The planning of an Aftermarket Supply Chain is a very complex task. This is due to an unpredictable demand which is driven by the need for maintenance and repair. This drive translates to a high variety of lead times, a large number of stock-keeping units (SKUs) and the capacity to deliver spare parts during its full lifecycle. With all these complexities in place, optimizing and parametrizing the planning process is a difficult and time-consuming task. Moreover, the current optimization tool focuses only on one node (each warehouse individually) of the whole Supply Chain, without considering the information such as inventory levels of the other nodes. Hence, the Supply Chain is not completely connected, making it difficult to get a better understanding of the system performance to identify cost draining areas. This leads to capital being tied up in the upper stream of the Supply Chain and later adding unnecessary costs like high inventory costs, rush freight costs, return or scrapping cost. In this study, Discrete Event Simulation (DES) is explored as an additional optimization tool that could analyse and improve the performance of the whole Supply Chain. To do that, the functioning of a node is modelled by replicating the logics behind the flow of material, which includes analysing some manual workflows which are currently present. In Addition, all the information needed from the orders, order lines and parts are mapped. The later part of the study aims to connect all the nodes to form a whole overview of the Supply Chain and further perform optimizations globally.  As an outcome, Multi-Echelon Inventory Optimization has been performed on the whole Supply Chain after connecting all the nodes and thus getting an overview. Furthermore, the impact of different parameters has been studied on the whole model to understand the sensitivity of parameters such as variations in lead time and demand. Finally, different what-if scenarios such as COVID and problems with delay in suppliers were studied, which could help understand the impact of unforeseen situations. / Planeringen av en eftermarknadskedja är en mycket komplex uppgift. Detta beror på en oförutsägbar efterfrågan som drivs av behovet av underhåll och reparation. Enheten översätter till många olika ledtider, ett stort antal lagerhållningsenheter (SKU) och kapacitet att leverera reservdelar under hela dess livscykel. Med alla dessa komplexiteter på plats är optimering och parametrering av planeringsprocessen en svår och tidskrävande uppgift. Dessutom fokuserar det nuvarande optimeringsverktyget bara på en nod (varje lager separat) i hela leveranskedjan utan att beakta informationen som lagernivåerna för de andra noderna. Därför är försörjningskedjan inte helt ansluten, vilket gör det svårt att få en bättre förståelse för systemets prestanda för att identifiera kostnadsavtappningsområden. Detta leder till att kapital binds i den övre strömmen i försörjningskedjan och senare lägger till onödiga kostnader som höga lagerkostnader, snabba fraktkostnader, retur- eller skrotningskostnader. I denna studie undersöks Discrete Event Simulation (DES) som ett ytterligare optimeringsverktyg som kan analysera och förbättra prestanda för hela försörjningskedjan. För att göra det modelleras en nods funktion genom att replikera logiken bakom materialflödet, vilket inkluderar analys av några manuella arbetsflöden som för närvarande finns. Dessutom kartläggs all information som behövs från beställningar, orderrader och delar. Den senare delen av studien syftar till att ansluta alla noder för att bilda en hel översikt över försörjningskedjan och ytterligare utföra optimeringar globalt. Som ett resultat har Multi-Echelon Lageroptimering utförts i hela försörjningskedjan efter att alla noder har anslutits och därmed fått en översikt. Dessutom har effekterna av olika parametrar studerats på hela modellen för att förstå känsligheten hos parametrar som variationer i ledtid och efterfrågan. Slutligen studerades olika tänkbara scenarier som COVID och problem med förseningar hos leverantörer, vilket kan hjälpa till att förstå effekterna av oförutsedda situationer.

The report presents a study of the operations at Rollmek AB. Rollmek is a company that manufactures various components for Panthera wheelchairs including backrests, chassis and leg rests. The thesis aims to develop various improvement proposals for the case company's painting department with regard to increasing production capacity. To achieve the goal, a simulation model has been constructed in the simulation program FACTS Analyzer, which reflects the department in question. The simulation model is based on three different variants with five different colours. For the construction of simulation models, data collected via various qualitative and quantitative research methods, including time studies, observations, and interviews. Improvement proposals that were developed were implemented in the simulation model and experimented based on the project's requirements. The study's results have led to five specific suggestions for improvement. Through the use of simulation models, data has been generated on the number of units produced and units produced/hour for the three product variations with associated color variations. With the help of these improvement suggestions, the company could implement them in production to increase its capacity to meet future demand.

This thesis aims to fill the gaps in the literature of the theory of constraints (TOC) in publicly funded health systems. While TOC seems to be a natural fit for this resource-constrained environment, there are still no reported application of TOC’s drum-buffer-rope tool and inadequate customizations with regards to defining system-wide goal and performance measures. The “Drum-Buffer-Rope for an Outpatient Cancer Facility” chapter is a real world case study exploring the usefulness of TOC’s drum-buffer-rope scheduling technique in a publicly funded outpatient cancer facility. With the use of a discrete event simulation model populated with historical data, the drum-buffer-rope scheduling policy is compared against “high constraint utilization” and “low wait time” scenarios. Drum-buffer-rope proved to be an effective mechanism in balancing the inherent tradeoff between the two performance measures of instances of delayed treatment and average patient wait time. To find the appropriate level of compromise in one performance measure in favor of the other, the linkage of these measures to system-wide performance measures are proposed. In the “Theory of Constraints’ Performance Measures for Publicly Funded Health Systems” chapter, a system dynamics representation of the classical TOC’s system-wide goal and performance measures for publicly traded for-profit companies is developed, which forms the basis for developing a similar model for publicly funded health systems. The model is then expanded to include some of the factors that affect system performance, providing a framework to apply TOC’s process of ongoing improvement in publicly funded health systems. The “Connecting Low-Level Performance Measures to the Goal” chapter attempts to provide a framework to link the low-level performance measures with system-wide performance measures. It is claimed that until such a linkage is adequately established, TOC has not been fully transferred to publicly funded health systems.

The Swedish health care system plays a vital role in satisfying the citizens’ demands for quality health care services. To deliver the right services in time in a hospital, an efficient admission plan is required, but this can be difficult to achieve. The Department of Pulmonary Medicine at the University Hospital in Linköping needs a better admission plan for their patients. In the department, the patient demand does not match the capacity, which leads to overcrowding at the ward. The aim of this thesis is to improve the admission plan of patients for the ward at the Department of Pulmonary Medicine by using discrete event simulation. To fulfil the aim, a simulation study is performed to understand how the system is working, where the problems emerged and how to prevent overcrowding. Different experiments are performed to check what could improve the admission plan at the ward. The results from this study shows that an improvement of the admission plan could be reached by better cooperation between involved units, improved documentation at the Department, a queue system of patients based on medical priorities and changed number of care beds. These solutions can prevent overcrowding and deliver health care services in time.

Discrete-event simulations are increasingly being used to solve problems and to aid in decision making which are proving useful in the manufacturing industry. The main aim for this thesis was to compare the current production line and how implementing changes for a future state as a supporting basis for making the decision. The theoretical framework focused on the Lean philosophy merged with simulation-based methods. The simulation model was build using the collected data. A time study was conducted in order to verify the process and setup times since these were only estimated at the time. Two simulation models were built for the current production line and the future state. The future state was based on the current one since no changes in the process and setup times would be made during the two simulation-model. Experiments were then done to compare the different states, one with batch and the other single-piece flow. The parameters were set on equal terms and the compared values were throughput (TH), lead-time (LT) and work in process (WIP). The conclusion drawn from the results is that the future state would be more efficient.

O objetivo deste trabalho é propor um procedimento híbrido para a solução do problema de formação de células de manufatura com réplicas de máquinas. Constrói-se um modelo matemático de otimização multiobjetivo cujos valores das funções-objetivo são obtidos por meio da execução de um modelo de simulação de eventos discretos, o qual representa um sistema de manufatura celular. Em seguida, geram-se soluções eficientes segundo o conceito de otimalidade de Pareto através de um processo de busca por valores ótimos executado por um algoritmo genético. Três funções-objetivo conflitantes são consideradas: inventário em processo, movimentação intercelular e investimento total em máquinas. Um algoritmo de análise de agrupamento é utilizado para a redução do conjunto final de soluções. A eficácia do procedimento é avaliada mediante a aplicação a dois casos da literatura. Os resultados obtidos são analisados e comentados. Conclui-se, por fim, que o procedimento é capaz de gerar um conjunto de configurações sub-ótimas equivalentes para as células de manufatura, representando aproximadamente os trade-offs entre as três funções-objetivo. / The purpose of this work is to propose a hybrid procedure for solving the manufacturing cell formation problem. A multiobjective optimization model is built whose objective function values are realized by running a discrete-event simulation model, which represents a cellular manufacturing system. Thereafter, efficient solutions are generated following the Pareto optimality concept through a search for optimum values carried out by a genetic algorithm. Three conflicting objective functions are considered, namely, work-in-process, intercell moves and total machine investment. A clustering algorithm is applied to the final solution set so as to reduce it. The procedure efficacy is evaluated via its application to two cases from the literature. The obtained results are analyzed and commented. Finally, it is concluded that the procedure is capable of generating a set of equivalent sub-optimal manufacturing cell configurations, representing approximately the trade-offs betvveen the objective functions adopted.

O objetivo deste trabalho é propor um procedimento híbrido para a solução do problema de formação de células de manufatura com réplicas de máquinas. Constrói-se um modelo matemático de otimização multiobjetivo cujos valores das funções-objetivo são obtidos por meio da execução de um modelo de simulação de eventos discretos, o qual representa um sistema de manufatura celular. Em seguida, geram-se soluções eficientes segundo o conceito de otimalidade de Pareto através de um processo de busca por valores ótimos executado por um algoritmo genético. Três funções-objetivo conflitantes são consideradas: inventário em processo, movimentação intercelular e investimento total em máquinas. Um algoritmo de análise de agrupamento é utilizado para a redução do conjunto final de soluções. A eficácia do procedimento é avaliada mediante a aplicação a dois casos da literatura. Os resultados obtidos são analisados e comentados. Conclui-se, por fim, que o procedimento é capaz de gerar um conjunto de configurações sub-ótimas equivalentes para as células de manufatura, representando aproximadamente os trade-offs entre as três funções-objetivo. / The purpose of this work is to propose a hybrid procedure for solving the manufacturing cell formation problem. A multiobjective optimization model is built whose objective function values are realized by running a discrete-event simulation model, which represents a cellular manufacturing system. Thereafter, efficient solutions are generated following the Pareto optimality concept through a search for optimum values carried out by a genetic algorithm. Three conflicting objective functions are considered, namely, work-in-process, intercell moves and total machine investment. A clustering algorithm is applied to the final solution set so as to reduce it. The procedure efficacy is evaluated via its application to two cases from the literature. The obtained results are analyzed and commented. Finally, it is concluded that the procedure is capable of generating a set of equivalent sub-optimal manufacturing cell configurations, representing approximately the trade-offs betvveen the objective functions adopted.

In this study, an approach is proposed to model and simulate the systems engineering process of design projects. One of the main aims is to model the systems engineering process, treating the process itself as a complex system. A conceptual model is developed as a result of a two-phase survey conducted with systems engineers. The conceptual model includes two levels of activity networks. Each first level systems engineering activity has its own network of second level activities. The model is then implemented in object oriented modeling language, namely SysML, using block definition diagrams and activity diagrams. Another aim is to generate a discrete event simulation model of the process for performance evaluation. For this purpose the SysML model is transformed to an Arena model using an Excel interface and VBA codes. Three deterministic and three stochastic cases are created to represent systems engineering process alternatives, which originate from the same conceptual model but possess different activity durations, resource availabilities and resource requirements. The scale of the project and the effect of uncertainty in activity durations are also considered. The proposed approach is applied to each of these six cases, developing the SysML models, transforming them to Arena models, and running the simulations. Project duration and resource utilization results are reported for these cases.

During development of a complex system, feasibility initially overshadows other concerns, in some cases leading to a design which may not be viable long-term. In particular for the case of Reusable Launch Vehicles, Operations&Maintenance comprises the majority of the vehicle's LCC, whose stochastic nature precludes direct analysis. Through the use of simulation, probabilistic methods can however provide estimates on the economic behavior of such a system as it evolves over time. Here the problem of operations optimization is examined through the use of discrete event simulation. The resulting tool built from the lessons learned in the literature review simulates a RLV or fleet of vehicles undergoing maintenance and the maintenance sites it/they visit as the campaign evolves over a period of time. The goal of this work is to develop a method for uncovering an optimal operations scheme by investigating the effect of maintenance technician skillset distributions on important metrics such as the achievable annual flight rate and maintenance man hours spent on each vehicle per flight. Using these metrics, the availability of technicians for each subsystem is optimized to levels which produce the greatest revenue from flights and minimum expenditure from maintenance.

VSM, Value stream mapping, V2SM, virtual value stream mapping, lean, lean manufacturing, DES, discrete event simulation, Tecnomatix Plant Simulation