

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Figure 1. Flowcharts of the wood and transporting flows of the simulation model.
Figure 1. Flowcharts of the wood and transporting flows of the simulation model.
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Figure 2. General layout of small scale train terminals displaying loading track, stockyard, and truck driving route.
Figure 2. General layout of small scale train terminals displaying loading track, stockyard, and truck driving route.
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Figure 3. Best performing truck to wagon ratios for one train pick-up and low tonnages regarding three strategies.
Figure 3. Best performing truck to wagon ratios for one train pick-up and low tonnages regarding three strategies.
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Figure 4. Best performing truck to truck ratios for two train pick-ups and low tonnages regarding three strategies.
Figure 4. Best performing truck to truck ratios for two train pick-ups and low tonnages regarding three strategies.
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Figure 5. Reduced truck trips for one train pick-up. P = train wagons pick-ups: P1 = one a day, P2 = two a day. T = tonnage of forest trucks equipped with crane: T1 = low, T2 = moderate, T3 = high. D = delivery time to train terminal: D1 = short, D2 = medium, D3 = long.
Figure 5. Reduced truck trips for one train pick-up. P = train wagons pick-ups: P1 = one a day, P2 = two a day. T = tonnage of forest trucks equipped with crane: T1 = low, T2 = moderate, T3 = high. D = delivery time to train terminal: D1 = short, D2 = medium, D3 = long.
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Figure 6. Reduced truck trips for two train pick-ups. P = train wagons pick-ups: P1 = one a day, P2 = two a day. T = tonnage of forest trucks equipped with crane: T1 = low, T2 = moderate, T3 = high. D = delivery time to train terminal: D1 = short, D2 = medium, D3 = long.
Figure 6. Reduced truck trips for two train pick-ups. P = train wagons pick-ups: P1 = one a day, P2 = two a day. T = tonnage of forest trucks equipped with crane: T1 = low, T2 = moderate, T3 = high. D = delivery time to train terminal: D1 = short, D2 = medium, D3 = long.
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Table 1. 18 scenario settings for simulation.
Table 1. 18 scenario settings for simulation.
One Train Pick-Up (P1)Two Train Pick-Ups (P2)
Delivery Time (D) Delivery Time (D)
Tonnage (T)P1D1T1P1D2T1P1D3T1Tonnage (T)P2D1T1P2D2T1P2D3T1
P1D1T2P1D2T2P1D3T2P2D1T2P2D2T2P2D3T2
P1D1T3P1D2T3P1D3T3P2D1T3P2D2T3P2D3T3
P = train wagons pick-ups: P1 = one a day, P2 = two a day. T = tonnage of forest trucks equipped with crane: T1 = low (MIN = 23 t/MODE = 24 t/MAX = 25 t), T2 = moderate (26/27/28), T3 = high (29/30/31). D = delivery time to train terminal: D1 = short (MIN = 5 min/MODE = 10 min/MAX = 15 min), D2 = medium (35/40/45), D3 = long (65/70/75).
Table 2. One way truck delivery time.
Table 2. One way truck delivery time.
Delivery
Time
Drive Time (min)Number of Trips
Per Truck Per Day
MINMODEMAX
short510153–4
medium3540452–3
long6570751–2
Table 3. Truck tonnages and dependent process times.
Table 3. Truck tonnages and dependent process times.
Transport TonnagesTonnage (t)Load Truck Time (min)Unload Time at Stockyard/Wagon (min)
MINMODEMAXMINMODEMAXMINMODEMAX
low232425303540354555
moderate262728333843384858
high293031364146415161
Table 4. Best performing simulation results for strategy MAX VOLUME (maximal terminal transhipment volume) for one train pick-up.
Table 4. Best performing simulation results for strategy MAX VOLUME (maximal terminal transhipment volume) for one train pick-up.
WagonsNumber of TrucksTerminal Transhipment Volume (m3)Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
13542402502409501070320201070120120120
232449047047071001301006011070110
33567307107303605301701010607070120
4949980940980261002902007013070140
5481212201220118007704302010809080150
612121214701470141030001500180302070140110140
7781317101650164083016001010708080140
Table 5. Best performing simulation results for strategy NO STOCKYARD (no or low required terminal stockyard) for one train pick-up.
Table 5. Best performing simulation results for strategy NO STOCKYARD (no or low required terminal stockyard) for one train pick-up.
WagonsNumber of TrucksTerminal Transhipment Volume (m3)Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
1112230230230120080100506070110
222345047044024000100408070110
3235710680660000100507070110
4347940940930000100608070120
545912201170117000020106090100130
6461113501350140000020207012090150
75713163016401640000203070130110140
Table 6. Best performing simulation results for strategy BEST FIT (at least 90% terminal transhipment volume) for one train pick-up.
Table 6. Best performing simulation results for strategy BEST FIT (at least 90% terminal transhipment volume) for one train pick-up.
WagonsNumber of TrucksTerminal Transhipment Volume (m3)Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
1112230230230120080100506070110
222345047044024000100408070110
3235710680660000100507070110
4347940940930000100608070120
545812201170110000020105090100120
6461013501350132000020206012090140
75711163016401490000203060130110140
Table 7. Strategy comparison for one train pick-up (in %).
Table 7. Strategy comparison for one train pick-up (in %).
Delivery TimeMAX VOLUMENO STOCKYARDBEST FIT
ShortMediumLongShortMediumLong
Number of trucks100−49−36−17−49−36−23
Transhipment volume100−5−3−3−5−3−7
Required stockyard100−96−100−95−96−100−95
Average queuing times100−170−17−170−23
Maximal queuing times100−15−3−5−15−3−8
Table 8. Best performing simulation results for strategy MAX VOLUME (maximal terminal transhipment volume) for two train pick-ups.
Table 8. Best performing simulation results for strategy MAX VOLUME (maximal terminal transhipment volume) for two train pick-ups.
WagonsNumber of TrucksTerminal Transhipment Volume (m3) Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
1224490470470360016020103070120120
244694098094071001020203012011080
31069142014101470250001030203013011090
411812188018801880241000302030120120160
5111015239023502360175000402040140120170
6131218283028202820191000302050150130180
712152133003430330011001300303060130190250
Table 9. Best performing simulation results for strategy NO STOCKYARD (no or low required terminal stockyard) for two train pick-ups.
Table 9. Best performing simulation results for strategy NO STOCKYARD (no or low required terminal stockyard) for two train pick-ups.
WagonsNumber of TrucksTerminal Transhipment Volume (m3)Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
112224047036012000010205012070
23459309808201900020202010011080
3268830141013000001020308011090
43812117018801880000202030110120160
55101517102350236000010204090120170
671218246028202820000202050110130180
781421282033003300000202060100120250
Table 10. Best simulation results for strategy BEST FIT (at least 90% terminal transhipment volume) for two train pick-ups.
Table 10. Best simulation results for strategy BEST FIT (at least 90% terminal transhipment volume) for two train pick-ups.
WagonsNumber of TrucksTerminal Transhipment Volume (m3)Required Terminal Stockyard (m3)Average Queuing Time (min)Maximal Queuing Time (min)
D1D2D3D1D2D3D1D2D3D1D2D3D1D2D3
12234904704603600202010207012080
234693098094019001020203010011080
346913101410147010001010203010011090
4681117701880176043000202030110120160
5891421802120224069000202040110130170
69111626502590259050000202040120120180
710141829903300306038000202050120120190
Table 11. Strategy comparison for two train pick-ups (in %).
Table 11. Strategy comparison for two train pick-ups (in %).
Delivery TimeMAX VOLUMENO STOCKYARDBEST FIT
ShortMediumLongShortMediumLong
Number of trucks100−54−2−5−33−5−9
Transhipment volume100−23−1−3−7−4−5
Required stockyard100−97−100−100−75−100−78
Average queuing times100−50−7−7−35−7−11
Maximal queuing times100−26−8−5−15−8−10

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Kogler, C.; Rauch, P. Contingency Plans for the Wood Supply Chain Based on Bottleneck and Queuing Time Analyses of a Discrete Event Simulation. Forests 2020, 11, 396. https://doi.org/10.3390/f11040396

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Kogler C, Rauch P. Contingency Plans for the Wood Supply Chain Based on Bottleneck and Queuing Time Analyses of a Discrete Event Simulation. Forests. 2020; 11(4):396. https://doi.org/10.3390/f11040396

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Kogler, Christoph, and Peter Rauch. 2020. "Contingency Plans for the Wood Supply Chain Based on Bottleneck and Queuing Time Analyses of a Discrete Event Simulation" Forests 11, no. 4: 396. https://doi.org/10.3390/f11040396

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