Collaborative Task Allocation

You think giving everyone 8 tasks is fair? Your "balanced" workload distribution is actually terrible. 🔥 Your A-player finishes in 2 days and sits idle Your struggling performer is drowning Your specialist is doing work beneath their skill level Your junior is assigned tasks way over their head Equal ≠ Equitable I built an AI system that fixes this. It redistributes weekly workloads based on: ➝ Capacity (not just hours, but effective capacity) ➝ Capability (skill match for each specific task) ➝ Context (what else is on their plate) ➝ Career growth (strategic stretch assignments) Example from last week: ✲ OLD WAY: Everyone gets 10 tasks. "Fair." ✲ AI WAY: ➝ Senior analyst: 7 complex tasks (40 hours) ➝ Mid-level: 12 medium tasks (40 hours) ➝ Junior: 15 simple tasks + 2 stretch tasks (40 hours) Same hours. Completely different distributions. Massively better outcomes. What's controversial? The AI suggested giving our top performer FEWER tasks. Because her tasks were all high-complexity strategic work that required deep focus. 12 strategic tasks > 20 scattered tactical tasks What the AI revealed about our team: ✲ Only low-value work was being done by our "busiest" employee. ✲ The most valuable output came from our "underutilized" individual. ✲ People were being burned out by the wrong kind of work, not too much work. The most potent feature of the system: "Workload Scoring with Impact Adjustment" Every task is different. 1 strategic task = 3 tactical tasks in cognitive load 1 creative task = 2 analytical tasks in energy drain 1 collaborative task = 1.5 solo tasks in time required The AI weights everything appropriately. Results after 8 weeks: ➝ 34% increase in high-impact work completed ➝ 56% reduction in "I'm overwhelmed" messages ➝ 0 people working weekends (down from 5) ➝ 89% team satisfaction score (up from 62%) It exposed that our workload problem wasn't volume it was misalignment. Who needs this? Every manager who: ⇢ Assigns work based on "who has time" ⇢ Deals with constant "I'm overloaded" complaints ⇢ Sees uneven performance across similar roles ⇢ Struggles with succession planning ⇢ Wants to develop their team strategically The framework (simplified): Input: Tasks + Team Data ↓ AI Analysis: Skill Match + Capacity + Priority ↓ Optimization: Balance cognitive load, not just hours ↓ Output: Recommendations with reasoning ↓ Manager: Approve/Adjust ↓ Tracking: Measure actual outcomes ↓ Learning: Improve future distributions Building this taught me: Most workload problems are actually: Poor task-skill matching (40%) Invisible cognitive load differences (30%) Unclear priorities (20%) Actual capacity issues (10%) AI solves # 1-3. Hiring solves # 4. What's your team's biggest workload challenge? Comment below and I'll tell you if AI can solve it. PS: If you're a manager still doing this manually, you're wasting 10+ hours per month. Let's fix that.

Scaling broke our system and here’s How we fixed it…. ✈️ You're mid-air. Everything's running. But something's off with the instruments — barely visible, but potentially critical. That's where we were a few months ago. As a CEO of an AI & IT consultancy and development firm, I'm not just thinking about innovation. I'm thinking about precision, consistency, and systems that scale without slipping. We’ve been growing, new clients, complex projects, and new developers joining our ranks. It’s an exciting phase. But scaling has its own hidden turbulence. On the surface, projects were progressing. Timelines were respected. Clients were happy. But internally, something didn’t feel aligned. Not broken, just... inefficient. The challenge wasn’t technical. It was operational. Task allocation🧩 — one of the most basic (and important) parts of delivery was becoming inconsistent. We noticed: ↳ Some developers were juggling too many tasks ↳ Others had bandwidth but weren’t being tapped at the right moment ↳ Matching skill sets to work was becoming guesswork, not design It wasn’t about quality slipping, it was about waste. Wasted effort, wasted time, wasted potential. So we did what most teams skip when things are “still working.” 🛑 We paused. 👂 We listened. 🔍 We diagnosed. We pulled the dev team and operations team into a series of focused sessions and asked questions. ↳ What’s working for you right now? ↳ What’s frustrating? ↳ Where are we duplicating effort? ↳ What’s the invisible cost of how we’re assigning work? ↳What’s your thought on improving over current process? And what emerged was clear: We weren’t lacking talent. We were lacking a system. With those insights, we took an operations research lens to the problem. We started mapping: 🧠 Developer skills and proficiencies 🧩 Task complexity and dependencies 📆 Real-time availability 🤝 Preferred work styles and pairing combinations We’re now in the process of building a dynamic internal allocation model, powered by the same AI principles we apply in client work. It’s not just about assigning tasks. It’s about optimizing the flow of value through the system. And it’s already working. Productivity is up. Devs feel more focused. We’ve eliminated bottlenecks before they even appear. The real win wasn’t just efficiency. It was alignment. Between people. Between capacity and delivery. Between intention and execution. Because when your team feels heard and your system reflects reality, everything works better. The friction we felt wasn't failure, it was feedback. The kind that only surfaces when you're scaling fast and care deeply about doing it right. Operational issues aren’t always dramatic. Sometimes they’re invisible. And solving them requires more than dashboards and KPIs, it requires conversations, systems thinking, and the willingness to rebuild mid-flight. Have you ever faced this quiet kind of misalignment as your team grew? Would love to hear how you approached it.👇

Welcome to the first #Automation Alphabet on Linkedin. Today; I had trouble finding anything related to AMRs starting with the letter J… But here it is: J- like Job scheduling As always, starting with a definition: Job scheduling for Autonomous Mobile Robots (AMRs) involves allocating and prioritizing tasks or "jobs" that AMRs need to perform within a defined workspace, such as a warehouse, manufacturing floor, or healthcare facility. In a larger scope this means to define the perimeter of work that the robot is doing; that’s the groundwork. But there is also a second layer which can impact performance quite a lot. The distribution of tasks and orders/ transport jobs that have to be done day in day out… Who is deciding which robot takes what pallet? And when? How is prioritization done? Here are the main takeaways: 1. Task Assignment Job Allocation: Jobs can include specific tasks like picking up materials, delivering items to a workstation, or scanning inventory. Job scheduling decides which AMR is assigned to which task based on factors like proximity, availability, and battery level. Task Priority: Some tasks may have higher priority (e.g., delivering materials to an assembly line) and need to be scheduled first. Prioritization ensures time-sensitive tasks are completed within their required timeframes. Dynamic Task Allocation: In dynamic environments, task requirements may change due to delays, obstacles, or urgent requests. AMRs should be rescheduled dynamically to adapt to these conditions, maintaining an optimal workflow. 2. Optimization Goals Minimizing Travel Time: Reducing the distance each AMR needs to travel to complete its tasks, which saves energy and minimizes wear on the robot. Energy Efficiency: Balancing AMR tasks so that robots with low battery levels are scheduled for shorter tasks or sent for recharging if necessary. Collision Avoidance and Congestion Management: Scheduling needs to consider paths of other robots and any stationary obstacles, avoiding potential bottlenecks and collisions. 3. Multi-Robot Coordination Swarm Intelligence: In environments with multiple AMRs, coordination and synchronization are crucial. Job scheduling should minimize instances where AMRs are idle or waiting for other robots to complete tasks. 4. Resource Constraints Battery Management: The schedule should account for battery levels, ensuring that AMRs can complete assigned jobs before needing a recharge. Station and Docking Resource Availability: In cases where AMRs have designated docking or storage points, the job scheduling may also need to include queue management for these resources. Machine Learning for Optimization: Some AMR fleets use AI-based scheduling, which learns from historical data to predict optimal task sequences and improve scheduling over time. Watch how a #Swarm of 30 AGILOX ONE help BMW Group build 1300 cars per day in their Regensburg plant. Without the need of any #fleetmanager… #AMRs #AGVs

Reinforcement Learning for Multi-Robot Task Allocation! Coordinating heterogeneous robots to complete tasks efficiently is a major challenge in robotics. Centralized scheduling methods are slow, and traditional reinforcement learning struggles with cooperation and deadlocks. This research introduces a reinforcement learning-based framework for multi-robot task allocation and scheduling, enabling decentralized agents to dynamically form teams and minimize idle time. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 1. Attention-Based Coordination: Robots learn task dependencies and adapt their schedules in real time. 2. Constrained Flash forward Mechanism: Prevents deadlocks by guiding agent decisions and improving cooperative planning. 3. Decentralized Multi-Agent RL: Scales to large problems, avoiding the bottlenecks of mixed-integer programming (MIP) solvers. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? The framework achieves near-optimal task allocation, outperforming heuristic and optimization-based methods while being 100x faster.  It successfully scales to 150 robots and 500 tasks, demonstrating real-world potential for applications like search and rescue, logistics, and industrial automation. Kudos to Weiheng DAI, Utkarsh Rai, Jimmy Chiun, Yuhong Cao, and Guillaume Sartoretti! 🔗 Read the full paper: https://lnkd.in/gSER3_-D I post the latest and interesting developments in robotics - 𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! #ReinforcementLearning #MultiRobot #TaskAllocation #AI #Robotics #Automation #DeepLearning #Optimization 

New article with Marios Nektarios Stamatopoulos on "Optimal Safety-Aware Scheduling for Multi-Agent Aerial 3D Printing with Utility Maximisation under Dependency Constraints" published in the IEEE Transactions on Automation Science and Engineering, 2026. Link: https://lnkd.in/dUhXCXcK Video: https://lnkd.in/dXpTexBK This article presents a novel coordination and task-planning framework to enable the simultaneous conflict-free collaboration of multiple unmanned aerial vehicles (UAVs) for aerial 3D printing. The proposed framework formulates an optimization problem that considers a construction mission divided into sub-tasks and a team of autonomous UAVs, along with limited volume and battery. It generates an optimal mission plan comprising task assignments and scheduling, while accounting for task dependencies arising from the geometric and structural requirements of the 3D design, inter-UAV safety constraints, material usage and total flight time of each UAV. The potential conflicts occurring during the simultaneous operation of the UAVs are addressed at a segment-level by dynamically selecting the starting time and location of each task to guarantee collision-free parallel execution. An importance prioritization is proposed to accelerate the computation by guiding the solution towards more important tasks. Additionally, a utility maximization formulation is proposed to dynamically determine the optimal number of UAVs required for a given mission, balancing the trade-off between minimizing makespan and the deployment of excess agents. The proposed framework’s effectiveness is evaluated through a Gazebo-based simulation setup, where agents are coordinated by a mission control module allocating the printing tasks based on the generated optimal scheduling plan while remaining within the material and battery constraints of each UAV. A video of the whole mission is available at the following link: https://lnkd.in/d6wFGRQf. #Robotics #AI #autonomy #constructio #3Dprinting #multirobots