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 Linear Discrete Method (LDM; SIMMEL 2000; SIMMEL ET AL. 2000) is used to solve the Stochastic Collection Equation (SCE) numerically. Comparisons are made to the Method of Moments (MOM; TzIVION ET AL. 1999) which is suggested as a reference for numerical solutions of the SCE. Simulations for both methods are shown for the GoLOVIN kernel (for which an analytical solution is available) and the hydrodynamic kernel after LONG (1974) as it is used by TZIVION ET AL. (1999). Different bin resolutions are investigated and the simulation times are compared. In addition, LDM simulations using the hydrodynamic kernel after BÖHM (1992b) are presented. The results show that for the GoLOVIN kernel, LDM is slightly closer to the analytic solution than MOM. For the LONG kernel, the low resolution results of LDM and MOM are of similar quality compared to the reference solution. For the BÖHM kernel, only LDM simulations were carried out which show good correspondence between low and high resolution results. / Die lineare diskrete Methode (LDM; SIMMEL 2000; SIMMEL ET AL. 2000) wird dazu benutzt, die Gleichung für stochastisches Einsammeln (stochastic collection equation, SCE) numerisch zu lösen. Dabei werden Vergleiche gezogen zur Methode der Momente (Method of Moments, MOM; TzIVION ET AL. 1999), die als Referenz für numerische Lösungen der SCE vorgeschlagen wurde. Simulationsrechnungen für beide Methoden werden für die Koaleszenzfunktion nach GoLOVIN (für die eine analytische Lösung existiert) und die hydrodynamische Koaleszenzfunktion nach LONG (1974) wie sie von TZIVION ET AL. (1999) verwendet wird, gezeigt. Verschiedene Klassenauflösungen werden untersucht und die Simulationszeiten verglichen. Zusätzlich werden LDM-Simulationen mit der hydrodynamischen Koaleszenzfunktion nach BÖHM (1992b) gezeigt. Die Ergebnisse für die Koaleszenzfunktion nach GoLOVIN zeigen, daß die LDM der analytischen Lösung etwas näher kommt als MOM. Für die Koaleszenzfunktion nach LONG sind die Ergebnisse von LDM und MOM mit niedriger Auflösung von ähnlicher Qualität verglichen mit der Referenzlösung. Für die Koaleszenzfunktion nach BÖHM wurden nur Simulationen mit der LDM durchgeführt, die eine gute Übereinstimmung der Ergebnisse mit niedriger und hoher Auflösung zeigen.

At the intersection of algebraic geometry, number theory, and combinatorics, an interesting problem is counting points on an algebraic curve over a finite field. When specialized to the case of elliptic curves, this question leads to a surprising connection with a particular family of graphs. In this document, we present some of the underlying theory and then summarize recent results concerning the aforementioned relationship between elliptic curves and graphs. A few results are additionally further elucidated by theory that was omitted in their original presentation.

In 1961, P. W. Kasteleyn provided a baffling-looking solution to an apparently simple tiling problem: how many ways are there to tile a rectangular region with dominos? We examine his proof, simplifying and clarifying it into this nearly self-contained work.

Even if an encryption algorithm is mathematically strong, humans inevitably make for a weak link in most security protocols. A sufficiently threatening adversary will typically be able to force people to reveal their encrypted data. Methods of deniable encryption seek to mend this vulnerability by allowing for decryption to alternate data which is plausible but not sensitive. Existing schemes which allow for deniable encryption are best suited for use by parties who wish to communicate with one another. They are not, however, ideal for personal data storage. This paper develops a plausibly-deniable encryption system for use with personal data storage, such as hard drive encryption. This is accomplished by narrowing the encryption algorithm’s message space, allowing different plausible plaintexts to correspond to one another under different encryption keys.

This research establishes the feasibility and potential utility of a software mechanism which employs artificial intelligence techniques to enhance the capabilities of standard discrete event simulators. As background, current methods of integrating artificial intelligence with simulation and relevant research are briefly reviewed.

Healthcare systems are traditionally characterised by complexity and heterogeneity. With the continuous increase in size and shrinkage of available resources, the healthcare sector faces the challenge of delivering high quality services with fewer resources. Healthcare organisations cannot be seen in isolation since the services of one such affects the performance of other healthcare organisations. Efficient management and forward planning, not only locally but rather across the whole system, could support healthcare sector to overcome the challenges. An example of closely interwoven organisations within the healthcare sector is the emergency medical services (EMS). EMS operate in a region and usually consist of one ambulance service and the available accident and emergency (A&E) departments within the coverage area. EMS provide, mainly, pre-hospital treatment and transport to the appropriate A&E units. The life-critical nature of EMS demands continuous systems improvement practices. Modelling and Simulation (M&S) has been used to analyse either the ambulance services or the A&E departments. However, the size and complexity of EMS systems constitute the conventional M&S techniques inadequate to model the system as a whole. This research adopts the approach of distributed simulation to model all the EMS components as individual and composable simulations that are able to run as standalone simulation, as well as federates in a distributed simulation (DS) model. Moreover, the hybrid approach connects agent-based simulation (ABS) and discrete event simulation (DES) models in order to accommodate the heterogeneity of the EMS components. The proposed FIELDS Framework for Integrated EMS Large-scale Distributed Simulation supports the re-use of existing, heterogeneous models that can be linked with the High Level Architecture (HLA) protocol for distributed simulation in order to compose large-scale simulation models. Based on FIELDS, a prototype ABS-DES distributed simulation EMS model was developed based on the London EMS. Experiments were conducted with the model and the system was tested in terms of performance and scalability measures to assess the feasibility of the proposed approach. The yielded results indicate that it is feasible to develop hybrid DS models of EMS that enables holistic analysis of the system and support model re-use. The main contributions of this thesis is a distributed simulation methodology that derived along the process of conducting this project, the FIELDS framework for hybrid EMS distributed simulation studies that support re-use of existing simulation models, and a prototype distributed simulation model that can be potentially used as a tool for EMS analysis and improvement.

Spatial resource allocation is an important consideration in shipbuilding and large-scale manufacturing industries. Spatial scheduling problems (SSP) involve the non-overlapping arrangement of jobs within a limited physical workspace such that some scheduling objective is optimized. Since jobs are heavy and occupy large areas, they cannot be moved once set up, requiring that the same contiguous units of space be assigned throughout the duration of their processing time. This adds an additional level of complexity to the general scheduling problem, due to which solving large instances of the problem becomes computationally intractable. The aim of this study is to gain a deeper understanding of the relationship between the spatial and temporal components of the problem. We exploit these acquired insights on problem characteristics to aid in devising solution procedures that perform well in practice. Much of the literature on SSP focuses on the objective of minimizing the makespan of the schedule. We concentrate our efforts towards the minimum sum of completion times objective and state several interesting results encountered in the pursuit of developing fast and reliable solution methods for this problem. Specifically, we develop mixed-integer programming models that identify groups of jobs (batches) that can be scheduled simultaneously. We identify scenarios where batching is useful and ones where batching jobs provides a solution with a worse objective function value. We present computational analysis on large instances and prove an approximation factor on the performance of this method, under certain conditions. We also provide greedy and list-scheduling heuristics for the problem and compare their objectives with the optimal solution. Based on the instances we tested for both batching and list-scheduling approaches, our assessment is that scheduling jobs similar in processing times within the same space yields good solutions. If processing times are sufficiently different, then grouping jobs together, although seemingly makes a more effective use of the space, does not necessarily result in a lower sum of completion times.

The famous Four Color Theorem states that any planar graph can be properly colored using at most four colors. However, if we want to properly color the square of a planar graph (or alternatively, color the graph using distinct colors on vertices at distance up to two from each other), we will always require at least \Delta + 1 colors, where \Delta is the maximum degree in the graph. For all \Delta, Wegner constructed planar graphs (even without 3-cycles) that require about \frac{3}{2} \Delta colors for such a coloring. To prove a stronger upper bound, we consider only planar graphs that contain no 4-cycles and no 5-cycles (but which may contain 3-cycles). Zhu, Lu, Wang, and Chen showed that for a graph G in this class with \Delta \ge 9, we can color G^2 using no more than \Delta + 5 colors. In this thesis we improve this result, showing that for a planar graph G with maximum degree \Delta \ge 32 having no 4-cycles and no 5-cycles, at most \Delta + 3 colors are needed to properly color G^2. Our approach uses the discharging method, and the result extends to list-coloring and other related coloring concepts as well.

The Probabilistic Method was primarily used in Combinatorics and pioneered by Erdös Pai, better known to Westerners as Paul Erdos in the 1950s. The probabilistic method is a powerful tool for solving many problems in discrete mathematics, combinatorics and also in graph .theory. It is also very useful to solve problems in number theory, combinatorial geometry, linear algebra and real analysis. More recently, it has been applied in the development of efficient algorithms and in the study of various computational problems.Broadly, the probabilistic method is somewhat opposite of the extremal graph theory. Instead of considering how a graph can behave in the extreme, we consider how a collection of graphs behave on 'average' where by we can formulate a probability space. The method allows one to prove the existence of a structure with particular properties by defining an appropriate probability space of structures and show that the desired properties hold in the space with positive probability.(please see PDF for complete abstract)