Browsing by Author "Boutselis, Petros"
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Item Open Access COMBAT SIMULATION - TWO SIDES.xls(Cranfield University, 2016-05-25 09:51) Boutselis, Petros; Ringrose, TrevorThe data are the outputs of the simulation of a closed-loop, two-sides (Blue vs Red) combat model saved in an Excel spreadsheet. The 1st tab has the input data while the other two tabs have the outputs for the Blue and for the Red. Each column in the Input tab is a design point randomly selected (250 in total). The first two rows represent the number of tanks and recce of the Blue, while rows 3 and 4 represent the respective Red. The 5th row represents the probability of shock that has been applied the same to both sides. Rows 6, 7 and 8 represent the percentage Unit participation of Tanks, Machine Guns and Anti-tanks respectively. These last three variables also apply equally to both Blue and Red. The model used (SIMBAT) is stochastic and therefore each design point was run 40 times, taking care to use the same random numbers for each point (Common Random Numbers) Therefore each of the 250 columns in the two output tabs has 40 rows. The data have been used in: P. Boutselis, Trevor J. Ringrose GAMLSS and neural networks in combat simulation metamodelling: A case study. Expert Syst. Appl. 40(15): 6087-6093 (2013), doi:10.1016/j.eswa.2013.05.023 to produce two different metamodels: a statistical model (GAMLSS) and a neural network (ANN), while recently the same data have been used to produce a Bayesian Network.Item Open Access Investigating the applicability of Bayesian networks to demand forecasting during the final phase of support operations(2019-03) Boutselis, Petros; McNaught, Ken R.; Zagorecki, AdamA challenge faced by businesses that provide logistical support to systems is when the provision of those support services is no longer required. A typical example of such a situation is when military operations come to an end. In such cases, those companies that have a contract with the Armed Forces to provide maintenance support for the deployed systems, need to maintain those systems at minimum cost during that final phase, that is from the time the decision to stop the operations is announced until their very end. During the final phase, a challenging problem is forecasting the demand for spare parts, corresponding to equipment failures within the system. This is because the support context, the number of supported systems, the support equipment or even the operational demand can change during that period, and also because there can be very limited opportunities to place orders to cover demand. This thesis suggests that these types of problems can take advantage of the data that have been collected during the support operations prior to the initiation of the closing down process. Moreover, the thesis investigates the exploitation of these data by the use of Bayesian Networks to forecast the demand for spares that will be required for the provision of maintenance during the final phase. The research uses stochastically simulated Support Chain scenarios to generate data and also to evaluate different methods of constructing Bayesian Networks. The simulated scenarios differ in the demand context as well as in the complexity of the Equipment Breakdown Structure of the supported systems. The Bayesian Networks’ structure development methods that are tested include unsupervised machine learning, eliciting the structure from Subject Matter Experts, and two hybrid approaches that combine expert elicitation and machine learning. These models are compared to respective logistic regression models, as well as subject matter experts-adjusted single exponential smoothing forecasts. The comparison of the models is made using both accuracy metrics and accuracy implication metrics. These forecast models’ comparison methods are analysed in order to evaluate their appropriateness. The analyses have provided a number of novel outputs. The algebraic analysis of the accuracy metrics theoretically proves empirical problems that have been discussed in the literature but also reveals others. Regarding the accuracy implication metrics, the analysis shows that for the particular type of problems examined in this thesis –final phase problems – the accuracy implication metrics commonly applied are not enough to inform decision making, and a number of additional ones are required.The research shows that for the scenarios examined, the Bayesian Networks that had their structure learned using an unsupervised algorithm performed better in the accuracy metric than any of the other models. However, even though these Bayesian Networks also did well with the accuracy implication metrics, neither they, nor any of the others was consistently dominant. The reason for the discrepancy in the results between the accuracy and the accuracy implication metrics is that the latter are not only driven by how accurate the forecast model’s prediction is, but also by the model of the residual error and the bias.Item Open Access Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context(Elsevier, 2018-06-28) Boutselis, Petros; McNaught, Ken R.A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work.