Solving dynamic optimisation problems by combining evolutionary algorithms with KD-tree

In this paper we propose a novel evolutionary algorithm that is able to adaptively separate the explored and unexplored areas to facilitate detecting changes and tracking the moving optima. The algorithm divides the search space into multiple regions, each covers one basin of attraction in the search space and tracks the corresponding moving optimum. A simple mechanism was used to estimate the basin of attraction for each found optimum, and a special data structure named KD-Tree was used to memorise the searched areas to speed up the search process. Experimental results show that the algorithm is competitive, especially against those that consider change detection an important task in dynamic optimisation. Compared to existing multi-population algorithms, the new algorithm also offers less computational complexity in term of identifying the appropriate sub-population/region for each individual

Red Button and Yellow Button: Usable Security for Lost Security Tokens

Currently, losing a security token places the user in a dilemma: reporting the loss as soon as it is discovered involves a significant burden which is usually overkill in the common case that the token is later found behind a sofa. Not reporting the loss, on the other hand, puts the security of the protected account at risk and potentially leaves the user liable. We propose a simple architectural solution with wide applicability that allows the user to reap the security benefit of reporting the loss early, but without paying the corresponding usability penalty if the event was later discovered to be a false alarm.The authors with a Cambridge affiliation are grateful to the European Research Council for funding this research through grant StG 307224 (Pico). Goldberg thanks NSERC for grant RGPIN-341529. We also thank the workshop attendees for comments

Monitoring of Biodiesel Transesterification Process Using Impedance Measurement

Transesterification is commonly used to produce biodiesel from methylester. In order to control the conversion process it is often useful to employ process monitoring and in particular monitor the mass transfer processes that limit the initial reaction rates. Such monitoring of the initial phase of reaction may provide opportunity for process optimization. Previous work has identified many methods to monitor reaction progress. This paper proposes the use of a simple method which is able to provide information regarding the progress of mass transfer and chemical reaction during biodiesel production. The process uses impedance measurement. The experimentally determined impedance results clearly show the two important phases of the transesterification reaction, a mass transfer control phase followed by a kinetically controlled phase

Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks - the case studies of container terminals

This paper aims to identify the optimal number of vehicles in environments with shuttle transportation tasks. These environments are very common industrial settings where goods are transferred repeatedly between multiple machines by a fleet of vehicles. Typical examples of such environments are manufacturing factories, warehouses and container ports. One very important optimisation problem in these environments is the fleet sizing problem. In real-world settings, this problem is highly complex and the optimal fleet size depends on many factors such as uncertainty in travel time of vehicles, the processing time of machines and size of the buffer of goods next to machines. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size

The impact of transport infrastructure projects on sustainable development within a major logistics gateway in North West England

In the North West of England the issue of a perceived infrastructure gap is of increasing concern. Investment needs to be made to improve the transport infrastructure of the region if it is to be expected to promote the development of its own regional logistics gateway. Funding tools have been set up to address the challenges arising from the imbalance in infrastructure development that exists between regions in the north of the United Kingdom and those in the south. For regions with well developed economies the outlook is promising as the availability of modern transport infrastructure looks set to improve. However, some sources believe that the development of new transport infrastructure will have a negative impact upon sustainable development. It is expected that this will occur in a range of both direct and indirect ways. As a result, it is critical that planning for the creation of new intermodal transport infrastructure, or the upgrading of that which already exists, takes into account the impact that these developments will have on the sustainable development of the host region. A scenario based development methodology is proposed in this paper. It was developed to provide a way to identify potential scenarios that may arise within a given region as a result of transport infrastructure projects. To create significant scenarios the methodology is dependent on the availability of a sufficient quantity of quality data. For this paper that data was collected through a focus group composed of stakeholders from the region in question. This was further supported by the performance of an impact survey using the same group of stakeholders

An improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals

This paper improves our previous attempts in which we studied a combination of an evolutionary algorithm (EA) and Monte Carlo simulation (MCS). Results of those studies showed the process of sampling in MCS is very time consuming. This prevents the EA from producing an accurate estimation of the robust solutions within reasonable time. Thus the present work improves the performance of the EA to make it possible to reach high quality solutions in reasonable time, therefore yielding a number of more practical solutions in real cases. Firstly, it proposes a new sampling technique to generate samples that better reflect the worst-case scenarios. This helps the EA to find more robust solutions using smaller sample sizes. Secondly, it proposes a new adaptive sampling technique to adjust the sample size during evolution. Subsequently, to evaluate the proposed algorithm we tested it in a typical environment with shuttle transport tasks: container terminal. Experimental results show that such improvements led to a significantly improved performance of the EA, thus making the proposed algorithm perfectly usable for empirical cases

Osteoprotegerin-Mediated Homeostasis of Rank+ Thymic Epithelial Cells Does Not Limit Foxp3+ Regulatory T Cell Development

In the thymus, medullary thymic epithelial cells (mTEC) regulate T cell tolerance via negative selection and Foxp3(+) regulatory T cell (Treg) development, and alterations in the mTEC compartment can lead to tolerance breakdown and autoimmunity. Both the receptor activator for NF-κB (RANK)/RANK ligand (RANKL)/osteoprotegerin (OPG) axis and expression of the transcriptional regulator Aire are involved in the regulation of thymus medullary microenvironments. However, their impact on the mechanisms controlling mTEC homeostasis is poorly understood, as are the processes that enable the thymus medulla to support the balanced production of mTEC-dependent Foxp3(+) Treg. In this study, we have investigated the control of mTEC homeostasis and examined how this process impacts the efficacy of Foxp3(+) Treg development. Using newly generated RANK Venus reporter mice, we identify distinct RANK(+) subsets that reside within both the mTEC(hi) and mTEC(lo) compartments and that represent direct targets of OPG-mediated control. Moreover, by mapping OPG expression to a subset of Aire(+) mTEC, our data show how cis- and trans-acting mechanisms are able to control the thymus medulla by operating on multiple mTEC targets. Finally, we show that whereas the increase in mTEC availability in OPG-deficient (Tnfrsf11b(−/−)) mice impacts the intrathymic Foxp3(+) Treg pool by enhancing peripheral Treg recirculation back to the thymus, it does not alter the number of de novo Rag2pGFP(+)Foxp3(+) Treg that are generated. Collectively, our study defines patterns of RANK expression within the thymus medulla, and it shows that mTEC homeostasis is not a rate-limiting step in intrathymic Foxp3(+) Treg production

Green vehicle technology to enhance the performance of a European port: a simulation model with a cost-benefit approach

In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port's performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one