The steel production is a highly capital and energy intensive industry that due to recent raw materials’ price increase and lowered demand, it has been squeezed and forced to look more deeply on how to add value to the customer at lower operative costs. The project was carried out on site at the ArcelorMittal’s millin Esch-Belval, Luxembourg which comprises an integrated melt shop, continuous casting plant and the rolling mill with the objectives of proposing optimization rules for the cold sector of the rolling mill and to analyze the impact of the future truckbay shipment area. The course of action followed was to draw a Value Stream Map (VSM) in order to understand the plants’ current status and serve as a roadmap to build a discrete event simulation model that after its validation, served as a support tool to analyze what-if scenarios. Similarly, a current status analysis of the  shipment/stock area was conducted collecting statistics about potential truckshipments and finally proposing a series of recommendations for its operation. The main proposed solutions to optimize the rolling mill’s cold sector were:(a) Integer programming model to globally optimize the scrap level when cutting the mother beams to customer size beams. (b) Updating pacemaker parameters and (c) Local process time improvements. Concerning the future truck loading, the simulation model was used as a support tool to dimension the transition area between the cranes’ and forklift operations resulting in a 6-9 bundles buffer capacity. Additionally, the current length-based storage policy was found to have competitive objectives so a turnover class-based storage policy is proposed with A, B, C classes which should provide an improved organization of the stock and travel distance of the cranes. The evaluation of the cranes’ performance remains an issue since there are currently no objective measures like, for instance, travelled distance. Optical measuring devices are suggested as one option to have a performance indicator that would help further investigate root cause problems in the shipping/stock area.

Purpose:The primary purpose of the current thesis is to develop a virtual model using discrete event simulation (DES), which aims at supporting the decision-making process regarding automation solution proposals for SMEs.  Method:The research approach is positivism, and it considers quantitative and empirical information. A literature search is conducted to generate a base for obtaining the theory required for the current report to answer the research questions. This search included the trace of relevant and reviewed topics regarding automation, discrete event simulation, and production lines. Then, a scenario simulation is designed and studied based on empiric knowledge and how automation would affect it, followed by a collection of information from the simulation iterations. Findings& Analysis: Two scenarios are presented. One with a fully manually operated assembly line consisting of only human operators and a second scenario, a semi-automated assembly line that includes some robots in specific areas doing specific operations. The two scenarios are simulated to check to what extend the KPI’s and parameters improved between each scenario. The experiment result concludes that by introducing automation elements in the production line, there is an increase in the overall efficiency, throughput rate, and a considerable gap against humans in WIP. Conclusions and recommendations: The results obtained from the experimentation in discrete event simulation software and study from previous research show that discrete event simulation has a significant contribution when considering a decision-making tool's role. Since it allows to understand and study the specific scenario by imitation and try different solutions in the same production system, it also allows studying several indicators from the scenarios to be checked to what extent they could be improved. Delimitations: The current thesis includes several delimitations. First, it focuses only on an operational level. Also, this study consists of a specific type of product with many variants, and finally, there are only two scenarios studied: a fully manual scenario and a semi-automated scenario with the presence of robots.

There is no doubt Medical Devices have a crucial role in hospital processes such as surgeries and therapeutic procedures. Medical devices available in hospitals are of two types; reusable and non-resalable medical devices. Reusable medical devices are washed and sterilized after each use. The process of sterilizing medical devices is performed in the sterilization department. Each medical device travels through a cycle each time it is utilized. It is explicit that any part of the sterilization cycle that delays the process can cause serious problems for hospitals’ performance. The washing step of the sterilization process has been a bottleneck in the system. Thus, optimization approaches can be highly advantageous to improve this bottleneck. The data of the medical devices are usually unknown prior to the scheduling process since the finishing time of the surgeries are not known in advance. Thus, there is no information available on the ready time of medical devices to be sterilized. Due to this factor, to develop applicable solutions, it is critical to consider this problem as an online problem and develop online scheduling methods. In this thesis, we take advantage of mathematical programming and heuristic algorithms to solve both the offline and online settings of the problem. We model the washing step of the sterilization cycle as a scheduling problem. Batch scheduling and bin packing, two well-known optimization approaches, are used for this purpose. Medical devices are batched together first and then scheduled on machines to reduce the total washing time of all medical devices. First, a mathematical model for the offline problem is provided and tested to solve the problem. Then a series of heuristic algorithms are developed using the batch scheduling approach for solving both offline and online problems. Moreover, a special case with divisible job sizes and equal release dates is studied. It was proved that for the strongly divisible sequence the First Fit Increasing algorithm finds the optimal solution, also for the weakly divisible sequence a Dynamic Programming algorithm is developed. Finally, we couple optimization with simulation to test the impact of the optimization of the washing step on the entire sterilization system. Moreover, since the next step of the sterilization cycle, the sterilization step, is very similar to the washing step, we also implement the developed heuristics in this step to evaluate its performance and improve it further. The results show that as long as the washing step is optimized it does not differ which algorithm is used in the sterilization step, thus, the optimization of this step is not necessary.

The Southern Swedish hospital region is the home of nearly 2 million people, inwhich 5,684 individuals were diagnosed by stroke during 2016, according tostatistics from the hospitals in the region. With this large number ofstroke-diagnosed patients across the region, an effective stroke transport policy isinevitably important to provide fast treatment for these patients.In this thesis, we developed an agent-based simulation model for evaluating theperformance of transport logistics policies. We followed the Design ScienceResearch methodology in order to design and develop the model. Using the model,we assessed two transport logistics policies for the Southern Swedish hospitalregion. We used a synthetic set of stroke patients, which we generated using Montecarlo simulation, for the processes of developing the model and assessing our twostroke transport logistics policies.We argue that the assessment of transport logistics policies is important for theability to improve the planning process, for example, when choosing hospital fordiagnosis of patients showing stroke symptoms. The optimization of the strokelogistics process aims to ensure the quality and operational efficiency of the hospitalsector as well as to increase the chance of survival of stroke patients.

The aim of the PhD thesis is to design a methodology for generating a material flow using a simulation meta-model of a mass production process. This methodology is in principle based on the relationship between selected material flow characteristics. Simulation of production and logistics processes has been increasingly used in planning, commissioning and subsequent operational management and optimization of the respective technological operations, in particular in mass production. The first part of the PhD thesis summarizes up-to-date findings in the field of discrete event simulation of material flow, related statistical and mathematical disciplines, but also information technology which enables effective realization of simulation studies. Attention is also paid to significant domestic and international conferences, symposia and interest associations related to simulation of manufacturing processes. The next part of the PhD thesis presents the methodology of reconstruction and generation of material flow using simulation meta-models developed for this purpose. Principles of algorithms used by these meta-models and their possible range of use are demonstrated by simulation experiments carried out. Their description and results are also commented. Special focus is put on the selection of significant material flow characteristics and their mutual relationship. For its evaluation a series of simulation experiments was conducted using a simulation model of a closed queuing system with variable parameters. Revealed interdependence between the selected material flow characteristics is experimentally verified using a detailed simulation model of particular selected mass production system. The conclusion of the PhD thesis summarizes provided findings and, with regard to the designed methodology of reconstruction and generating of material flow, it outlines possible further steps both in research and their practical application.

Large scale systems provide a powerful computing platform for solving large and complex scientific applications. However, the inherent complexity, heterogeneity, wide distribution, and dynamism of the computing environments can lead to performance degradation of the scientific applications executing on these computing systems. Load imbalance arising from a variety of sources such as application, algorithmic, and systemic variations is one of the major contributors to their performance degradation. In general, load balancing is achieved via scheduling. Moreover, frequently occurring resource failures drastically affect the execution of applications running on high performance computing systems. Therefore, the study of deploying support for integrated scheduling and fault-tolerance mechanisms for guaranteeing that applications deployed on computing systems are resilient to failures becomes of paramount importance. Recently, several research initiatives have started to address the issue of resilience. However, the major focus of these efforts was geared more toward achieving system level resilience with less emphasis on achieving resilience at the application level. Therefore, it is increasingly important to extend the concept of resilience to the scheduling techniques at the application level for establishing a holistic approach that addresses the performability of these applications on high performance computing systems. This can be achieved by developing a comprehensive modeling framework that can be used to evaluate the resiliency of such techniques on heterogeneous computing systems for assessing the impact of failures as well as workloads in an integrated way. This dissertation presents an experimental methodology based on discrete event simulation for the analysis and the evaluation of the resilience of scheduling scientific applications on high performance computing systems. With the aid of the methodology a wide class of dependencies existing between application and computing system are captured within a deterministic model for quantifying the performance impact expected from changes in application and system characteristics. Ideally, the results obtained by employing the proposed simulation-based performance prediction framework enabled an introspective design and investigation of scheduling heuristics to reason about how to best fully optimize various often antagonistic objectives, such as minimizing application makespan and maximizing reliability.

Hospitals around the country are struggling to provide timely access to inpatient beds. We use discrete event simulation to study the inpatient admission and discharge processes in US hospitals. Demand for inpatient beds comes from two sources: the Emergency Department (ED) and elective surgeries (NonED). Bed request and discharge rates vary from hour to hour; furthermore, weekday demand is different from weekend demand. We use empirically collected data from national and local (Massachusetts) sources on different-sized community and referral hospitals, demand rates for ED and NonED patients, patient length of stay (LOS), and bed turnover times to calibrate our discrete event simulation model. In our computational experiments, we find that expanding hours of discharge, increasing the number of days elective patients are admitted in a week, and decreasing length of stay all showed statistically significant results in decreasing the average waiting time for patients. We discuss the implications of these results in practice, and list the key limitations of the model.

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