Browse Topic: Planning / scheduling
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
Large farms cultivating forage crops for the dairy and livestock sectors require high-quality, dense bales with substantial nutritional value. The storage of hay becomes essential during the colder winter months when grass growth and field conditions are unsuitable for animal grazing. Bale weight serves as a critical parameter for assessing field yields, managing inventory, and facilitating fair trade within the industry. The agricultural sector increasingly demands innovative solutions to enhance efficiency and productivity while minimizing the overhead costs associated with advanced systems. Recent weighing system solutions rely heavily on load cells mounted inside baling machines, adding extra costs, complexity and weight to the equipment. This paper addresses the need to mitigate these issues by implementing an advanced model-based weighing system that operates without the use of load cells, specifically designed for round baler machines. The weighing solution utilizes mathematical
Cargo Routing Problem or Container Allocation Problem is key decision-making challenge in the maritime industry at operational level. Existing research focus on static environment or planning decisions, ignoring the dynamic arrival property of shipping request in practical world. In this paper, we introduced the Online Cargo Routing problem and formulation the path-based models under a space-time network. We proposed an online algorithm under the online primal-dual scheme: re-solving strategy. We further conducted simulation experiments under different demand distributions to demonstrate the performance of the proposed algorithm over the offline baselines.
The high rate of structural changes to the North American Light Vehicle market demands a new approach by the supply base towards strategic planning. A new Supplier Strategy Playbook is in order. First, some historical perspective. For the last several decades, suppliers grew accustomed to a product cadence of approximately five years between all-new platforms and major revisions. In North America, we were constantly pressed to continue improving vehicle efficiency and reduce emissions. Improved powertrain efficiency, vehicle lightweighting, and the advent of enhanced aerodynamics helped an industry that required constant innovation. Additionally, many programs were global in scope, requiring production and tooling in the major regions to launch in close sequence with global scale in tow. Wash, Rinse, Repeat. The textbook for suppliers was complex, though relatively predictable.
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning, one which considers not only the tasks to be performed by the RAS themselves but regards the flow of information to support the observability of the RAS by the operator. GTRI has developed an initial capability for mission planning of mixed motive, heterogeneous, autonomous systems for management of macro level metrics that support the decision making of the operator or user during employment. The work is ongoing, extensible to additional capability sets, and modular to support integration of other autonomous capabilities.
September is unofficially known in the industry as a key forecasting month. It's when several suppliers lock in their revenue forecasts for the next year. As we approach 2026, there are still several balls in the air with respect to the trajectory of the light vehicle market. Looming U.S. tariffs, negative economic and geo-political shifts, and the impact of changes to U.S. vehicle emission legislation have all brought with them a cloud of uncertainty that hovers over the industry. An industry that requires greater planning clarity, not less. Let's start with the tariffs. As of this writing, the major vehicle and parts importers outside of North America have agreed to 15% U.S. tariffs for vehicles and parts. In the case of Japan and the European Union, this is 12.5 percentage points higher than 2024 levels. In the case of South Korea, it's 15 points more, as there was a free trade agreement in force. While these framework agreements drive some level of certainty, the final details
The automotive industry faces the challenge of developing vehicles that meet current customer needs while being future-proof. Surveys conducted for this study show that customers are concerned about the financial risks of essential components such as energy storage systems, mainly due to aging and performance degradation, which significantly affect vehicle lifespans. Based on vehicle developer surveys, a clear need for action was identified. Given the rapid technological advancements in electrified drive systems, there is a need for innovative approaches that can easily adapt to changing requirements. Therefore, this paper presents a strategy combining foresight-based planning of system upgrades with product architecture design to create adaptable and sustainable vehicles through modularity. First, dynamic subsystem characteristics are identified to establish future energy storage technology requirements. Subsequently, future energy storage system technologies are examined to determine
The automotive industry is facing unprecedented pressure to reduce costs without compromising on quality and performance, particularly in the design and manufacturing. This paper provides a technical review of the multifaceted challenges involved in achieving cost efficiency while maintaining financial viability, functional integrity, and market competitiveness. Financial viability stands as a primary obstacle in cost reduction projects. The demand for innovative products needs to be balanced with the need for affordable materials while maintaining structural integrity. Suppliers’ cost structures, raw material fluctuations, and production volumes must be considered on the way to obtain optimal costs. Functional aspects lead to another layer of complexity, once changes in design or materials should not compromise safety, durability, or performance. Rigorous testing and simulation tools are indispensable to validate changes in the manufacturing process. Marketing considerations are also
From a mission operations perspective, swarms pose a planning challenge due to the limited scalability of ground operations. The capabilities needed to support swarm missions go beyond operator-specified geometry, alignment, or separation, but also crosslink communication with maintaining position in the formation. To address scalable control of orbital dynamics, NASA Ames Research Center has patented Swarm Orbital Dynamics Advisor (SODA) — a solution that accepts high-level configuration commands and provides the orbital maneuvers needed to achieve the desired type of swarm relative motion.
The impact of the upcoming U.S. federal election, global trade turmoil, a mediocre U.S. economy and the slumping ICE-to-EV (internal combustion engine to electric vehicle) transition must be considered. In my last column, we explored the growing use of scenarios to provide guardrails for future strategy. Suppliers can no longer rely upon a single forecast to drive future planning. The main culprits clouding the planning environment are program delays, rescopes and EV strategy shifts accompanied by the extension of ICE/hybrid models. The trajectory of EV launches and new offerings is decidedly ahead of the skis of consumer acceptance. This supply-and-demand mismatch is an ongoing challenge. It is important to understand the severity of program changes amid this slowing EV growth environment.
Bringing a construction project from planning on the page to execution in the real world is replete with challenges. Whether a company is building a sprawling solar farm or laying lines on the road, precision is paramount. Misfires of just a few inches can have massive implications, and that often leads to a plodding layout process. But, in partnership with Point One, Civ Robotics is ensuring that precise construction layouts won’t be at odds with efficiency.
Humans are generally good at whole-body manipulation, but robots struggle with such tasks. Now, MIT researchers have found a way to simplify this process, known as contact-rich manipulation planning. They use an AI technique called smoothing, which summarizes many contact events into a smaller number of decisions, to enable even a simple algorithm to quickly identify an effective manipulation plan for the robot.
For decades, there has been a tug-of-war between many suppliers and their vehicle-manufacturer customers with respect to future planning volumes. The stakes are significant. Using volumes that are too high drives an extreme capital commitment and risk suppliers to stranded capital and missed opportunities to employ resources elsewhere. Using volumes that are too low means the OEM may miss potential sales and the supplier would be stressed with extreme overtime to keep up. It is a never-ending balance. OEMs often use internally built ‘Capacity Planning Volumes’ (CPVs) to ensure they capacitize to both their annual and peak volume expectations. These volumes are used as the divisor to understand per-part costs and how tooling, machines, infrastructure and other capitalized items are amortized over the life of the program. Suppliers often utilize third-party views such as the S&P Global Mobility Light Vehicle Production Forecasts to gain an impartial perspective of market dynamics, as
Aiming at the problem of weak communication, strong interference, cross-domain, and large-scale environment, it is difficult to achieve efficient decision-making and planning in the collaborative operation of intelligent groups. Based on the SOM algorithm, this paper proposes a dual-selection allocation and distributed vectorized trajectory planning. Form a collaborative planning algorithm that can be updated with high frequency and a rational decision-making mechanism. Provide technical support for collaborative search and detection of intelligent groups. At the same time, based on the principle of minimum consistency, this paper proposes a clock synchronization model under spatial coordination and conducts simulation experiments to verify it. The result proves the efficiency and practicability of the collaborative intelligent decision-making plan proposed in this paper.
Autonomous vehicle navigation requires signal processing of the vehicle’s sensors to provide meaningful information to the planners such that challenging artifacts like shadows, rare events, obstructive vegetation, etc. are identified properly, avoiding ill-informed navigation. Using a single algorithm such as semantic segmentation of camera images is often not enough to identify those challenging features but can be overcome by processing more than one type of sensor and fusing their results. In this work, semantic segmentation of camera image and LiDAR point cloud signals is performed using Echo State Networks to overcome the challenge of shadows identified as obstructions in off-road terrains. The coordination of algorithms processing multiple sensor signals is shown to avoid unnecessary road obstructions caused by high-contrast shadows for more informed navigational planning.
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