This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.

Maximum likelihood is an ubiquitous method used in the estimation of generalized linear mixed model (GLMM). However, the method entails computational difficulties and relies on the normality assumption for random effects. We propose a second-order least squares (SLS) estimator based on the first two marginal moments of the response variables. The proposed estimator is computationally feasible and requires less distributional assumptions than the maximum likelihood estimator. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is proposed. We show that the SLS estimators are consistent and asymptotically normally distributed under fairly general conditions in the framework of GLMM. Missing data is almost inevitable in longitudinal studies. Problems arise if the missing data mechanism is related to the response process. This thesis develops the proposed estimators to deal with response data missing at random by either adapting the inverse probability weight method or applying the multiple imputation approach. In practice, some of the covariates are not directly observed but are measured with error. It is well-known that simply substituting a proxy variable for the unobserved covariate in the model will generally lead to biased and inconsistent estimates. We propose the instrumental variable method for the consistent estimation of GLMM with covariate measurement error. The proposed approach does not need any parametric assumption on the distribution of the unknown covariates. This makes the method less restrictive than other methods that rely on either a parametric distribution of the covariates, or to estimate the distribution using some extra information. In the presence of data outliers, it is a concern that the SLS estimators may be vulnerable due to the second-order moments. We investigated the robustness property of the SLS estimators using their influence functions. We showed that the proposed estimators have a bounded influence function and a redescending property so they are robust to outliers. The finite sample performance and property of the SLS estimators are studied and compared with other popular estimators in the literature through simulation studies and real world data examples.

In simulation-based design, a key challenge is to formulate and solve analysis problems efficiently to evaluate a large variety of design alternatives. The solution of analysis problems has benefited from advancements in commercial off-the-shelf math solvers and computational capabilities. However, the formulation of analysis problems is often a costly and laborious process. Traditional simulation templates used for representing analysis problems are typically brittle with respect to variations in artifact topology and the idealization decisions taken by analysts. These templates often require manual updates and "re-wiring" of the analysis knowledge embodied in them. This makes the use of traditional simulation templates ineffective for multi-disciplinary design and optimization problems. Based on these issues, this dissertation defines a special class of problems known as variable topology multi-body (VTMB) problems that characterizes the types of variations seen in design-analysis interoperability. This research thus primarily answers the following question: How can we improve the effectiveness of the analysis problem formulation process for VTMB problems? The knowledge composition methodology (KCM) presented in this dissertation answers this question by addressing the following research gaps: (1) the lack of formalization of the knowledge used by analysts in formulating simulation templates, and (2) the inability to leverage this knowledge to define model composition methods for formulating simulation templates. KCM overcomes these gaps by providing: (1) formal representation of analysis knowledge as modular, reusable, analyst-intelligible building blocks, (2) graph transformation-based methods to automatically compose simulation templates from these building blocks based on analyst idealization decisions, and (3) meta-models for representing advanced simulation templates VTMB design models, analysis models, and the idealization relationships between them. Applications of the KCM to thermo-mechanical analysis of multi-stratum printed wiring boards and multi-component chip packages demonstrate its effectiveness handling VTMB and idealization variations with significantly enhanced formulation efficiency (from several hours in existing methods to few minutes). In addition to enhancing the effectiveness of analysis problem formulation, KCM is envisioned to provide a foundational approach to model formulation for generalized variable topology problems.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

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The world becomes more integrated and sophisticated, especially in the birth of advanced technologies, which have influenced all life aspects. Automated systems could be considered an example of those aspects, which have been affected by recent changes in today’s life. The competition in the market is putting increasing pressure on different manufacturing organizations to find the best methods that enable them to stay up to date with the latest technologies in the industrial field. One of the most famous dilemmas that exist in this field is designing an efficient and flexible material handling system. This issue draws the attention of both decision-makers in different companies and software developers who put considerable effort into making that desired system real. Inclusive research needs to be performed to obtain such a system, and the most significant part of the research that requires special attention is the applied methodology.The approach to be adapted determines the degree of stability of a particular material handling system to function effectively in the case studied. Several methods are available and could be implemented to design that effective system such as meta-heuristic algorithms, and approaches that depend on simulation software tools. The latter approach, which is the simulation approach, seems to get increasing attention from developers of the industrial system since it plays a vital role in reducing the cost and preserving available resources. Besides, it helps predict future changes and scenarios of the system to be analyzed.In this project, a discrete-event simulation model was built for the proposed layout of the main shop floor owned by a Swedish manufacturing company. The corporation located in the south of Sweden, and it produces a vast range of manufacture of goods. The chosen methodology is a combination of lean, simulation, and optimization approaches. It has been implemented on the proposed layout in which material is handled into production lines by using automated guided vehicles (AGVs) as a means of transportation. The analysis of results shows potential benefits, where the production process became more efficient and organized since the operational cost has been reduced by decreasing the number of required vehicles. Moreover, the simulation approach facilitated testing new ideas and designing improved scenarios without the necessity to change the current state of the factory layout or disturbing the regular activities.

Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised: (i) Is this approach a suitable strategy to improve WP; (ii) Is the sensor for irrigation control suitable; (iii) Which crop growth models are suitable to be part of that approach; and (iv) Can the combined application with experiments prove an increase of WP? The stochastic simulation-optimization approach allowed deriving parameter values for an optimal irrigation control for sensor-based full and deficit irrigation strategies. Objective was to achieve high WP with high reliability. Parameters for irrigation control included irrigation thresholds of soil-water potentials because of the working principle behind plant transpiration where pressure gradients are transmitted from the air through the plant and into the root zone. Optimal parameter values for full and deficit irrigation strategies were tested in irrigation experiments in containers in a vegetation hall with drip irrigated maize and compared to schedule-based irrigation strategies with regard to WP and water consumption. Observation data from one of the treatments was used afterwards in a simulation study to systematically investigate the parameters for implementing effective setups of DI systems. The combination of simulation-optimization and irrigation experiments proved to be a suitable approach for investigating and improving WP, as well as for deriving optimal parameter values of different irrigation strategies. This was verified in the irrigation experiment and shown through overall high WP, equally high WP between deficit and full irrigation strategies, and achieved water savings. Irrigation thresholds beyond the measurement range of tensiometers are feasible and applicable. The pF-meter performed satisfactorily and is a promising candidate for irrigation control. Suitable crop models for being part of this approach were found and their properties formulated. Factors that define the behavior of DI systems regarding WP and water consumption were investigated and assessed. This research allowed for drawing the first conclusions about the potential range of operations of sensor-based DI systems for achieving high WP with high reliability through its systematical investigation of such systems. However, this study needs validation and is therefore limited with regard to exact values of derived thresholds.

Bike share system (BSS) has received increasing attention in research for its potential economic and environmental benefits. However, some research has pointed out the negative sustainability impacts of BSS from rebalancing activity, due to its greenhouse gas (GHG) emissions and additional vehicle travels. Additionally, bike and station manufacturing also bring considerable emissions to the system. Therefore, it is important to analyze the current rebalancing efficiency and sustainability of BSSs, and to assist the BSS operators in optimizing the BSS design. Existing studies lack tools to estimate the real-world rebalancing activities and vehicle usage for system sustainability evaluation and improvements. To address this gap, this research first proposed a framework to estimate rebalancing activities and applied a clustering-based method to estimate the rebalancing vehicle use. Applying the framework to the BSSs in Chicago, Boston, and Los Angeles, this study estimated the rebalancing operation and compared the rebalancing efficiencies among the three systems. The analysis results show that 1) only a small proportion of stations and bikes were involved in the daily rebalancing activities; 2) most rebalancing activities were operated during the daytime, while the overnight rebalancing was limited; 3) the system scale, trip demand, and station types are critical for the rebalancing efficiency; and 4) reducing the rebalancing activities at self-rebalance stations could help to improve the rebalancing efficiency and benefits system sustainability. Additionally, the sustainability performance (e.g., carbon emissions) of BSS is not only decided by the rebalance, but also the manufacturing of bikes and stations. It is important to consider all these factors when optimizing a BSS. The existing literature on system improvement for the BSSs lacks an integrated view, and a well-designed integrated model for current BSS improvement is needed. The second part of this thesis built a simulation-based optimization model and generated 2400 scenarios for evaluation. This model aims to minimize the expansion investment, rebalancing mileage, and maximize the system demand and service rate. A Weight Sum Model is applied to solve the multi-criteria decision analysis. The model results show that the best system improvement is to build a new station with a small capacity and initial bikes. The investment and location impacts are discussed to find the tradeoff among expansion strategies. A sensitivity analysis is conducted to evaluate how different weight combinations (refer to different preferences in decision making) impact the preferred station configuration (docks and bikes) and new station locations.

Due to the changes and improvements that have occurred over the years, the manufacturing sector has evolved. Companies in the 21st-century face changes in the marketplace that are difficult to predict due to international competition and the rapid emergence of new products. To cope, companies must reinvent themselves and design manufacturing systems that seek to produce quality and low-cost products, and respond to the changes that must be faced. These capabilities are encompassed in reconfigurable manufacturing systems (RMS), capable of dealing with uncertainties quickly and economically. On the other hand, production planning with this type of system presents a significant challenge. Although simulation-based optimization techniques have been applied to address certain RMS challenges, only a few studies have applied simulation-based multi-objective optimization to simultaneously address several conflicting design objectives, as is the case in this project. This project aims to investigate some aspects using SBMO that directly affect the performance of a plant and demonstrate the usefulness of the method. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

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