Forestry 2017 - The Simulation
Forestry 2017 The Simulation is developed by Joindots in collaboration with SilentFuture and is published under the banner of United Independent Entertainment GmbH. This game was released on 25th March, 2016. You can also download American Truck Simulator 2016.
Forestry 2017 - The Simulation
In this version of Forestry 2017 The Simulation PC Game you will be playing as a professional woodcutter and need to go into the forest in order to collect the woods. You need to control the machinery like Tractors, trucks, harvester. After cutting the wood you need to arrange it and then sell it in the market. The main goal is to earn money and to expand your company. You have to maintain your forest and need to take care of different species of the tree in this way you can earn more money which can be utilized in buying advanced machinery and equipment. You can select the incoming orders and then can adjust the cycle of production accordingly. The game features a day/night cycle and also a unique character leveling system. The graphics of this game are awesome and you will surely love to play this game. You can also download Euro Truck Simulator.
Thirty-one percent of the world's land surface, equivalent to 4 billion hectares, is covered by forests (Adams 2012). Forests are considered to be the world's largest repository of terrestrial biodiversity (Food and Agriculture Organization of the United Nations [FAO] 2014), and they affect our daily lives considerably (World Wildlife Fund [WWF] 2016). They provide economic benefits for communities and habitats for diverse animal species (WWF 2016). They also reduce global warming by sequestering carbon dioxide during photosynthesis (Pan et al. 2011, WWF 2016). Therefore, it is important to make sure that forests are used in a sustainable manner (Whiteman 2014, Kangas et al. 2015). This requires proper decision making related to forestry while taking into account the competing and often conflicting interests of various stakeholders such as governments, industry, environmental groups, etc.
Discrete-event simulation is a modeling technique designed to mimic real-world systems where various states of the system (such as queues) change at random points in time because of the occurrence of discrete events (Sadoun 2000, Power 2002, Banks et al. 2005, Hillier and Lieberman 2015, Kaizer et al. 2015). It is the most popular type of simulation (the others being agent based, system dynamics, and Monte Carlo) (Jahangirian et al. 2010), particularly for studies on process flow. The use of discrete-event simulation modeling allows the consideration of uncertain/stochastic variables without the need for a large model that takes up considerable resources or has a very high cost (Myers and Richards 2003, Banks et al. 2005, Hillier and Lieberman 2015). In the forest products sector, the simulation modeling was used to analyze systems with uncertainties and interactions between system components. Most of the studies in the forest products sector that used discrete-event simulation have assessed different facility layouts, harvesting systems, or supply chains. The main uncertainties in these models have been forest and tree attributes, processing times, machine failure and repair rates and durations, machine interactions, demand, and transportation distances.
Because of the increased usefulness of discrete-event simulation as a decision-making tool in other industries such as health care (Swain 2015a), automotive, electronics, and aerospace, the challenges facing the forest products sector (Shahi and Pulkki 2013), and the limited number of existing review papers (Awudu and Zhang 2012, Shahi and Pulkki 2013, Rahman et al. 2014a, Segura et al. 2014) covering discrete-event simulation in forestry, this article reviews the relevant literature (from 1990 to 2015) in the forest products sector to highlight the applications of discrete-event simulation, gain a better understanding of the benefits and usefulness of it as a modeling technique in the sector, and identify future directions for research.
Computerized simulation methods have evolved over the last 50 to 60 years from programming languages to software packages that demonstrate the behaviors of the system visually (Nance 1996, Nance and Sargent 2002, Swain 2013). Much of this evolution is attributed to the available computer power, specifically related to memory and speed (Smith 2003), and therefore the use of simulation modeling progressed from being limited to a few research centers to being almost ubiquitous (Jacobson et al. 2006, Swain 2015a). Simulation has become an extremely useful tool (Swain 2015a), with more than 55 packages currently available on the market (Swain 2015b), all of which are capable of discrete-event simulation.
Discrete-event simulation has been used in the forest products sector since at least 1972, when Johnson et al. (1972) studied timber-harvesting systems. The studies here are grouped into those that (1) compared forest management techniques and harvesting systems, (2) assessed different facility layouts and configurations, (3) assessed transportation and supply-chain strategies and techniques, (4) determined facility locations, and (5) performed feasibility and cost assessment. Some studies had some overlap between these categories, but they were placed in the category that best matches the main purpose of the article. Table 1 summarizes all the studies and includes their various uncertain parameters.
Early simulation models in the forest sector were developed to determine the best equipment mix for whole-tree chipping operations (Johnson and Biller 1974). Although these models did assess the interactions of different machines, they did not include a discussion on validation, and they only had three replications of each of 12 scenarios. Many studies before the mid-1990s involved the analysis of either single machines or were deterministic, numerical simulation (Wang et al. 1998, Wang and Greene 1999). It was not until Baumgras et al. (1993), who assessed the differences between two logging crews and different wood utilization alternatives, that most studies included significant validation sections and assessed machine interactions in more detail. It was claimed that validation was not discussed in detail in previous studies mostly because of the high cost of collecting data to perform it adequately (Baumgras et al. 1993).
Studies by Hogg (2009) and Hogg et al. (2010) were conducted to determine the utility of simulation software for analyzing different forest-harvesting techniques and their effects on productivity and cost. They analyzed three systems: System 1 (the base case) and System 2 consisted of the same machinery, but differed in terms of the operating procedures and policies; System 3 changed both equipment type and operating procedures and policies. It was determined that Arena 9 software could be used for forest operations problems, but it required a high level of user expertise to understand the complexities of the system, as well as some other limitations in the changing of background logic.
A simulation system to investigate the impact of mixed species management of hardwood plantations on the proportion of clear (wood without knots) cherrybark oak was conducted by Oswalt (2008). The model incorporated different combinations of plantation types (dense and sparse), and different combinations of treatments to the trees. Tree characteristics, such as mortality rate, diameter, volume, and crown size, were uncertain parameters considered. When initial stand density was similar, the mixed-species approach was found to produce greater amounts of clear wood. The model was found to be a valuable method for the evaluation of hardwood plantations.
There are studies in both primary and secondary wood manufacturing that used discrete-event simulation to assess the different facility layouts or configurations. To the best of our knowledge, discrete-event simulation studies at primary wood manufacturing mills other than sawmills, such as veneer or chipping mills, have not been conducted. Some studies were conducted in furniture rough mills where raw wood or lumber is broken down into the parts required for the furniture being manufactured in other facilities (Kline et al. 1992, Wiedenbeck and Araman 1995, Thomas and Buehlmann 2002), whereas others were conducted in the furniture manufacturing facilities themselves (Gupta and Arasakesari 1991, Kyle and Ludka 2000). To the best of our knowledge, there are no published studies conducted on the manufacturing processes of various engineered wood products, or the manufacturing of products such as kitchen cabinets. This is likely because possible studies would be conducted privately and confidentially by the individual companies and would not be the subjects of published material.
Many early simulation studies in sawmilling focused on log breakdown patterns (Reynolds and Gatchell 1969), whereas Aune (1973) assessed wood manufacturing processes. The study analyzed how changes in the log characteristics and interactions between machine centers affected the total productivity. The productivity was found to be highly sensitive to changes in the characteristics of the logs and machines. The findings of Aune (1973, 1974) led to the development of future models studying facility layouts and configurations, particularly for sawmills.
Kline et al. (1992) conducted a study at an eastern US furniture rough mill to evaluate throughput, operation expenses, inventory levels, and delays due to bottlenecks. The bottleneck of the process was determined to be the ripsaw. The article by Gupta and Arasakesari (1991) assessed the effects of the addition of a third packaging line, a change in the availability of the edge banders, and changes in batch sizes being processed, compared with the existing system, on the capacity and in-process inventory of a facility in Zeeland, Michigan. The interactions of all machinery, including the breakdowns and downtime, were considered. The development of a model to evaluate a proposed layout of a dining room tabletop plant was discussed in Kyle and Ludka (2000). The evaluation was based on the effects of the proposed layout on staffing levels in each department, batch sizes, buffer sizes, and the flow between multiple departments. Specific results were not provided, but it was stated that the study provided great value to the partner company. Moreover, the impacts on staffing levels could be considered as a connection to the social implications of simulation modeling. 041b061a72