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simulation modeling and arena solution manual zip
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Discrete-event simulation is an important tool for the modeling of complex systems. Simulation is used to represent manufacturing, transportation, and service systems in a computer program to perform experiments on a computer. Simulation modeling involves elements of system modeling, computer programming, probability and statistics, and engineering design. Simulation Modeling and Arena, by Dr. Manuel Rossetti, is an introductory textbook for a first course in discrete-event simulation modeling and analysis for upper-level undergraduate students as well as entering graduate students. The text is focused on engineering students (primarily industrial engineering); however, the text is also appropriate for advanced business majors, computer science majors, and other disciplines where simulation is practiced. Practitioners interested in learning simulation and Arena could also use this book independently of a course.
Note: The running and animation of Arena and some large simulation models can be calculation-intensive, so a faster processor with additional memory may result in significantly improved performance. In addition, a larger monitor and a screen resolution of at least 1024 x 768 are recommended for improved animation viewing.
To illustrate the difference between continuous and rule-based population modeling approaches, we compared BacArena and COMETS [24] in the context of a two-species syntrophic community of the methanogenic archeum Methanosarcina barkeri and the hydrogen producing bacterium Clostridium beijerinckii (Fig 3). The hydrogen produced by C. beijerinckii is taken up as an electron donor by M. barkeri to reduce carbon dioxide to methane, which is secreted into the environment. This is in concordance with experimental knowledge, showing the metabolic exchange between hydrogen producing bacteria and methanogenic archaea [31]. Notably, COMETS and BacArena produce similar results in terms of these predicted cross-feeding interactions and are therefore consistent. Based on the quantitative biomass production, both methods predict a smaller growth of M.barkeri compared to C.beijerinckii, however, the biomass production is higher in COMETS compared to BacArena. For the exponential phase of each simulation COMETS predicted a doubling time of 0.5h, BacArena predicted 1.1h, and the experimentally measured value is 4.3h [32]. The reason for this difference can be attributed to the underlying growth model of both methods. COMETS models colony growth as a 2D diffusion while BacArena models individual cell behavior and replication which causes the population to grow slower in the initial phase to reach a certain number of individuals. In BacArena populations consist of heterogeneous individuals (bottom-up) which have their own characteristics, e.g. movement and metabolic phenotypes. COMETS, on the other hand, is a top-down approach describing colonies on the population level (Fig 3). Both approaches differ concerning the representation of the spatial scale. In BacArena one individual is represented per grid position, whereas COMETS represents a population of multiple cells per position. Both, BacArena and COMETS, can predict heterogeneous growth rates according to spatial concentration gradients. By focusing on individuals, BacArena can be used to model additional heterogeneity of cells by accounting for their history and by integration of further rules such as cellular lysis. The explicit consideration of heterogeneous individuals has been regarded as especially helpful for addressing the complexity of biological systems, because local species interactions can represent biological systems more realistically [28, 33, 34]. In particular, the heterogenic movement in BacArena can be relevant when modeling an aqueous or viscose environment, such as the human gut, in which the movement is accelerated. Furthermore, by combining individual-based modeling with FBA, BacArena can model the metabolic state of each individual cell to investigate metabolic heterogeneity within a population of cells. This metabolic heterogeneity is captured by our definition of metabolic phenotypes, whose applicability and biological relevance we show in the next section on the basis of a biofilm model of Pseudomonas aeruginosa.
To investigate the impact of alternative optimal FBA solutions on the reproducibility of our results, we randomized the selection of alternative optimal solutions and checked the simulations against each other (S4 Fig). We found that growth curves did not change with differing methods, which we expected since our simulated alternative optimal solutions have the same objective value (in our case the growth rate). Despite some metabolite concentrations variations (S4 Fig), the general trend was consistent and thus we concluded our results to be stable.
By default, FBA is used to calculate the metabolic fluxes given the metabolite concentrations of the local grid cells. Since most metabolic models are undetermined by having more reactions than metabolites, alternative optimal solutions (different flux distributions with the same objective value) occur during the simulations. To deal with this issue, we devised several alternatives to standard FBA calculations, which can be chosen by the user. For instance, parsimonious FBA can be used to minimize the total flux through all reactions of a metabolic model. In this case, the primary objective (e.g. biomass) is optimized first and afterwards a secondary objective (total flux) is minimized using the first optimal objective value as a constraint. The second optimization acts as a proxy for minimal enzyme usage to simulate a more realistic behavior of cells in the exponential growth phase [59]. Additionally, the secondary objective can be chosen as a single reaction, which is picked randomly for each individual in each optimization, while enforcing the same biomass objective, pre-computed by FBA. The randomization of alternative optimal solutions can also be performed on the level of exchange reactions exclusively to get a better representation of secreted and consumed metabolites. The resulting flux distribution of the respective simulation strategy is then used to calculate and update the secretion or uptake for each individual in each simulation step. The linear programming problems can be solved using different solvers, such as GLPK [66], CLP [67], CPLEX [68], and Gurobi [69].
A model for the human gut was assembled using seven recently reconstructed genome-scale metabolic models of human gut bacteria [77]. In this study, the models were manually curated and checked using published experimental data. The bacterial species were selected according to their relevance and abundance within the human gut microbiota to represent a simplified human intestinal microbiota (SIHUMI) [47]. The following microbial reconstructions were used Anaerostipes caccae DSM 14662, Bacteroides thetaiotaomicron VPI-5482, Blautia producta DSM 2950, Escherichia coli str. K-12 substr. MG1655, Clostridium ramosum VPI 0427, DSM 1402, Lactobacillus plantarum subsp. plantarum ATCC 14917, Bifidobacterium longum NCC2705, and Akkermansia muciniphila ATCC BAA-835. The models used for the simulations are available on vmh.uni.lu as well as S2 File.
The selection of work orders toform a new batch to be released into the shop floor and the sequencingof individual orders within a batch are two problems which must be solvedrepeatedly. In the proposed approach solution procedures for the two sub-problemsare presented, which are able to consider sequence dependent setup timesfor the component placement machines in the cell. The system performanceis measured by a simulation model of the respective cell. In addition,local dispatching and sequencing rules are applied within the simulationmodel. A numerical study for different order arrival processes is conductedand the contribution of input sequencing to the system performance is shown.
The teaching approach to managementin general does not take in consideration the interaction between the differentsubject areas due to the organizational structure of the academic programs.In real life situations however, problems solution required most of thetimes the understanding of the multiple subject interactions within theorganization. For instances decisions that directly affect the productionprocess are taken many times without knowing its implication in the financialsector. This lack of interaction might cause waste of resources that couldbe saved and used in a better way. Using data of a ceramic company we aredeveloping an integrated simulation model to allow the student to becomeaware of the complexity of the process decision. Functional areas of salesforecasting, production and finance are integrated such that the effectof any decision taken in one of these areas can be evaluated in the others.This model can be used as a teaching tool in the classroom. 2ff7e9595c
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