This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.

Public transport network design deals with finding the most efficient network solution among a set of alternatives, that best satisfies the often-conflicting objectives of different network stakeholders like passengers and operators. Simulation-based Optimisation (SBO) is a discipline that solves optimisation problems by combining simulation and optimisation models. The former is used to evaluate the alternative solutions, while the latter searches for the optimal solution among them. A SBO model for designing public transport networks is developed in this dissertation. The context of the research is the MyCiTi Bus Rapid Transit (BRT) network in the City of Cape Town, South Africa. A multi-objective optimisation algorithm known as the Non-dominated Sorting Genetic Algorithm (NSGA-II) is integrated with Activity-based Travel Demand Model (ABTDM) known as the Multi-Agent Transport Simulation (MATSim). The steps taken to achieve the research objectives are first to generate a set of feasible network alternatives. This is achieved by manipulating the existing routes of the MyCiTi BRT with a computer based heuristic algorithm. The process is guided by feasibility conditions which guarantee that each network has routes that are acceptable for public transport operations. MATSim is then used to evaluate the generated alternatives, by simulating the daily plans of travellers on each network. A typical daily plan is a sequential ordering of all the trips made by a commuter within a day. Automated Fare Collection (AFC) data from the MyCiTi BRT was used to create this plan. Lastly, the NSGA-II is used to search for an efficient set of network solutions, also known as a Pareto set or a non-dominated set in the context of Multi-objective Optimisation (MOO). In each generation of the optimisation process, MATSim is used to evaluate the current solution. Hence a suitable encoding scheme is defined to enable a smooth iv translation of the solution between the NSGA-II and MATSim. Since the solution of multi-objective optimisation problems is a set of network solutions, further analysis is done to identify the best compromise solution in the Pareto set. Extensive computational testing of the SBO model has been carried out. The tests involve evaluating the computational performance of the model. The first test measures the repeatability of the model's result. The second computational test considers its performance relative to indicators like the hypervolume and spacing indicators as well as an analysis of the model's Pareto front. Lastly, a benchmarking of the model's performance when compared with other optimisation algorithms is carried out. After testing the so-called Simulation-based Transit Network Design Model (SBTNDM), it is then used to design pubic transport networks for the MyCiTi BRT. Two applications are considered for the model. The first application deals with the public transport performance of the network solutions in the Pareto front obtained from the SBTNDM. In this case study, different transport network indicators are used to measure how each solution performs. In the second scenario, network design is done for the 85th percentile of travel demand on the MyCiTi network over 12 months. The results show that the model can design robust transit networks. The use of simulation as the agency of optimisation of public transport networks represents the main innovation of the work. The approach has not been used for public transport network design to date. The specific contribution of this work is in the improved modelling of public transport user behaviour with Agent-based Simulation (ABS) within a Transit Network Design (TND) framework. This is different from the conventional approaches used in the literature, where static trip-based travel demand models like the four-step model have mostly been used. Another contribution of the work is the development of a robust technique that facilitates the simultaneous optimisation of network routes and their operational frequencies. Future endeavours will focus on extending the network design model to a multi-modal context.

No description available.

No description available.

In order for the companies to be competitive in today’s market, it is vital to adapt quickly to the market trends. The steady shift towards mass customization from mass production has been challenging many industries globally, which demands the use of digital tools and technologies in various areas to improve performance throughout the supply chain processes. One of these areas is short-term scheduling of jobs on the shop floor. Short-term scheduling of jobs plays a very vital role in many production systems. Optimisation of short-term scheduling help the companies in improving their operational Key Performance Indicators (KPIs), thus saving both money and resources. Today’s complex production systems with multiple constraints, system level interactions and the dynamic environment have challenged the traditional static scheduling approaches. These complex production systems require new scheduling approaches which can consider all the dynamics and should be capable of real-time reconfiguring in case of uncertainties in the shop floor. In this thesis, a case study was performed in a steel manufacturing company over the period of five months to validate Discrete Event Simulation (DES) as a tool for short-term scheduling of heavy plates in a dynamic environment. The challenges related to DES for short-term scheduling during model design, development and implement phases were also identified. In addition to this, the requirements to implement DES model for short-term scheduling in a dynamic environment were also discussed. Furthermore, a Systematic Literature Review (SLR) was also conducted to support the empirical findings from the case study. The idea of this study was to generate an optimal schedule by minimizing overall makespan and maximizing resource utilization using DES model. The findings from both SLR and DES model has clearly proven that DES as a digital tool is exceptional for short-term scheduling in a dynamic environment, nevertheless there are still some challenges associated which needs to be investigated further. The same model can also be used for other purposes such as analysing and identifying bottlenecks in the whole production system. / För att företagen ska vara konkurrenskraftiga på dagens marknad är det viktigt att snabbt anpassa sig till marknadstrenderna. Vägen mot mass-anpassning från massproduktion har utmanat många industrier globalt, vilket kräver användning av digitala verktyg och tekniker inom olika områden för att förbättra prestandan under hela leveranskedjans processer. Ett av dessa områden är kortsiktig schemaläggning av arbeten på fabriksgolvet. Kortsiktig schemaläggning av arbeten spelar en mycket viktig roll i många produktionssystem. Optimering av kortsiktig schemaläggning hjälper företagen att förbättra sina operativa nyckeltal, vilket sparar både pengar och resurser. Dagens komplexa produktionssystem med flera begränsningar, systemnivåinteraktioner och den dynamiska miljön har utmanat de traditionella statiska schemaläggningsmetoderna. Dessa komplexa produktionssystem kräver nya schemaläggningsmetoder som kan ta hänsyn till all dynamik och bör ha möjligheten att omkonfigurera i realtid vid osäkerheter på fabriksgolvet. I denna avhandling genomfördes en fallstudie i ett ståltillverkningsföretag under fem månader för att validera Diskret händelsestyrd simulering som verktyg för kortsiktig schemaläggning av grovplåtstillverkning i en dynamisk miljö. Utmaningarna relaterade till Diskret händelsestyrd simulering för kortsiktig schemaläggning under modellens design, utveckling och implementeringsfaser identifierades också. Utöver detta diskuteras också kraven för att implementera Diskret händelsestyrd simulering för kortsiktig schemaläggning i en dynamisk miljö. Dessutom genomfördes en systematisk litteraturstudie för att stödja de empiriska resultaten från fallstudien. Tanken med den här studien var att generera ett optimalt schema genom att minimera den totala schemalängden och maximera resursutnyttjandet med hjälp av Diskret händelsestyrd simuleringsmodellen. Resultaten från både den systematiska litteraturöversynen och Diskreta händelse simuleringsmodellen har tydligt bevisat att Diskret händelse simulering som ett digitalt verktyg är exceptionellt för kortsiktig schemaläggning i en dynamisk miljö även om det fortfarande finns några utmaningar som måste undersökas ytterligare. Samma modell kan också användas för andra ändamål som att analysera och identifiera flaskhalsar i hela produktionssystemet.

Transit system have a large importance in modern urban cities, with urban rail often acting as the central system with it efficient travel time and great capacity. As cities grow in population, so to does the usage of urban rail resulting in increased crowding on the platform and in the trains. Since crowding level is directly correlated to the experience of travel as well as a safety issue, much research has been done to improve it. Currently its common to utilise transit assignment models (TAM) to evaluate and research transit system but for them to work optimally requires weight parameters connected to perceived time spent on the journey. To get the weight parameters for a system requires surveys to be preformed which is costly and not always possible. Therefor its attractive to find these weights through optimisation using available data. Most transit system uses automated fare collection (AFC), which can be used to create origin-destination (OD) data, and automated vehicle location (AVL) together with link-load data. This project aims to develop a simulation-based optimisation (SBO) that automatically finds the weights for a frequency-based assignment model using OD and link-load as input arguments. The SBO will evaluate five different algorithm, genetic algorithm (GA), simulated annealing (SA), Nelder-Mead method (NM), simultaneous perturbation stochastic approximation (SPSA), and Bayesian optimisation (BO), using a fitness model based on KolmogorovSmirnov test. Synthetic data was implemented to evaluate the algorithms where result needed to be within a margin of error of the set weight. No algorithm was however able to converge during the simulation, therefor not optimising the weights to within the margin of error. A longer simulation was evaluated to see if the length needed to reach convergence was to short but achieved the same results. While the cause was not found, the standard deviation of the TAM could be the problem since the deviation was larger than the change of weight parameters achieved. Even if this project could not achieve its objective of developing a SBO method, it can be used for future research and work as a guide on further development on TAM research. / Transportsystem har en stor påverkan i moderna städer, specifikt tunnelbanan som ofta agerar som det centrala systemet med dess snabba transport samt stora kapacitet. Alltmedan städer växer i befolkning så ökar användandet av tunnelbanan vilket resulterar till trängsel både på plattformen och på tåget. Trängsel är väl studerat inom forskningen då den direkt påverkar den upplevda trivseln samt säkerheten på plattformen. Nuförtiden är det vanligt att använda sig av transport modeller för att undersöka och forska om transportsystemet men modellerna kräver viktparametrar kopplade till den uppfattade tiden man har för att fungera. Vanligtvis behöver man utföra undersökningar för att ta reda på vad viktparametrarna är men det är både dyrt och komplicerat. Därför vill man kunna få fram dessa vikter genom att optimera kända data. De flesta transportsystem använder sig av automatiska biljettsystem (AFC), vilket kan användas för att skapa start-stop (OD) data, och automatisk fordonslokalisering (AVL) tillsammans med länk-belastningsdata. Detta projekts syfte är att utveckla en simuleringsbaserad optimering (SBO) som automatiskt hittar vikterna för en frekvensbaserad transportmodell genom att använda OD- och länk-belastningsdata som argument. SBO kommer att undersöka fem olika algoritmer, genetic algorithm (GA), simulated annealing (SA), Nelder-Mead method (NM), simultaneous perturbation stochastic approximation (SPSA), and Bayesian optimisation (BO), tillsammans med en objektfunktion baserad på Kolmogorov-Smirnov testet. Syntetiskt data användes för att utvärdera algoritmerna, där resultatet behövde vara inom en viss marginal av de satta vikterna. Inga algoritmer konvergerade vilket resulterade att deras resultat inte var inom marginalen. Ett längre test var utfört då konvergensen kunde ha skett senare men det blev samma resultat som tidigare. Anledningen kunde inte finnas men sannolikt var det TAM standardavvikelse som var del av felet då den var större än en förändring av viktparametrarna skapade. Även om detta projekt inte kunde uppnå sitt mål kan den användas för fortsatt arbete inom området och vara som guide för framtida utvecklingar.