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In arid countries both water scarcity and salinity represent the key factors which drastically limit crop yield in irrigated agriculture. In addition, relatively poor management practices with pretty low water productivity (WP) seriously aggravate the situation. In order to get “more crop per drop', i.e., to substantially improve water use efficiency, this thesis proposes the novel strategy NEMO (Nested Experimental, Modeling, and Optimization Strategy) for reliably evaluating an optimal irrigation schedule. The proposed methodology relies upon a close interaction between in-depth field investigations and physically based process modeling. It is tailored specifically to fit the requirements in resource-restricted regions. Comprehensive field experiments, on site measurements as well as various laboratory analyses provide a representative database for characterizing the relevant environmental parameters as e.g. the soil properties at the considered location and the prevailing climate. A substantial part of the data obtained from the field experiments provided the input for the internationally recognized SVAT software DAISY1 or APSIM2, both physically based irrigation models which have already been successfully applied in arid regions. APSIM - which is used in the advanced parts of the study - includes not only a process based model for soil moisture transport but also a plant physiological model which describes the plant behavior under specific irrigation scenarios for a selected crop throughout a growing season. The adaption of the irrigation model to local conditions and its preliminary parameterization firstly follows available guidelines and data for areas with similar climate and soil conditions. Reference data and deterministic weather data served to build up DAISY’s basic model files. DAISY is then used within the framework of the custom made and problem oriented optimization software GET-OPTIS for evaluating the corresponding optimal irrigation schedule for a first preliminary series of experiments (IrrEx1). A second series of field experiments (IrrEx2) was accompanied by transient soil moisture measurements, which served for evaluating the soil hydraulic parameters, while the obtained yield was used for calibrating the plant physiological model of APSIM. Taking still into account the stochastic nature of weather phenomena, a stochastic optimization with GET-OPTIS was then applied not only for the traditional full irrigation but also for the most important deficit irrigation and the irrigation with saline water. The obtained optimal irrigation schedules are subsequently used for a final series of rigorous irrigation experiments (IrrEx3) which specifically focused on: (1) full irrigation for high yields with most economic water application, (2) deficit irrigation aiming at a maximum yield with only a limited amount of irrigation water, and (3) full irrigation with saline irrigation water for maximum yield. At the harvesting time, the observed crop yield and the water productivity were compared - together with other plant characteristics - with the corresponding calculated values. The agreement between calculated and measured crop data was excellent. All the field experiments have been performed following a parallel use of the common traditional FAO class A-Pan method and the novel NEMO technology. Based on the outcome of the field experiments, the NEMO applications demonstrated a striking superiority throughout all scenarios as compared to the FAO method as regards economic efficiency and sustainable use of irrigation water in both aspects water quantity and salt accumulation. Contrary to common practice, the optimal NEMO irrigation schedule - which relies on stochastic weather data - has an extended validity. Together with the use of physical data and adequate process models, the developed methodology features a highly promising potential for generalizing the experimental findings for other, environmentally similar, regions. NEMO thus opens wide possibilities for a cost effective and sustainable long-term application to other arid or semi-arid areas.

A two level hierarchical simulation and decision modeling framework is proposed for electric power networks involving PV based solar generators, various storage, and grid connection. The high level model, from a utility company perspective, concerns operational decision making and defining regulations for customers for a reduced cost and enhanced reliability. The lower level model concerns changes in power quality and changes in demand behavior caused by customers' response to operational decisions and regulations made by the utility company at the high level. The higher level simulation is based on system dynamics and agent-based modeling while the lower level simulation is based on agent-based modeling and circuit-level continuous time modeling. The proposed two level model incorporates a simulation based optimization engine that is a combination of three meta-heuristics including Scatter Search, Tabu Search, and Neural Networks for finding optimum operational decision making. In addition, a reinforcement learning algorithm that uses Markov decision process tools is also used to generate decision policies. An integration and coordination framework is developed, which details the sequence, frequency, and types of interactions between two models. The proposed framework is demonstrated with several case studies with real-time or historical for solar insolation, storage units, demand profiles, and price of electricity of grid (i.e., avoided cost). Challenges that are addressed in case studies and applications include 1) finding a best policy, optimum price and regulation for a utility company while keeping the customers electricity quality within the accepted range, 2) capacity planning of electricity systems with PV generators, storage systems, and grid, and 3) finding the optimum threshold price that is used to decide how much energy should be bought from sold to grid to minimize the cost. Mathematical formulations, and simulation and decision modeling methodologies are presented. A grid-storage analysis is performed for arbitrage, to explore if in future it is going to be beneficial to use storage systems along with grid, with future technological improvement in storage and increasing cost of electrical energy. An information model is discussed that facilitates interoperability of different applications in the proposed hierarchical simulation and decision environment for energy systems.

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.

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.

Several exact and approximate dynamic programming formulations have already been proposed to solve hotel revenue management (RM) problems. To obtain tractable solutions, these methods are often bound by simplifying assumptions which prevent their application on large and dynamic complex systems. This dissertation introduces HotelSimu, a flexible simulation-based optimization approach for hotel RM, and investigates possible approaches to increase the efficiency of black-box optimization methods in the presence of noise. In fact, HotelSimu employs black-box optimization and stochastic simulation to find the dynamic pricing policy which is expected to maximize the revenue of a given hotel in a certain period of time. However, the simulation output is noisy and different solutions should be compared in a statistically significant manner. Various black-box heuristics based on variations of random local search are investigated and integrated with statistical analysis techniques in order to manage efficiently the optimization budget.

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This master thesis presents a practical framework for integrating Simulation-Based Optimization into facility layout studies. While various methodologies exist for improving facility layouts and the utilization of Simulation-based Optimization, there is a gap in practical frameworks offering a systematic approach to combine these methods effectively. Existing frameworks lack specificity, require substantial prior knowledge, and offer limited insight into the methodologies employed. Consequently, there is a need for a comprehensive, step-by-step framework accessible to a broader range of practitioners. The proposed framework addresses this gap by providing a practical step-by-step approach that guides practitioners through a Simulation-Based Optimization integrated facility layout study. This framework facilitates the development of alternative layouts and system configurations using trusted methods. To assess the proposed framework, a case study was conducted at a manufacturing facility with the purpose of enhancing the production throughput. The case study followed the steps outlined in the framework in a real-world scenario, which provided valuable insights into the practicality and usefulness of the framework. The framework's effectiveness hinges on careful execution of each step, particularly given its front-loaded nature. Neglecting any step could lead to inaccuracies in subsequent stages, undermining the overall validity of the study. Adequate time allocation, especially in data collection and simulation model development, is critical to ensuring robust results. While the framework's applicability has been demonstrated in a high-mix low-volume production environment, its broader effectiveness across different settings remains to be explored. Nonetheless, the framework's intuitive flow and reliance on established methodologies enhance its usability and potential to improve production system throughput. Ultimately, the study contributes a tangible solution to the research question, offering practitioners a valuable tool for streamlining production.