The U.S. military uses pattern recognition training to observe anomalies in human behavior. An examination of the pattern recognition training literature for Warfighters reveals a gap in training to discern patterns of human behavior in live environments. Additionally, the current state of warfare is evolving and requires operations to change. As a result, pattern recognition training must accommodate new practices to improve performance. A technique used to improve memory for identifying patterns in the environment is Kim's game. Kim's game establishes patterns to identify inanimate objects, of which information retains in memory for later recall. The paper discusses the fundamental principles of Kim's game applied to virtual Simulation-Based Training. The virtual version of Kim's game contains customized scenarios for training behavior cue analysis. Virtual agents display kinesic cues that exhibit aggressive (i.e., slap hands and clench fist) and nervous behaviors including wring hands and check six. This research takes a novel approach by animating the kinesics cues in the virtual version of Kim's game for pattern recognition training. Detection accuracy, response time, and false positive detection serve as the performance data for analysis. Additional survey data collected include engagement, flow, and simulator sickness. All collected data was compared to a control condition to examine its effectiveness of behavior cue detection. A series of one-way between subjects design ANOVA's were conducted to examine the differences between Kim's game and control on post-test performance. Although, the results from this experiment showed no significance in post-test performance, the percent change in post-test performance provide further insight into the results of the Kim's game and control strategies. Specifically, participants in the control condition performed better than the Kim's game group on detection accuracy and response time. However, the Kim's game group outperformed the control group on false positive detection. Further, this experiment explored the differences in Engagement, Flow, and Simulator Sickness after the practice scenario between Kim's game group and the control group. The results found no significant difference in Engagement, partial significance for Flow, and significant difference for Simulator Sickness between the Kim's game and control group after the practice scenario. Next, a series of Spearman's rank correlations were conducted to assess the relationships between Engagement, Flow, Simulator Sickness, and post-test performance, as well as examine the relationship between working memory and training performance; resulting in meaningful correlations to explain the relationships and identifying new concepts to explain unrelated variables. Finally, the role of Engagement, Flow, and Simulator Sickness as a predictor of post-test performance was examined using a series of multiple linear regressions. The results highlighted Simulator Sickness as a significant predictor of post-test performance. Overall, the results from this experiment proposes to expand the body of pattern recognition training literature by identifying strategies that enhance behavior cue detection training. Furthermore, it provides recommendations to training and education communities for improving behavior cue analysis. ?

Feedback is essential to guide performance in simulation-based training (SBT) and to refine learning. Generally outcomes improve when feedback is delivered with personalized tutoring that tailors specific guidance and adapts feedback to the learner in a one-to-on environment. Therefore, emulating by automation these adaptive aspects of human tutors in SBT systems should be an effective way to train individuals. This study investigates the efficacy of automating different types of feedback in a SBT system. These include adaptive bottom-up feedback (i.e., detailed feedback, changing to general as proficiency develops) and adaptive top-down feedback (i.e., general feedback, changing to detailed if performance fails to improve). Other types of non-adaptive feedback were included for performance comparisons as well as to examine the overall cognitive load. To test hypotheses, 130 participants were randomly assigned to five conditions. Two feedback conditions employed adaptive approaches (bottom-up and top-down), two used non-adaptive approaches (constant detailed and constant general), and one functioned as a control group (i.e., only a performance score was given). After preliminary training on the simulator system, participants completed four simulated search and rescue missions (three training missions and one transfer mission). After each training mission, all participants received feedback relative to the condition they were assigned. Overall performance on missions, knowledge post-test scores, and subjective cognitive load were measured and analyzed to determine the effectiveness of the type of feedback. Results indicate that: (1) feedback generally improves performance, confirming prior research; (2) performance for the two adaptive approaches (bottom-up vs. top-down did not differ significantly at the end of training, but the bottom-up group achieved higher performance levels significantly sooner; (3) performance for the bottom-up and constant detailed groups did not differ significantly, although the trend suggests that adaptive bottom-up feedback may yield significant results in further studies. Overall, these results have implications for the implementation of feedback in SBT and beyond for other computer-based training systems.

This research focuses on effective decision-making for sustainable manufacturing design and management. The research contributes to the decision-making tools that can enable sustainability analysts to capture the aspects of the economic, environmental and social dimensions into a common framework. The framework will enable the practitioners to conduct a sustainability impact analysis of a real or proposed manufacturing system and use the outcome to support sustainability decision. In the past, the industries had focused more on the economic aspects in gaining and sustaining their competitive positions; this has changed in the recent years following the Brundtland report which centred on incorporating the sustainability of the future generations into our decision for meeting today's needs (Brundtland, 1987). The government regulations and legislation, coupled with the changes in consumers' preference for ethical and environmentally friendly products are other factors that are challenging and changing the way companies, and organisations perceive and drive their competitive goals (Gu et al., 2015). Another challenge is the lack of adequate tools to address the dynamism of the manufacturing environment and the need to balance the business' competitive goal with sustainability requirements. The launch of the Life Cycle Sustainability Analysis (LCSA) framework further emphasised the needs for the integration and analysis of the interdependencies of the three dimensions for effective decision-making and the control of unintended consequences (UNEP, 2011). Various studies have also demonstrated the importance of interdependence impact analysis and integration of the three sustainability dimensions of the product, process and system levels of sustainability (Jayal et al., 2010; Valdivia et al., 2013; Eastwood and Haapala, 2015). Although there are tools capable of assessing the performance of either one or two of the three sustainability dimensions, the tools have not adequately integrated the three dimensions or address the holistic sustainability issues. Hence, this research proposes an approach to provide a solution for successful interdependence impact analysis and trade-off amongst the three sustainability dimensions and enable support for effective decision-making in a manufacturing environment. This novel approach explores and integrates the concepts and principles of the existing sustainability methodologies and frameworks and the simulation modelling construction process into a common descriptive framework for process level assessment. The thesis deploys Delphi study to verify and validate the descriptive framework and demonstrates its applicability in a case study of a real manufacturing system. The results of the research demonstrate the completeness, conciseness, correctness, clarity and applicability of the descriptive framework. Thus, the outcome of this research is a simulation-based impact analysis framework which provides a new way for sustainability practitioners to build an integrated and holistic computer simulation model of a real system, capable of assessing both production and sustainability performance of a dynamic manufacturing system.

This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.

ABSTRACT The aim of industrial production changed from mass production at the beginning of the 20th century. Today, production flexibility determines manufacturing companies' course of action. In this sense, Volvo Group Trucks Operations is interested in meeting customer demand in their assembly lines by adjusting manpower. Thus, this investigation attempts to analyze the effect of manning on the main final assembly line for thirteen-liter heavy-duty diesel engines at Volvo Group Trucks Operations in Skövde by means of discrete-event simulation. This project presents a simulation model that simulates the assembly line. With the purpose of building the model data were required. One the one hand, qualitative data were collected to improve the knowledge in the fields related to the project topic, as well as to solve the lack of information in certain points of the project. On the other hand, simulation model programming requires quantitative data. Once the model was completed, simulation results were obtained through simulation-based optimization. This optimization process tested 50,000 different workforce scenarios to find the most efficient solutions for three different sequences. Among all results, the most interesting one for Volvo is the one which render 80% of today’s throughput with the minimum number of workers. Consequently, as a case study, a bottleneck analysis and worker performance analysis was performed for this scenario. Finally, a flexible and fully functional model that delivers the desired results was developed. These results provide a comparison among different manning scenarios considering throughput as main measurement of the main final assembly line performance. After analyzing the results, system output behavior was revealed. This behavior allows predicting optimal system output for a given number of operators.

The focus of this thesis is on the precast concrete products manufacturing industry, which as one of the labour-intensive industries requires a substantial number of highly skilled operators in terms of crews to produce the final product. A crew is a group of multi-skilled chargehands and operators that have various skills and experience necessary to conduct an activity in a professional way. The high cost of skilled operators and the apparent inefficiencies of utilising such skilled operators in the industry are the major driving force. To achieve this, optimal crew allocation is required. Crew allocation is complex because of the multi-criteria nature of the problem and availability of thousands of possibilities and allocation alternatives. There is a gap in previous research efforts associated with crew allocation planning in the precast industry. Current practices suggest that the crew allocation process is carried out intuitively and the allocation of crews to production processes is subjective. This has led to high process-waiting times, improper allocation of skilled operators and ultimately higher production costs. In this context, the aim of this research is to propose an effective crew allocation methodology and a computer-based intelligent simulation model for its implementation. The objective of the approach is to guarantee a better workflow through minimising process-waiting time, optimising operator utilisation, and subsequently reducing the allocation cost. This research develops a holistic and integrated methodology for modelling crew allocation problems by reviewing state-of-art resource allocation techniques, structured interviews with production managers, site visits and a detailed case study. The methodology is developed using an IDEF0 process model and a generic process map for both the business and the production processes of the precast manufacturing system. A multi-layered genetic algorithm model is developed in conjunction with a process-simulation model to form a hybrid allocation system dubbed ‘SIM_Crew’. The model incorporates databases (Excel and MS Access), a simulation model (developed using Arena 12.0) and genetic algorithms (developed using Visual Basic for Applications) to facilitate the generation and evaluation of various “what-if” crew allocation scenarios. A number of performance criteria have been developed to evaluate the allocation plans. ‘SIM_Crew’ enables the investigation and analysis of allocating possible schedules and provides a facility to visualise the production processes. ‘SIM_Crew’ was validated using real life case study data and it was concluded that the allocation of crews to precast processes using genetic algorithm improves the throughput time and reduces the allocation cost as compared with real life production data. It is anticipated that future use of this research will solve the crew allocation problem in the precast industry.

The purpose of business training programs is to improve performance, which improved performance changes leadership behaviors based on the knowledge, skills, and abilities (KSAs) learned in training. One of the most common criticisms of leadership training is the tendency to focus on teaching theory but not on applying theory into practice, that is, transfer of learning. Research usually ends at the point of identifying, describing, or measuring factors that influence transfer. Ongoing research must identify what constructs in the transfer of learning process should be effectively changed or managed. There is a gap in research on the degree to which performance improvement through KSAs learned in a simulation training program actually transfer to the work environment. Additional research is needed that examines the relationship between transfer of learning and intent to transfer, which are critical outcomes in the field of human resource management and development. The purpose of the study was to examine the relationship between intent to transfer and four constructs in the transfer of learning process during a simulation-based leadership training program. Participants completed self-report assessments that measured the relationships between intent to transfer and four constructs: ability, motivation, work environment, and learner readiness. A correlational design was administered using a population of mid-level managers in a telecommunications organization.

Our world is filled with large-scale engineering systems, which provide various services and conveniences in our daily life. A distinctive trend in the development of today's large-scale engineering systems is the extensive and aggressive adoption of automation and autonomy that enable the significant improvement of systems' robustness, efficiency, and performance, with considerably reduced manning and maintenance costs, and the U.S. Navy's DD(X), the next-generation destroyer program, is considered as an extreme example of such a trend. This thesis pursues a modeling solution for performing simulation-based analysis in the conceptual or preliminary design stage of an intelligent, self-reconfigurable ship fluid system, which is one of the concepts of DD(X) engineering plant development. Through the investigations on the Navy's approach for designing a more survivable ship system, it is found that the current naval simulation-based analysis environment is limited by the capability gaps in damage modeling, dynamic model reconfiguration, and simulation speed of the domain specific models, especially fluid network models. As enablers of filling these gaps, two essential elements were identified in the formulation of the modeling method. The first one is the graph-based topological modeling method, which will be employed for rapid model reconstruction and damage modeling, and the second one is the recurrent neural network-based, component-level surrogate modeling method, which will be used to improve the affordability and efficiency of the modeling and simulation (M&S) computations. The integration of the two methods can deliver computationally efficient, flexible, and automation-friendly M&S which will create an environment for more rigorous damage analysis and exploration of design alternatives. As a demonstration for evaluating the developed method, a simulation model of a notional ship fluid system was created, and a damage analysis was performed. Next, the models representing different design configurations of the fluid system were created, and damage analyses were performed with them in order to find an optimal design configuration for system survivability. Finally, the benefits and drawbacks of the developed method were discussed based on the result of the demonstration.

In this thesis, a framework is laid for holistic uncertainty management for simulation-based design of multiscale systems. The work is founded on uncertainty management for microstructure mediated design (MMD) of material and product, which is a representative example of a system over multiple length and time scales, i.e., a multiscale system. The characteristics and challenges for uncertainty management for multiscale systems are introduced context of integrated material and product design. This integrated approach results in different kinds of uncertainty, i.e., natural uncertainty (NU), model parameter uncertainty (MPU), model structure uncertainty (MSU) and propagated uncertainty (PU). We use the Inductive Design Exploration Method to reach feasible sets of robust solutions against MPU, NU and PU. MMD of material and product is performed for the product autonomous underwater vehicle (AUV) employing the material in-situ metal matrix composites using IDEM to identify robust ranged solution sets. The multiscale system results in decision nodes for MSU consideration at hierarchical levels, termed as multilevel design. The effectiveness of using game theory to model strategic interaction between the different levels to facilitate decision making for mitigating MSU in multilevel design is illustrated using the compromise decision support problem (cDSP) technique. Information economics is identified as a research gap to address holistic uncertainty management in simulation-based multiscale systems, i.e., to address the reduction or mitigation of uncertainty considering the current design decision and scope for further simulation model refinement in order to reach better robust solutions. It necessitates development of an improvement potential (IP) metric based on value of information which suggests the scope of improvement in a designer's decision making ability against modeled uncertainty (MPU) in simulation models in multilevel design problem. To address the research gap, the integration of robust design (using IDEM), information economics (using IP) and game theoretic constructs (using cDSP) is proposed. Metamodeling techniques and expected value of information are critically reviewed to facilitate efficient integration. Robust design using IDEM and cDSP are integrated to improve MMD of material and product and address all four types of uncertainty simultaneously. Further, IDEM, cDSP and IP are integrated to assist system level designers in allocating resources for simulation model refinement in order to satisfy performance and robust process requirements. The approach for managing MPU, MSU, NU and PU while mitigating MPU is presented using the MMD of material and product. The approach presented in this article can be utilized by system level designers for managing all four types of uncertainty and reducing model parameter uncertainty in any multiscale system.

<p>When a new product should be acquired, a model over its functionality is made. A quite new idea in the military area is to use simulations to find out what and how much to acquire. Since the product never has been on the market before it is hard to know how factors in the surroundings, like weather and other active objects, will affect it. Therefore these unknown factors that appear during the creation or acquiring of a new product need to be taken into consideration.</p><p>A literature study is performed about how modelling of simulations can be done, and how unknown factors can be considered when modelling a simulation. The study goes into if unknown factors are taken into consideration when modelling in the Process component in Simulation Based Acquisition (SBA). The result of this study shows that SBA facilitates in the process of finding and reducing unknown factors.</p>