Matching marginal moments and lag autocorrelations with MAPs

This paper presents a procedure that constructs a Markovian Arrival Process (MAP) based on the mean, the squared coefficient of variation and the lag-1 autocorrelation of the inter-arrival times. This method always provides a valid MAP without posing any restrictions on the three input parameters. Besides matching these three parameters, it is possible to match the third moment of the inter-arrival times and the decay of the autocorrelation function as well, if they fall into the given (very wide) bounds

Bloody fast blood collection

This thesis consists of four parts: The first part contains an introduction, the second presents approaches for the evaluation of waiting times at blood collection sites, the third uses these to present approaches that improve waiting times at blood collection sites. The final part shows the application of two of the approaches to data from real blood collection sites, followed by the conclusions that can be drawn from this thesis. Part I: Introduction, contains two chapters. Chapter 1 introduces the context for this thesis: blood banks in general, the Dutch blood bank Sanquin and blood collection sites. The chapter sketches some of the challenges faced with respect to blood collection sites. As blood donors are voluntary and non-remunerated, delays and waiting times within blood collection sites should be kept at acceptable levels. However, waiting times are currently not incorporated in staff planning or in other decisions with respect to blood collection sites. These blood collection sites will be the primary focus of this thesis. This thesis provides methods that do take waiting times into account, aiming to decrease waiting times at blood collection sites and leveling work pressure for staff members, without the need for additional staff. Chapter 2 then presents a technical methods that will be used most of the chapters in this thesis: uniformization. Uniformization can be used to transform Continuous Time Markov Chains (CTMCs) — that are very hard to analyze — into Discrete Time Markov Chains (DTMCs) — that are much easier to analyze. The chapter shows how the method works, provides an extensive overview of the literature related to the method, the (technical) intuition behind the method as well as several extensions and applications. Although not all of the extensions and applications are necessary for this thesis, it does provide an overview of one of the most valuable methods for this thesis. Part II: Evaluation, contains two chapters that propose and adapt several methods to compute waiting times and queues at blood collection sites. A blood collection site is best modeled as a time-dependent queueing network, requiring non-standard approaches. Chapter 3 considers a stationary, i.e. not time-dependent model of blood collection sites as a first step. A blood collection site consists of three main stations: Registration, Interview and Donation. All three of the stations can have their own queue. This means that even the stationary model is non-trivial for some computations. However, for the stationary model, an analytic so-called product form expression is derived. Based on this product form, two more results are shown. The first result is that the standard waiting time distributions from M|M|s queues are applicable, as if the queue is in isolation. It is then concluded that no closed form expression exist for the total waiting or delay time distribution, as the distributions of the three stations in tandem are not independent. Therefore a numerical approach is presented to compute the total delay time distribution of a collection site. All of the results are supported by numerical examples based on a Dutch blood collection site. The approach for the computation of the total delay time distribution can also be combined with the approach from Chapter 4 for an extension to a time-dependent setting. Chapter 4 shows an approach to deal with these time-dependent aspects in queueing systems, as often experienced by blood collection sites and other service systems, typically due to time-dependent arrivals and capacities. Easy and quick to use queueing expressions generally do not apply to time-dependent situations. A large number of computational papers has been written about queue length distributions for time-dependent queues, but these are mostly theoretical and based on single queues. This chapter aims to combine computational methods with more realistic time-dependent queueing networks, with an approach based on uniformization. Although uniformization is generally perceived to be too computationally prohibitive, we show that our method is very effective for practical instances, as shown with an example of a Dutch blood collection site. The objective of the results is twofold: to show that a time-dependent queueing network approach can be beneficial and to evaluate possible improvements for Dutch blood collection sites that can only be properly assessed with a time-dependent queueing method. Part III: Optimization, contains four chapters that aim to improve service levels at Sanquin. The first three chapters focus on three different methods to decrease queues at blood collection sites. Chapters 5 and 6 focus on improving the service by optimizing staff allocation to shifts and stations. Chapter 7 focuses on improving the arrival process with the same goal. Chapter 8 is focused at improving inventory management of red blood cells. Donors do not arrive to blood collection sites uniformly throughout the day, but show clear preferences for certain times of the day. However, the arrival patterns that are shown by historical data, are not used for scheduling staff members at blood collection sites. As a first significant step to shorten waiting times we can align staff capacity and shifts with walk-in arrivals. Chapter 5 aims to optimize shift scheduling for blood collection sites. The chapter proposes a two-step procedure. First, the arrival patterns and methods from queueing theory are used to determine the required number of staff members for every half hour. Second, an integer linear program is used to compute optimal shift lengths and starting times, based on the required number of staff members. The chapter is concluded with numerical experiments that show, depending on the scenario, a reduction of waiting times, a reduction of staff members or a combination of both. At a blood collection site three stations (Registration, Interview and Donation) can roughly be distinguished. Staff members at Dutch blood collection sites are often trained to work at any of these stations, but are usually allocated to one of the stations for large fractions of a shift. If staff members change their allocation this is based on an ad hoc decision. Chapter 6 aims to take advantage of this mostly unused allocation flexibility to reduce queues at blood collection sites. As a collection site is a highly stochastic process, both in arrivals and services, an optimal allocation of staff members to the three stations is unknown, constantly changing and a challenge to determine. Chapter 6 provides and applies a so-called Markov Decision Process (MDP) to compute optimal staff assignments. Extensive numerical and simulation experiments show the potential reductions of queues when the reallocation algorithm would be implemented. Based on Dutch blood collection sites, reductions of 40 to 80% on the number of waiting donors seem attainable, depending on the scenario. Chapter 7 also aims to align the arrival of donors with scheduled staff, similarly to Chapter 5. Chapter 7 tries to achieve this by changing the arrivals of donors. By introducing appointments for an additional part of donors, arrivals can be redirected from the busiest times of the day to quiet times. An extended numerical queueing model with priorities is introduced for blood collection sites, as Sanquin wants to incentive donors to make appointments by prioritizing donors with appointments over donors without appointments. Appointment slots are added if the average queue drops below certain limits. The correct values for these limits, i.e. the values that plan the correct number of appointments, are then determined by binary search. Numerical results show that the method succeeds in decreasing excessive queues. However, the proposed priorities might result in unacceptably high waiting times for donors without appointments, and caution is therefore required before implementation. Although this thesis mainly focuses on blood collection sites, many more logistical challenges are present at a blood bank. One of these challenges arises from the expectation that Sanquin can supply hospitals with extensively typed red blood cell units directly from stock. Chapter 8 deals with this challenge. Currently, all units are issued according to the first-in-first-out principle, irrespective of their specific typing. These kind of issuing policies lead to shortages for rare blood units. Shortages for rare units could be avoided by keeping them in stock for longer, but this could also lead to unnecessary wastage. Therefore, to avoid both wastage and shortages, a trade-off between the age and rarity of a specific unit in stock should be made. For this purpose, we modeled the allocation of the inventory as a circulation flow problem, in which decisions about which units to issue are based on both the age and rarity of the units in stock. We evaluated the model for several settings of the input parameters. It turns out that, especially if only a few donors are typed for some combinations of antigens, shortages can be avoided by saving rare blood products. Moreover, the average issuing age remains unchanged. Part IV: Practice and Outlook concludes this thesis. The first of two chapters in this part shows the combined application of two approaches from this thesis to data from three collection sites in the Netherlands. The final chapter of this thesis presents the conclusions that can be drawn from this thesis and discusses an outlook for further research. Chapter 9 shows the combined application of the methods in Chapters 5 and 6 to three real collection sites in Dutch cities: Nijmegen, Leiden and Almelo. The collection sites in Nijmegen and Leiden are both large fixed collection sites. The collection site in Almelo is a mobile collection site. The application of each one of the methods individually reduce waiting times significantly, and the combined application of the methods reduces waiting times even further. Simultaneously, small reductions in the number of staff hours are attainable. The results from Chapter 9 summarize the main message of this thesis: waiting time for blood donors at blood collection sites can be reduced without the need for more staff members when the working times of staff members are used more effectively and efficiently, and controlling the arrival process of donors. The approaches presented in this thesis can be used for this purpose. This is not only beneficial for blood donors, but will also result in more balanced workload for staff members, as fluctuations in this workload are reduced significantly

Scalable Performance Analysis of Massively Parallel Stochastic Systems

The accurate performance analysis of large-scale computer and communication systems is directly inhibited by an exponential growth in the state-space of the underlying Markovian performance model. This is particularly true when considering massively-parallel architectures such as cloud or grid computing infrastructures. Nevertheless, an ability to extract quantitative performance measures such as passage-time distributions from performance models of these systems is critical for providers of these services. Indeed, without such an ability, they remain unable to offer realistic end-to-end service level agreements (SLAs) which they can have any confidence of honouring. Additionally, this must be possible in a short enough period of time to allow many different parameter combinations in a complex system to be tested. If we can achieve this rapid performance analysis goal, it will enable service providers and engineers to determine the cost-optimal behaviour which satisfies the SLAs. In this thesis, we develop a scalable performance analysis framework for the grouped PEPA stochastic process algebra. Our approach is based on the approximation of key model quantities such as means and variances by tractable systems of ordinary differential equations (ODEs). Crucially, the size of these systems of ODEs is independent of the number of interacting entities within the model, making these analysis techniques extremely scalable. The reliability of our approach is directly supported by convergence results and, in some cases, explicit error bounds. We focus on extracting passage-time measures from performance models since these are very commonly the language in which a service level agreement is phrased. We design scalable analysis techniques which can handle passages defined both in terms of entire component populations as well as individual or tagged members of a large population. A precise and straightforward specification of a passage-time service level agreement is as important to the performance engineering process as its evaluation. This is especially true of large and complex models of industrial-scale systems. To address this, we introduce the unified stochastic probe framework. Unified stochastic probes are used to generate a model augmentation which exposes explicitly the SLA measure of interest to the analysis toolkit. In this thesis, we deploy these probes to define many detailed and derived performance measures that can be automatically and directly analysed using rapid ODE techniques. In this way, we tackle applicable problems at many levels of the performance engineering process: from specification and model representation to efficient and scalable analysis

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Network Science for IoT

The research work presented in this thesis is based on the concept and defintion of network that can spread in several and different real world contexts. Indeed, we can refer to a network in a telecommunications sense considering a collection of transmitters, receivers, and communication channels that send or are used to send information to one another. However, as a matter of fact, in nature there are other several examples of networks: the human brain is one of them. The relationship between the actors in Hollywood can be studied in terms of network as well, a generic social community can be compared to a network, eco-systems are networks of species. The recent Network Science aims at studying all these systems using a set of common mathematical methods. In the following of the thesis, we will focus on some of well known telecommunications networks issues using standard telecommunications procedures to address them, with relevant reference to video flow transmissions and management of electric vehicles networks. At the same time, different models aiming at reach the same goals in contexts that may differ from a telecommunications setup can be used. In more details, we will evaluate queueing systems, jamming problems, groups recognition in networks, and mobile computing using game theoretic approaches. It is worth noting that this aspect can be also seen in a reverse order. Indeed, we will discuss how standard telecommunications analysis can be used to investigate on problems not directly related to a telecommunications background. In particular, one of our future purposes is to investigate on the brain connectivity that is raising significant interest in the recent scientific society

Optimizing Constrainted Concurrent Applications at Run-time

Computer systems are resource constrained. Application adaptation is a useful way to optimize system resource usage while satisfying an application’s performance requirements. Current multicore computer systems supporting these applications, however, are not designed to reliably meet these requirements. Meanwhile, these computer systems are resource-limited, e.g., have power-induced energy and thermal constraints. Compounding the application’s performance requirements are increasingly-stringent microprocessor thermal constraints. Previous application adaptation efforts, however, were ad-hoc, time-consuming, and highly application-specific, with limited portability between computer systems. This thesis presents OCCAM, a software platform for developing multicore adaptable applications. OCCAM’s design-time platform consists of design patterns, APIs, and data structures that allow application developers to specify the performance constraints and application-specific optimization techniques. OCCAM generates a run-time controller offline, using profiling data. It then uses this profiling data to generate an internal model that it subsequently employs to generate a robust Markov Decision Process-based Model Predictive Controller. Using a set of Recognition, Mining, and Synthesis benchmarks, the experimental study demonstrates that OCCAM can successfully optimize the system while meeting the systems performance requirements across a wide range of computer platforms, ranging from an energy-constrained single-core system to a high-performance 16-core system. Finally, OCCAM presents a simulation-based, stochastic model checking-based framework for quantifying the robustness of the controller

Theoretical Optimization of Enzymatic Biomass Processes

This dissertation introduces a complete, stochastically-based algorithmic framework Cellulect to study, optimize and predict hydrolysis processes of the structured biomass cellulose. The framework combines a comprehensive geometric model for the cellulosic substrate with microstructured crystalline/amorphous regions distribution, distinctive monomers, polymer chain lengths distribution and free surface area tracking. An efficient tracking algorithm, formulated in a serial fashion, performs the updates of the system. The updates take place reaction-wise. The notion of real time is preserved. Advanced types of enzyme actions (random cuts, reduced/non-reduced end cuts, orientation, and the possibility of a fixed position of active centers) and their modular structure (carbohydrate-binding module with a flexible linker and a catalytic domain) are taken into account within the framework. The concept of state machines is adopted to model enzyme entities. This provides a reliable, powerful and maintainable approach for modelling already known enzyme features and can be extended with additional features not taken into account in the present work. The provided extensive probabilistic catalytic mechanism description further includes adsorption, desorption, competitive inhibition by soluble product polymers, and dynamical bond-breaking reactions with inclusive dependence on monomers and their polymers states within the substrate. All incorporated parameters refer to specific system properties, providing a one to one relationship between degrees of freedom and available features of the model. Finally, time propagation of the system is based on the modified stochastic Gillespie algorithm. It provides an exact stochastic time-reaction propagation algorithm, taking into account the random nature of reaction events as well as its random occurrences. The framework is ready for constrained input parameter estimation with empirical data sets of product concentration profiles by utilizing common optimization routines. Verification of the available data for the most common enzyme kinds (EG, β-G, CBH) in the literature has been accomplished. Sensitivity analysis of estimated model parameters were carried out. Dependency of various experimental input is shown. Optimization behavior in underdetermined conditions is inspected and visualized. Results and predictions for mixtures of optimized enzymes, as well as a practical way to implement and utilize the Cellulect framework are also provided. The obtained results were compared to experimental literature data demonstrate the high flexibility, efficiency and accuracy of the presented framework for the prediction of the cellulose hydrolysis process

Verified Synthesis of Optimal Safety Controllers for Human-Robot Collaboration

In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. To address this need, we introduce and evaluate a tool-supported approach for safety controller synthesis and deployment. The new approach focuses on human-robot collaboration in manufacturing, and is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, formally verified against correctness criteria, and translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task

Verified synthesis of optimal safety controllers for human-robot collaboration

We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our controller synthesis, verification and validation approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot