A presente pesquisa aborda o problema de dimensionamento de frota de navios rebocadores do tipo AHTS, que são utilizados essencialmente nas tarefas de operações de apoio à exploração e produção de petróleo offshore (em alto mar). Essas atividades se caracterizam pela requisição simultânea de múltiplos navios de classes diferentes, e possuem parâmetros como: compatibilidade de classes de navios com as tarefas, duração em dias, local de execução e instante desejado de atendimento. Para representar este problema foi desenvolvido um modelo de simulação com parâmetros estocásticos, cuja programação é orientada para minimização dos custos totais da operação, que englobam custos fixos, custos de penalidade por atraso no atendimento das tarefas, e penalidade por falta de cumprimento de tarefas. A abordagem de solução do modelo é a busca exaustiva onde são comparados cenários de simulação de eventos discretos. Adicionalmente, foram comparadas 2 modos de escolhas de tarefas na fila de tarefas, o primeiro é o modo FIFO (First In First Out), o segundo modo é a priorização de tarefas com maior custo de penalidade associado para o dimensionamento de frota. / This research addresses a fleet sizing problem of anchor and handling and tug supply vessels (AHTS), which support the exploration and production of oil at the sea. The support activities are characterized by simultaneous request of multiple vessels of one or more classes. Other characteristics of the research problem are:: the compatibility between vessels and tasks, task duration (in days), a place of execution the task and a desired instant to be attended. A simulation model with stochastic parameters was developed to represent this problem, aiming to minimize the total operational cost that includes fixed costs,penalty costs if tasks are delayed and penalty costs with not completed tasks. The strategy to solve this problem was the exhausted search through discrete-event simulation. Aditionally, 2 methods of approach for the queue were analyzed: the first one is the FIFO (First In First Out) and the second one is the priority according the highest penalty cost to size the fleet.

A key task of managing an inbound call center is in estimating its performance and consequently plan its capacity, which can be considered a complex task since several system variables are stochastic. These issues are highly crucial for certain time-sensitive services, such as emergency call services. Waiting times affect the service quality of call centers in general, but various customers may place different waiting time expectancies depending on the need. Call center managers struggle to find the relationship between these expectations to their strategical, tactical and operational issues. They are assisted by queueing models that approximate the outcome. Simple setups use analytical approximations while a network of multi-skilled agents serving several customer classes is dependent on computer simulations. Regardless of simple or complex setups, models assume that the system components are homogenous, that the components have some parametric distribution, and that they remain the same regardless of the setup. Human resource and marketing research show that such status quo assumptions are not highly reliable. As an example, customer experience is often affected by the skill of the agent, and agents themselves are affected by their workload and duties, which inter alia affect their efficiency. This thesis aim to assist the Swedish emergency call center with a strategical issue, which require detection of some causalities in the set of system components. The overall aim is to design a simulation model, but such model requires a lot of detailed system knowledge, which itself adds to the knowledge gap in the research field. Findings that contribute to the scientific knowledge body include the burst model that addresses some of the non-stationarity of call arrivals, since some rapid rate increments derives from a latent emergency event. Other contributions are the introduction of stochastic agent behavior, which increases the uncertainty in queueing models; and the service time relationship to geographical distance. The latter may involve general evidence on how area-specific understanding and cultural differences affect the quality of service. This is important for organizations that consider off-shoring or outsourcing their call center service. These findings, along with several undiscovered and unknown influencers, are needed in order to design a reliable simulation model. However, the proposed model in this study cannot be rejected, in terms of waiting time replication. This robust model allowed traffic routing strategies to be evaluated and also assisted managers of the emergency call center into a strategical shift in the late 2015. / <p>Vid tidpunkten för framläggningen av avhandlingen var följande delarbeten opublicerade: delarbete 1 och 3 inskickat.</p><p>At the time of the defence the following papers were unpublished: paper 1 and 3 submitted.</p>

Manufacturing systems have developed both physically and technologically, allowing production of innovative new products in a shorter lead time, to meet the 21st century market demand. Flexible flow lines for instance use flexible entities to generate multiple product variants using the same routing. However, the variability within the flow line is asynchronous and stochastic, causing disruptions to the throughput rate. Current autonomous variability control approaches decentralise the autonomous decision allowing quick response in a dynamic environment. However, they have limitations, e.g., uncertainty that the decision is globally optimal and applicability to limited decisions. This research presents a novel formula-based autonomous control method centered on an empirical study of the effect of stochastic variability on the performance of flexible human-dependent serial flow lines. At the process level, normal distribution was used and generic nonlinear terms were then derived to represent the asynchronous variability at the flow line level. These terms were shortlisted based on their impact on the throughput rate and used to develop the formula using data mining techniques. The developed standalone formulas for the throughput rate of synchronous and asynchronous human-dependent flow lines gave steady and accurate results, higher than closest rivals, across a wide range of test data sets. Validation with continuous data from a real-world case study gave a mean absolute percentage error of 5%. The formula-based autonomous control method quantifies the impact of changes in decision variables, e.g., routing, arrival rate, etc., on the global delivery performance target, i.e., throughput, and recommends the optimal decisions independent of the performance measures of the current state. This approach gives robust decisions using pre-identified relationships and targets a wider range of decision variables. The performance of the developed autonomous control method was successfully validated for process, routing and product decisions using a standard 3x3 flexible flow line model and the real-world case study. The method was able to consistently reach the optimal decisions that improve local and global performance targets, i.e., throughput, queues and utilisation efficiency, for static and dynamic situations. For the case of parallel processing which the formula cannot handle, a hybrid autonomous control method, integrating the formula-based and an existing autonomous control method, i.e., QLE, was developed and validated.

Inventory management under uncertainty is a widely researched field and many different types of inventory models have been used to address inventory problems in practice [1, 10, 11, 26, 50, 35]. However, there is a lack of published studies focusing on inventory planning in environments, such as the military, that are characterised by uncertainty as a result of extreme events. A critical area in military decision support is inventory management. Planning for stock levels in particular can be a daunting task, due to the uncertainty associated with the future. The military is typically an environment where improbable events can have massive impacts on operations; and the availability of the correct amount of stock can enhance the responsiveness, efficiency, and preparedness of the military, and ultimately save human lives. On the other hand, excessive stock - especially ammunition - can result in huge monetary losses through damages, stock degradation, and stock obsolescence. Excessive ammunition also poses a risk to public safety, and can ultimately challenge a country's ability to control the use of force. It is therefore very important to provide proper attention to determining the required stock levels during military inventory management. This dissertation aims, therefore, to develop a reliable decision support tool that can assist with inventory management in the military. To achieve this, a mixed multi-objective mathematical model is used that attempts to minimise cost, shortages, and stock while incorporating demand uncertainty by means of probability distributions and fuzzy numbers. The model considers three different scenarios, and determines the minimum required stock level and the best order quantity for three different stock categories, for a single ammunition item. The model is converted into its crisp, non-fuzzy, and deterministic counterpart first by transforming the fuzzy constraints into their crisp versions and then deriving the deterministic model of the crisp recourse stochastic model. The corresponding crisp, deterministic model is then solved using exact branch-and-bound embedded in the LINGO 10.0 optimisation software package and the reliability of the solutions in different scenarios is tested by means of discrete event simulation. The reliability of the model is then compared with the reliabilities of the well known (r;Q) and (s; S) inventory models in the literature. The comparison indicates that the mixed model proposed in this dissertation is more reliable in extreme scenarios than the (r;Q) and (s; S) inventory models in the literature. A sensitivity analysis is then performed and results indicate that the model yields reliable solutions with a reliability that varies between 74.54% and 100%, depending on the scenario investigated. The lower reliability is during the high demand scenario, this is caused by the ability of the inventory model to prioritise different scenarios based on their estimated possibility to ensure that stock levels are not unneccessary escalated for highly improbable events. It can be concluded that the proposed mixed multi-objective mathematical model that aims to minimise inventory cost, surplus stock, and shortages is a reliable inventory decision support model for the uncertain military environment. / Dissertation (MEng)--University of Pretoria, 2011. / Industrial and Systems Engineering / unrestricted

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In today’s competitive market, a company will not succeed unless they stand out in other ways than pure benefits with its products. This can be reached by in some way altering the process currently in place. One way, is by introducing process innovation. Advantages related to the adoption of process innovation has been found in literature, for example by increase competitiveness, increase productivity, and increase plant visibility. However, process innovation evokes uncertainty. Discrete Event Simulation (DES) models has in previous research been suggested as a tool to reduce uncertainty at manufacturing companies when they are undergoing changes. However, the study of change in process innovation setting has been largely ignored. By acknowledging this gap in current research, the aim of this study is to investigate whether the use of DES models are able to reduce uncertainties in process innovation. The study is guided by three research questions: 1. What are the characteristics of process innovation introduction in a production process context? 2. How is the production process at manufacturing companies affected by process innovation related uncertainties? 3. How can the usage of DES contribute to reduction of uncertainties during the introduction of process innovation at manufacturing companies? In order to answer these questions, a research methodology consisting of a literature review and a case study including the usage of DES were applied. In this thesis the case study is conducted at a manufacturing company, presented with the goal of making a modification in the production. This is done in an attempt to make it more environmental friendly while also establish a competitive edge over the rivals. To reach this, an implementation of a process innovation technology is under planning, but introducing something new creates numerous uncertainties. To be able to implement this process innovation, uncertainty reduction is crucial. By identify literature within the field, and compare with findings from interviews and workshops at the studied company, process innovation characteristics and how process innovation evokes uncertainties were identified. From the usage DES in this thesis, uncertainties were reduced, partly reduced and identified while some uncertainties remain unresolved. Moreover, the findings point to the creation of the simulation model working as a visualisation of the current production and the possible future, which generates a discussion platform for all stakeholders involved. / I den nuvarande konkurrenskraftiga marknaden har ett företag många utmaningar ifall de vill lyckas. Det räcker nämligen inte längre med att ha bra produkter utan de måste också förbättras på andra sätt. Ett sätt att uppnå detta på, är att genomföra förändringar i den nuvarande produktionen, en metod för detta är introducera en processinnovation på företaget. Under detta arbete har fördelarna relaterade till processinnovation upptäckts i befintlig litteratur, till exempel genom att ökad konkurrentskraftighet, produktivitet och synlighet för fabriken. Dessvärre framkallar implementeringen av en processinnovation osäkerheter. Diskret händelse simulering (DES) modeller har i tidigare forskning föreslagits som ett verktyg för at minska osäkerheter i tillverkningsföretag, medan de planerar att genomgår en förändring. Forskning om hur simulering hanterar fabriker som genomgår en processinnovation har i hög grad ignorerats. De här studien har för avsikt att undersöka just det området där nuvarande forskning brister, nämligen om ifall DES modeller kan minska osäkerheter i processinnovationer. Tre forskningsfrågor har tagits fram för att styra arbetet: 1. Vilka kännetecken har introduktionen av processinnovation i en produktionsprocess kontext? 2. Hur påverkas produktionsprocess hos tillverkande företag av de osäkerheter som processinnovation medför? 3. Hur kan DES användas för att bidra till minskandet av osäkerheter i tillverkande företag som introducerar processinnovation? För att besvara dessa frågor genomfördes an litteraturstudie och en fallstudie som innehöll simulering. Fallstudien som utfördes på ett tillverkningsföretag som är i planeringsstadiet för att införa en processinnovation. Innovationen har för avsikt att göra produktionen mer miljövänlig och samtidigt skapa en fördel över konkurrenterna. Nuvarande planering är fylld av osäkerheter eftersom tillägget av någonting nytt alltid gör det. Därför är reduceringen av osäkerheter avgörande för att en implementering ska kunna genomföras Genom att identifiera forskning inom området, och jämföra den med resultat från företagsrelaterade intervjuer och workshops, identifierade kännetecken på processinnovation och hur processinnovation skapar osäkerheter. Genom att använda DES i examensarbetet, minskades antalet osäkerheter, till fullo och delvis, och nya osäkerheter identifierades. Dessutom visar resultat på studien att simulering kan användas som ett visualiseringsverktyg för att skapa en diskussionsplattform angående framtida förändringar i produktionen.

A master thesis within mechanical engineering performed by two student has been conducted at Scania in Oskarshamn. The purpose has been to investigate if Discrete Event Simulation using ExtendSim can be applied to increase Scanias assembly productivity. The projectiles was to investigate how the buffer systems could be managed by vary the amount of buffers and their transport speed. The assembly line takt times with regard of their availability was also investigated. The method of approach was to build a simulation model to gaining valid decision making information regarding these aspects. Process stop data was extracted and imported to ExtendSim where the reliability library was used to generate shutdowns. Comparing 24 sets over 100 runs to each other a median standard deviation of 0,91 % was achieved. Comparing the total amount of assembled cabs over a time period of five weeks with the real timedata a difference of 4,77 % was achieved. A difference of 1,85 % in total amount of shutdown time was also achieved for the same conditions. The biggest effect of varying buffer spaces was for system 6A. An increasement of up to 20 more assembled cabs over a time period of five weeks could then be achieved. By increasing all the buffertransports speeds by 40 % up to 20 more assembled cabs over a time period of ve weeks could be achieved. A push and pull system was also investigated where the push generated the best results. A 22 hour decreasement of total shutdown time and an increasement of 113 more assembled cabs over a time period of ve weeks could be achieved.

Newsvendor problems in Operations Research predict the optimal inventory levels necessary to meet uncertain demands. This thesis examines an extended version of a single period multi-product newsvendor problem known as the ice cream vendor problem. In the ice cream vendor problem, there are two products – ice cream and hot chocolate – which may be substituted for one another if the outside temperature is no too hot or not too cold. In particular, the ice cream vendor problem is a data-driven extension of the conventional newsvendor problem which does not require the assumption of a specific demand distribution, thus allowing the demand for ice cream and hot chocolate respectively to be temperature dependent. Using Discrete Event Simulation, we first simulate a real-world scenario of an ice cream vendor problem via a demand whose expected value is a function of temperature. A sample average approximation technique is subsequently used to transform the stochastic newsvendor program into a feature-driven linear program based on the exogenous factors of probability of rainfall and temperature. The resulting problem is a multi-product newsvendor linear program with L1-regularization. The solution to this problem yields the expected cost to the ice cream vendor as well as the optimal order quantities for ice cream and hot chocolate, respectively.

This thesis assesses the potential of using computer simulation to aid existing lean manufacturing training methods such as lecture and live simulation. An investigation of this possibility was carried out in conjunction with UMEP's Lean 101 class. In the study, two experimental computer simulation models demonstrating the push and pull production scenarios were constructed using ProModel software. Simulation models were equipped with a Visual Basic interface to aid trainees to manipulate the model via ActiveX. Constructed computer simulation was compared with live simulation to answer these research questions: 1. Was computer simulation able to teach additional lean concepts not covered in live simulations? 2. Was training time less for trainees going through a computer simulation than for those going through a live simulation? 3. Was a computer simulation quicker and easier to set up than a live simulation for trainers? 4. Did computer simulation achieve comparable educational objectives as live simulation? Objective measurements for first three questions were positive and conclusive. For the fourth one, a survey was conducted among trainees of a treatment group (computer simulation only) and a control group (live simulation only) to collect responses. Statistical analysis of the subjective responses indicated the computer simulation aided the trainees to learn and implement lean manufacturing, but was not as effective as live simulation. Holistically, these results did not warrant the complete changeover from live simulation to computer simulation. Yet, a combined implementation of computer simulation and live simulation was proposed to reap the benefits from the best of both approaches.

This thesis is focused on using discrete event simulation in a large plant in order to improve the control logic of the production department. Specifically it’s about the factory ŠKODA AUTO a.s. located in Mladá Boleslav and solved production node is the paint shop area. The chapters of this thesis are focused on used conveyor technology, basics of discrete event simulation, the subsequent development of a virtual model, design of simulation experiments and concept of a new solution. At the end of this work evaluation of proposed solution with comparison to the original solution is included.