Roadmap Planning Research Articles

A comprehensive prescriptive action plan development model for information systems through project prioritization and roadmap planning

Information technology/information systems investments are critical for business success. However, managers often face the challenge of prioritizing and selecting projects that better satisfy their needs, mainly due to the high number of options available and limited resources. Since investment decisions can strongly impact the organizational path, well-defined processes are required to guide them. At present, there is a lack of clarity about how organizations should proceed in information systems planning since most of the research literature focuses on decision models and criteria that only produce a prioritized list of initiatives without addressing the process and the subsequent need to define a plan for project implementation. In this article, we propose a new method for supporting the definition of information systems action plans resulting from a design science research project carried out in a public organization. We contribute to the theory and practice by providing a method that outlines the process comprehensively and describes the definition and application of a straightforward decision model for project prioritization in a real case, aiming to assist managers in conducting an efficient decision and prioritization of information technology investments.

Open energy system modelling for low-emission hydrogen roadmap planning: The case of Colombia

Climate change is driving the need for new approaches to energy production and utilization globally, contributing to the mitigation of greenhouse gas (GHG) emissions. Low-emission hydrogen is recognized as a key energy vector for decarbonization, and numerous countries are currently evaluating strategies and roadmaps for its integration. Colombia, with its abundant renewable resources, is poised as a potential player in hydrogen production. This study presents a techno-economic assessment of hydrogen pathways and their cross-sectoral interactions within the Colombian energy system. By employing an open energy system optimization framework (OSeMOSYS), we modelled a detailed supply-demand hydrogen chain and analysed a series of scenarios from 2021 to 2050, including a sensitivity analysis. Our results show that hydrogen pathways will require 2–6.4 Mt/year in hydrogen production, 8–27 GW in new installed capacity, and US$50–80 billion in investments, with favourable impacts in GHG mitigation, energy imports, and socioeconomic benefits. We use these findings to suggest an update to the targets of the current National Hydrogen Roadmap focused on a higher ambition to deploy a full hydrogen economy. Our open methodology provides a valuable tool for conducting further assessments in developing countries interested in defining low-emission hydrogen roadmaps.

Product Roadmap Planning in Dynamic Markets

In the quickly changing business environment of today, product roadmap planning has become an essential component of organisational strategy. This is especially true in dynamic markets that are characterised by fast technology breakthroughs, altering customer tastes, and strong competitive challenges. This article looks into the complexities of product roadmap planning within such unpredictable marketplaces, analysing the most important tactics, best practices, and obstacles that are encountered by organisations that are attempting to retain a competitive advantage. Product roadmaps are strategic blueprints that explain the vision, direction, and advancement of a product across time. They are used to plan the product's development. For these roadmaps to be effective in dynamic marketplaces, they need to be flexible and forward-looking, able to accommodate developments that are both anticipated and unexpected. The purpose of this article is to investigate the fundamental elements that constitute efficient product roadmap planning, with a particular focus on the importance of adaptability and agility in the face of changes in the market. Finding a way to strike a balance between short-term and long-term aims is one of the most significant issues that dynamic marketplaces provide. Companies have to find a way to negotiate the conflict that exists between the urgent requirements of their customers and their wider strategic goals. The implementation of iterative development, periodic appraisal of market trends, and the inclusion of consumer input into the roadmap are some of the strategies that are discussed in this article as potential methods for striking this balance.

An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.

Enterprise Architecture Frameworks as Support for Implementation of Regulations: Approach and Experiences from GDPR

Enterprise Architecture (EA) management has been discussed as being supportive for implementation of regulations in enterprises and organizations, but the role of EA frameworks in this context has not been addressed intensely. The EU General Data Protection Regulation (GDPR) is one of the most frequently discussed regulation in industry and research, and expected to cause a shift in viewpoint of enterprises from a technological perspective dominated by information security issues to an organizational perspective governed by GDPR-compliant organizational structures and processes. A well-documented Enterprise Architecture (EA) and a working Enterprise Architecture Management (EAM) organization are expected to significantly ease the roadmap planning for GDPR implementation. Therefore, this article focuses on the practice of EA use for GDPR implementation. The main contributions of this article are (a) an analysis and comparison of existing architecture frameworks and how they address security-related issues, and (b) a case study from financial industries illustrating the use of EA for implementing GDPR compliance.

Quantifying decision making for data science: from data acquisition to modeling

Organizations, irrespective of their size and type, are increasingly becoming data-driven or aspire to become data-driven. There is a rush to quantify value of their own internal data or the value of integrating their internal data with external data, and performing modeling on such data. A question that analytics teams often grapple with is whether to acquire more data or expend additional effort on more complex modeling, or both. If these decisions can be quantified a priori, it can be used to guide budget and investment decisions. To that end, we quantify the Net Present Value (NPV) of the tasks of additional data acquisition or more complex modeling, which are critical to the data science process. We develop a framework, NPVModel, for a comparative analysis of various external data acquisition and in-house model development scenarios using NPVs of costs and returns as a measure of feasibility. We then demonstrate the effectiveness of NPVModel in prescribing strategies for various scenarios. Our framework not only acts as a suggestion engine, but it also provides valuable insights into budgeting and roadmap planning for Big Data ventures.

Water chemistry technology – one of the key technologies for safe and reliable nuclear power plant operation

Water chemistry control is one of the key technologies to establish safe and reliable operation of nuclear power plants. Continuous and collaborative efforts of plant manufacturers and plant operator utilities have been focused on optimal water chemistry control, for which, a trio of requirements for water chemistry should be simultaneously satisfied: (1) better reliability of reactor structures and fuel rods; (2) lower occupational exposure and (3) fewer radwaste sources. Various groups in academia have carried out basic research to support the technical bases of water chemistry in plants. The Research Committee on Water Chemistry of the Atomic Energy Society of Japan (AESJ), which has now been reorganized as the Division of Water Chemistry (DWC) of AESJ, has played important roles to promote improvements in water chemistry control, to share knowledge about and experiences with water chemistry control among plant operators and manufacturers and to establish common technological bases for plant water chemistry and then to transfer them to the next generation of plant workers engaged in water chemistry. Furthermore, the DWC has tried and succeeded arranging R&D proposals for further improvement in water chemistry control through roadmap planning. In the paper, major achievements in plant technologies and in basic research studies of water chemistry in Japan are reviewed. The contributions of the DWC to the long-term safe management of the damaged reactors at the Fukushima Daiichi Nuclear Power Plant until their decommissioning are introduced.

Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCTSPN). Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable probabilistic roadmap (PRM) is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmetric TSP (ATSP). Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.

A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots

We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that are most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.

Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning

Manipulation planning is a complex task for robots with a manipulator arm that need to grasp objects in the environment, specifically under narrow spatial conditions restricting the workspace of the robot. A popular approach for generating motion plans is probabilistic roadmap planning. However, the sampling strategy of such planners is usually unguided, and hence may lead to motion plans that seem counterintuitive for a human observer. In this article we present an approach that generates heuristics for the probabilistic sampling strategy from spatial plans that abstract from concrete metric data. These spatial plans describe a free trajectory in the workspace of the robot on a purely qualitative level, i.e., by employing spatial relations from formalisms considered in the domain of Qualitative Spatial and Temporal Reasoning. We discuss how such formalisms and constraint-based reasoning methods can be applied to approximate geometrically feasible motions. The paper is completed by an evaluation of a hybrid planning system in different spatial settings showing that run-times are notably improved when an abstract plan is considered as a guidance heuristic.

On the Probabilistic Foundations of Probabilistic Roadmap Planning

Why is probabilistic roadmap (PRM) planning probabilistic? How does the probability measure used for sampling a robot’s configuration space affect the performance of a PRM planner? These questions have received little attention to date. This paper tries to fill this gap and identify promising directions to improve future planners. It introduces the probabilistic foundations of PRM planning and examines previous work in this context. It shows that the success of PRM planning depends mainly and critically on favorable “visibility” properties of a robot’s configuration space. A promising direction for speeding up PRM planners is to infer partial knowledge of such properties from both workspace geometry and information gathered during roadmap construction, and to use this knowledge to adapt the probability measure for sampling. This paper also shows that the choice of the sampling source—pseudo-random or deterministic—has small impact on a PRM planner’s performance, compared with that of the sampling measure. These conclusions are supported by both theoretical and empirical results.

Sampling and node adding in probabilistic roadmap planners

The probabilistic roadmap approach is one of the leading motion planning techniques. Over the past decade the technique has been studied by many different researchers. This has led to a large number of variants of the approach, each with its own merits. It is difficult to compare the different techniques because they were tested on different types of scenes, using different underlying libraries, implemented by different people on different machines. In this paper we provide a comparative study of a number of these techniques, all implemented in a single system and run on the same test scenes and on the same computer. In particular we compare collision checking techniques, sampling techniques, and node adding techniques. The results were surprising in the sense that techniques often performed differently than claimed by the designers. The study also showed how difficult it is to evaluate the quality of the techniques. The results should help future users of the probabilistic roadmap planning approach in deciding which technique is suitable for their situation.

Narrow passage sampling for probabilistic roadmap planning

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.

Product Chain Responsibility — An Industry Perspective

Traditionally, ecologically conscious product design (EcoDesign) emphasises the systematic environmental improvement of products, with tools derived from life cycle assessment (LCA) being used to record the improvements achieved. The possible integration within a company's environmental management system (EMS), taking Philips as the example, has been described in previously world-wide distributed and publicly available management guidelines called ‘Point of no return’. An operational counterpart was published in 2000 and is called ‘Greening Your Business’, it features an extended method, which includes all milestones in the total sequence from company policy via strategic choices and roadmap planning to the product's creation. This article describes the overall approach.