Integrated drought risk assessment to support adaptive policy making in the Netherlands

Adaptive policy-making to prepare for current and future drought risks requires an integrated assessment of policy actions and combinations of those under changing conditions. This entails quantification of drought risks, integrating drought probability and socio-economic consequences for all relevant sectors that are potentially impacted by drought. The investment costs of proposed policy actions and strategies (various actions combined) can then be compared with the expected risk reduction to determine the cost-effectiveness. This paper presents a method to quantify drought risk in the Netherlands under changing future conditions and in response to policy actions. It illustrates how to use this information as part of a societal cost-benefit analysis and in building an adaptive long-term strategy. The method has been successfully applied to support decision making on the Netherlands’ national drought risk management strategy as part of the National Delta Program for climate change adaptation.

Balancing earliness and tardiness within workload control order release: an assessment by simulation

Most Workload Control literature assumes that delivery performance is determined by tardiness related performance measures only. While this may be true for companies that directly deliver to end-customers, for make-to-stock companies or firms that are part of supply chains, producing early often means large inventories in the finished goods warehouse or penalties incurred by companies downstream in the supply chain. Some earlier Workload Control studies used a so-called time limit, which constrains the set of jobs that can be considered for order release, to reduce earliness. However, recent literature largely abandoned the time limit since it negatively impacts tardiness performance. This study revisits the time limit, assessing the use of different adaptive policies that restrict its use to periods of either low or high load. By using a simulation model of a pure job shop, the study shows that an adaptive policy allows to balance the contradictory objectives of delaying the release of orders to reduce earliness and to release orders early to respond to periods of high load as quick as possible. Meanwhile, only using a time limit in periods of high load was found to be the best policy.

PANDEMICS, TOURISM AND COMMUNITIES: AN ADAPTIVE POLICY FROM A CASE STUDY OF INDONESIA

This article shed more light on several policies can be adopted by the Indonesian government in terms saving the tourism-communities and tourism itself during Covid-19 pandemic. However, travel restriction policies in various countries also demanded the solidarity of tourists to stay at home, avoiding crowds, and delaying travel plans during the COVID-19 pandemic. It clearly causes the tourism sector to be heavily affected. As a solution , this research used the systematic literature review method to collecting data from 63 article that relevant to this topic. The results of this research classified into three main point that inovation can be raise by re-emphasising alternative forms of tourism through responsible torism and sustainable tourism. Utilization of technology (e-tourism) is a new form of transformation in the tourism sector, it can help tourist screening, case and contact tracing. Therefore, several policy should be created by central to local government that concern about communities sustainability during it’s condition.

A spacecraft attitude manoeuvre planning algorithm based on improved policy gradient reinforcement learning

To solve the problem of spacecraft attitude manoeuvre planning under dynamic multiple mandatory pointing constraints and prohibited pointing constraints, a systematic attitude manoeuvre planning approach is proposed that is based on improved policy gradient reinforcement learning. This paper presents a succinct model of dynamic multiple constraints that is similar to a real situation faced by an in-orbit spacecraft. By introducing return baseline and adaptive policy exploration methods, the proposed method overcomes issues such as large variances and slow convergence rates. Concurrently, the required computation time of the proposed method is markedly reduced. Using the proposed method, the near optimal path of the attitude manoeuvre can be determined, making the method suitable for the control of micro spacecraft. Simulation results demonstrate that the planning results fully satisfy all constraints, including six prohibited pointing constraints and two mandatory pointing constraints. The spacecraft also maintains high orientation accuracy to the Earth and Sun during all attitude manoeuvres.

Achieving Effective Resource Management in Distributed SDN Controller Architectures

<p>As an emerging computer networking paradigm, Software-Defined Networking (SDN) empowers network operators with simplified network configuration and centralized network management. Recently, distributed controller architectures have become a notable invention where multiple controllers are jointly deployed in the network for request processing. One major research challenge for distributed controller architectures is to effectively manage the controller resources including allocating sufficient controllers to the suitable network locations and making the best use of the given controller resources. In general, existing approaches for managing the controller resources in the literature can be classified into three main directions. Designing new controller architectures belongs to the first direction, where the focus is on enabling workload shifting among controllers using switch migration. Designing controller placement algorithms to identify the number and locations of controllers is the second direction. Given the controller placement solution, the third direction is controller scheduling which aims to make the best use of the shared controllers by properly distributing requests among them. However, existing approaches have three major limitations. First, existing controller architectures feature a switch-controller binding which restricts the requests generated by a switch to only be processed by a predefined controller. Since each switch comes with different workload and the workload can be time-variant, the binding renders the bound controller susceptible to either being overloaded or underloaded. Second, existing placement algorithms have consistently underestimated the importance of controller scheduling. Due to the NP-hardness of the placement problem, Genetic Algorithm (GA) is a promising candidate. However, as a population-based approach, GA can be computationally expensive. Especially in a large network, the corresponding search space becomes too large for GA to handle effectively. Third, existing approaches for controller scheduling are mostly designed under the switch-controller binding constraint. When the scheduling is performed at a per-request level, the scheduling complexity increases significantly, rendering the efficiency and effectiveness of existing algorithms questionable. Apart from that, existing studies mainly focus on manually designing request dispatching policy which strongly relies on domain knowledge and involves a time-consuming fine-tuning process. The overall goal of this thesis is to effectively manage the controller resources in distributed SDN controller architectures. To address the three major limitations, three research objectives are established. First, this thesis aims to propose a new controller architecture to enable flexible controller placement and scheduling. Second, the thesis focuses on effectively and scalably identifying suitable controller placement while jointly taking the controller scheduling problem into consideration. Third, the thesis seeks to incorporate machine learning techniques in the request dispatching policy design to automatically learn adaptive and effective policies. To achieve the first objective, this thesis proposes a new BindingLess Architecture for distributed Controllers (BLAC) which features bindingless association between switches and controllers. With the newly introduced scheduling layer, requests can be transparently and flexibly dispatched among multiple controllers without invoking the time-consuming and complicated switch migration. Experiments conducted in this thesis show that BLAC significantly reduces the average response time and improves the throughput compared to existing SDN architectures. To achieve the second objective, this thesis proposes a Clustering-based Genetic Algorithm with Cooperative Clusters (CGA-CC) to tackle the controller placement problem. Particularly, CGA-CC partitions a large network into non-overlapping sub-networks to substantially reduce the search space of GA. Within each sub-network, GA is applied to identifying the placement solution. The quality of any given placement solution is evaluated by a gradient-descent-based scheduling algorithm which is developed to optimize the probability distribution of requests among all controllers. Moreover, a greedy load re-distribution mechanism is developed to handle unexpected demand variations by dynamically forwarding indigestible requests to adjacent sub-networks. Extensive simulations show that our algorithms can significantly outperform several existing and state-of-the-art algorithms and is more robust in handling unexpected traffic bursts. To achieve the third objective, this thesis proposes a Multi-Agent (MA) deep-reinforcement-learning-based approach with the aim to automatically learn adaptive, effective, and efficient policies used by each switch. In particular, a new adaptive policy representation is proposed to support networks with a changing number of controllers. To enable the training of an adaptive policy, a new policy gradient calculation technique is developed. Then the policy design problem is formulated as an MA Markov Decision Processing and a new MA training algorithm is proposed. The results show that the policy designed by our algorithm can easily adapt to networks with a changing number of controllers. Moreover, our policy can achieve significantly better performance compared with existing policies including the man-made policy (e.g., weighted round-robin), the model-based policy (e.g., the gradient-descent-based scheduling algorithm), and policies designed by other reinforcement learning algorithms (e.g., the proximal policy optimization algorithm).</p>

Achieving Effective Resource Management in Distributed SDN Controller Architectures

<p>As an emerging computer networking paradigm, Software-Defined Networking (SDN) empowers network operators with simplified network configuration and centralized network management. Recently, distributed controller architectures have become a notable invention where multiple controllers are jointly deployed in the network for request processing. One major research challenge for distributed controller architectures is to effectively manage the controller resources including allocating sufficient controllers to the suitable network locations and making the best use of the given controller resources. In general, existing approaches for managing the controller resources in the literature can be classified into three main directions. Designing new controller architectures belongs to the first direction, where the focus is on enabling workload shifting among controllers using switch migration. Designing controller placement algorithms to identify the number and locations of controllers is the second direction. Given the controller placement solution, the third direction is controller scheduling which aims to make the best use of the shared controllers by properly distributing requests among them. However, existing approaches have three major limitations. First, existing controller architectures feature a switch-controller binding which restricts the requests generated by a switch to only be processed by a predefined controller. Since each switch comes with different workload and the workload can be time-variant, the binding renders the bound controller susceptible to either being overloaded or underloaded. Second, existing placement algorithms have consistently underestimated the importance of controller scheduling. Due to the NP-hardness of the placement problem, Genetic Algorithm (GA) is a promising candidate. However, as a population-based approach, GA can be computationally expensive. Especially in a large network, the corresponding search space becomes too large for GA to handle effectively. Third, existing approaches for controller scheduling are mostly designed under the switch-controller binding constraint. When the scheduling is performed at a per-request level, the scheduling complexity increases significantly, rendering the efficiency and effectiveness of existing algorithms questionable. Apart from that, existing studies mainly focus on manually designing request dispatching policy which strongly relies on domain knowledge and involves a time-consuming fine-tuning process. The overall goal of this thesis is to effectively manage the controller resources in distributed SDN controller architectures. To address the three major limitations, three research objectives are established. First, this thesis aims to propose a new controller architecture to enable flexible controller placement and scheduling. Second, the thesis focuses on effectively and scalably identifying suitable controller placement while jointly taking the controller scheduling problem into consideration. Third, the thesis seeks to incorporate machine learning techniques in the request dispatching policy design to automatically learn adaptive and effective policies. To achieve the first objective, this thesis proposes a new BindingLess Architecture for distributed Controllers (BLAC) which features bindingless association between switches and controllers. With the newly introduced scheduling layer, requests can be transparently and flexibly dispatched among multiple controllers without invoking the time-consuming and complicated switch migration. Experiments conducted in this thesis show that BLAC significantly reduces the average response time and improves the throughput compared to existing SDN architectures. To achieve the second objective, this thesis proposes a Clustering-based Genetic Algorithm with Cooperative Clusters (CGA-CC) to tackle the controller placement problem. Particularly, CGA-CC partitions a large network into non-overlapping sub-networks to substantially reduce the search space of GA. Within each sub-network, GA is applied to identifying the placement solution. The quality of any given placement solution is evaluated by a gradient-descent-based scheduling algorithm which is developed to optimize the probability distribution of requests among all controllers. Moreover, a greedy load re-distribution mechanism is developed to handle unexpected demand variations by dynamically forwarding indigestible requests to adjacent sub-networks. Extensive simulations show that our algorithms can significantly outperform several existing and state-of-the-art algorithms and is more robust in handling unexpected traffic bursts. To achieve the third objective, this thesis proposes a Multi-Agent (MA) deep-reinforcement-learning-based approach with the aim to automatically learn adaptive, effective, and efficient policies used by each switch. In particular, a new adaptive policy representation is proposed to support networks with a changing number of controllers. To enable the training of an adaptive policy, a new policy gradient calculation technique is developed. Then the policy design problem is formulated as an MA Markov Decision Processing and a new MA training algorithm is proposed. The results show that the policy designed by our algorithm can easily adapt to networks with a changing number of controllers. Moreover, our policy can achieve significantly better performance compared with existing policies including the man-made policy (e.g., weighted round-robin), the model-based policy (e.g., the gradient-descent-based scheduling algorithm), and policies designed by other reinforcement learning algorithms (e.g., the proximal policy optimization algorithm).</p>

Learning Innovation in Elementary School to Welcome the New Normal In Makassar City

The application of policy adaptive to the implementation of policies in the world of education will result in a control. The existence of a policy adaptive policy that is applied can measure 70% of the output generated from the policy. Therefore, policy adaptive makes policy implementation in the world of education more active to contribute in achieving the required tasks. This study aims to (1) describe and analyze educational policy settings. (2) Describe the design and implementation of education policies. (3) Knowing policy monitoring on the implementation of education policies. This research method uses a descriptive qualitative approach through case studies. Collecting data through observation, interviews, and documentation. Data analysis uses data reduction, data presentation, verification, and drawing conclusions. The results of this study found that policy adaptation in the implementation of education policies in the Covid 19 Era was applied with 3 indicators, namely (1) policy settings through 6 important points whose implementation was in accordance with the ability of the school. (2) the design and implementation of education policies is carried out through 9 points, namely SOP, School Task Force, Curriculum Design, Design of technical guidance and special training for educators, PTM scenarios for online learning, and coordination of schools with supervisors, task forces, health centers and the Committee for Policy Implementation Government. (3) policy monitoring on the implementation of education policies carried out through 6 stages.

Evidentiary vacuum, epistemic communities and rare disease policymaking in India: an evolutionary policy perspective

There are divergent views among scholars and policymakers about the nature of permissible evidence for policymaking. It is often not feasible to construct a policy system exclusively based on objective research findings, particularly for rare diseases where conventionally accepted evidence remains a rarity. Evolutionary theories in such cases offer an overarching framework to represent the various heterodox understandings of what constitutes evidence and how evidence-based policies can be formulated under knowledge uncertainty. We conduct an empirical investigation of India’s rare disease policymaking endeavour in evolutionary perspective. The existing rare diseases policy architecture in India, in our view, reflects a ‘rationalistic’ framework. It intends to act only on ‘hard evidence’ to make, what may be called, an optimum decision, rather than initiating a ‘good enough’ policy decision based on existing (limited, soft) evidence and improving it incrementally through learning and trial-and-error. Our findings suggest that in the presence of ‘evidentiary vacuum’ and knowledge uncertainty, broadening the contours of epistemic communities, to include ‘lived experiences’ of the ‘lay’-stakeholders, can be effective in formulating an adaptive policy framework, which would ‘learn’ to better fit with the dynamic environment through inclusive deliberations, and trial-and-error.

The complexity of achieving UHC through PHC: how do we connect the competing narratives?

The commentary discusses the importance of developing Universal Health Coverage strategies through the lens of complex systems framework that evaluates policies not only in terms of the final desired outcome but also as an interplay of disparate views among diverse actors in the system. This view also confers a degree of agency and autonomy on the individual actors, whether they be patients or healthcare workers, and necessitates the inclusion of bottom-up participatory process in the development of UHC policies and interventions. These are consistent with the Primary Health Care principles articulated in the 1978 Alma Ata Declaration and will need to be integrated into the health system development framework to achieve UHC. Ultimately, this approach would encourage the creation of a more cooperative and adaptive policy environment in which each actor is encouraged to collaborate and are nudged toward a desirable outcome rather than through coercive means.

Marae-opoly: supporting localised Māori climate adaptation decisions with serious games in Aotearoa New Zealand

Far from being passive and/or static victims of climate change, indigenous peoples are hybridizing knowledge systems, and challenging and negotiating new environmental and social realities to develop their own adaptation options within their own registers of what is place and culture appropriate. Our paper seeks to demonstrate how we, as guests on Māori land, were able to develop a partnership with a Māori community facing difficult adaptation decisions regarding climate change hazards through the pragmatic navigation of multi-disciplinary research and practice. In particular, we co-developed and tested the potential of a serious game (Marae-opoly) approach as a platform which assembles cross-cultural climate change knowledge to learn, safely experiment and inform adaptation decisions. Marae-opoly was developed bespoke to its intended context—to support the creation of mutually agreeable dynamic adaptive policy pathways (DAPP) for localized flood adaptation. Game material was generated by drawing together detailed local knowledge (i.e. hydrology, climate data, mātauranga hapū) and situated adaptation options and accurate contextual data to create a credible gaming experience for the hapū of Tangoio Marae. We argue that the in-situ co-development process used to co-create Marae-opoly was fundamental in its success in achieving outcomes for the hapū. It also provided important lessons for the research team regarding how to enter as respectful guests and work together effectively to provide a resource to support our partners' adaptation decisions. The paper discusses the steps taken to establish research partnerships and develop the serious game and its subsequent playing, albeit we do not evaluate our indigenous research partners' adaptation decisions. Our contribution with this paper is in sharing an approach which cultivated the ground to enter as respectful guests and work together effectively to provide a resource for our partners' adaptation decisions.