Industry Signals
Quickbase Acquires Solvice: Constraint-Based Route Optimisation and Workforce Scheduling Enter a No-Code Enterprise Platform 🔗
Foundation: Solvice is an optimisation-as-a-service company whose APIs solve Vehicle Routing Problems (VRPs) and workforce scheduling problems under real-world constraints and return executable plans. Quickbase is a no-code application platform serving approximately 12,000 enterprise customers who build custom operational workflows without dedicated engineering teams. Embedding a solver API into a no-code platform means the constraint-based optimisation runs inside business applications built by operational managers, without any solver integration work.
Quickbase announced on 2 April 2026 that it had acquired Solvice, a Belgian optimisation company. Solvice's two products are OnRoute, which handles vehicle routing under vehicle capacity, crew skills, time windows, multi-day horizons, and real-time changes, and OnShift, which handles workforce scheduling under qualification constraints, working-hour limits, and workload balance requirements. Solvice's published benchmark reports up to 35% reduction in drive time for routing customers. Initial integration into Quickbase Business/Enterprise and FastField Pro is planned for H2 2026.
NVIDIA cuOpt v26.04.00: Host-Resident Problem Data, Mixed-Precision PDLP, and Remote Execution 🔗
Foundation: NVIDIA cuOpt is a GPU-accelerated optimisation engine covering mixed-integer linear programming (MILP), linear programming (LP), quadratic programming (QP), and vehicle routing. PDLP (Primal-Dual LP) is a first-order LP algorithm that scales well on GPUs by avoiding the dense factorisation that interior-point methods require. Mixed precision means using 32-bit floating-point (FP32) instead of 64-bit (FP64) arithmetic for part of the computation, which is faster on GPUs and often sufficient in practice.
NVIDIA cuOpt v26.04.00, released 9 April 2026, introduces memory model support for host-resident problem data, allowing problem instances that exceed GPU memory to be held in CPU RAM and streamed to the GPU during solve. FP32 and mixed-precision support in the PDLP solver extends GPU-native LP solving to hardware without FP64 acceleration. Remote execution via cuopt_grpc_server enables cuOpt to act as a network microservice in distributed OR pipelines. The release also includes improved lexicographic search and 30+ bug fixes for variable bounds, race conditions, and numerical stability.
| Field | Content |
|---|---|
| Version | v26.04.00 — 9 April 2026 |
| What changed |
|
| Impact | Practitioners can now deploy cuOpt on a broader range of GPU hardware (FP32-only or mixed) and integrate it as a containerised microservice. The host-memory model removes a hard problem-size ceiling for vehicle routing instances. |
| Migration | Breaking change: the solution object now includes the LP solver used -- code that inspects solution objects directly may need updating. |
Research Papers
Graph-RHO: Critical-Path-Aware Heterogeneous Graph Network for Long-Horizon Flexible Job Shop Scheduling 🔗
Long-horizon flexible job shop scheduling is a class of combinatorial problem that quickly becomes intractable for exact solvers as the number of operations grows. Prior deep reinforcement learning (DRL) approaches improve on dispatching rules but specialise to a single problem type, collapsing when transferred to a structurally related variant. Graph-RHO bridges that gap by combining a topology-aware heterogeneous graph neural network with a rolling horizon optimisation framework and a critical-path-aware training mechanism that distinguishes bottleneck operations from robust ones. The combination achieves over 30% solve time reduction on large-scale instances and generalises zero-shot across FJSP (Flexible Job Shop Scheduling Problem), JSSP (Job Shop Scheduling Problem), and FFSP (Flexible Flow Shop Problem) benchmarks, a result prior DRL schedulers do not achieve on any of those problem families simultaneously. 📋 Case Study below ↓
arXiv:2604.10073 — Li et al., Apr 2026Day-Ahead Offering for Virtual Power Plants: A Stochastic Linear Programming Reformulation with 100x Speedup over Column-and-Constraint Generation 🔗
Foundation: A Virtual Power Plant (VPP) aggregates distributed energy resources (DERs) -- rooftop solar, batteries, electric vehicles, small generators -- into a single entity that bids into the wholesale electricity market as if it were a conventional power station. The day-ahead offering problem is the daily decision of what quantity of electricity to commit to delivering at each hour of the following day, given that actual DER output will be uncertain. Stochastic adaptive robust optimisation models both distributional uncertainty (market prices following a Markov process) and set-based uncertainty (DER output ranges) in the same formulation.
Meng, Li, and Cui (arXiv:2604.01755, 2 April 2026) formulate the VPP day-ahead offering problem as a scenario-based two-stage stochastic adaptive robust optimisation program. The key insight is that the robust second-stage problem can be reformulated as a linear program with a nested resource allocation structure that admits an efficient greedy algorithm, and that the first-stage feasible region is isotonic -- enabling a projected subgradient descent algorithm to replace the standard column generationAn LP solution method that works with a small subset of variables, adding promising columns only when they improve the objective. First explained 9 Apr 2026. approach. On real-world data, the projected subgradient method achieves about two orders of magnitude speedup over the column-and-constraint generation (C&CG) baseline while maintaining solution quality.
Term of the Day
Rolling Horizon Optimisation
Rolling Horizon Optimisation (RHO) is a method for tackling long-horizon planning problems by solving a sequence of short windows rather than the whole horizon at once. At each step, you solve a problem spanning the next w periods, implement the first k decisions, slide the window forward by k periods, and re-optimise. The key mechanism: each re-solve incorporates updated information and revised forecasts that were not available at the previous step. Sounds like a complete solution to long-horizon intractability. It is not -- the decisions near the end of each window can be systematically suboptimal because the model has no information about what comes after the window closes.
A concrete example -- manufacturing scheduling
A job shop has 500 operations to sequence over a 5-day horizon. An exact solver is intractable, so the scheduler uses a rolling window of 8 hours. Without RHO awareness: the model schedules jobs optimally within the 8-hour window but treats the window boundary as if the world ends there. Jobs that start near the end of hour 8 are scheduled as if they have unlimited machine availability for their remaining operations -- because the model cannot see hours 9 onwards. The result is a schedule that looks good within the window but creates a machine bottleneck in the next window when those jobs reappear as partially complete. With RHO awareness (Graph-RHO's approach): the policy explicitly identifies which operations are on the critical path -- the sequence that determines the minimum makespan -- and biases scheduling decisions to protect those operations from the end-effect, even if they fall near the window boundary.
Where this shows up in practice
Manufacturing: Job shop and flow shop scheduling over multi-day or multi-week horizons, where exact solvers scale poorly beyond a few hours of operations. Energy: Unit commitment and economic dispatch solvers typically run in rolling 24-hour or 48-hour windows updated every hour; decisions made near the end of the window can leave the system in a state that the next window cannot efficiently recover from. Supply chain: Multi-period replenishment and production planning models that re-run nightly with a rolling forecast horizon can over-order inventory in the last few periods of each horizon because no holding cost appears beyond the horizon. Transport: Multi-day crew and vehicle scheduling uses rolling windows to handle late changes; schedules created near the boundary of each window may strand crews or vehicles at hard-to-recover positions. The first question to ask when evaluating a published rolling horizon model is: was the end-effect explicitly modelled or measured?
Upcoming Conferences
- Quickbase + Solvice Route and schedule optimisation — previously requiring dedicated engineering to deploy — is now built into a no-code business platform serving ~12,000 companies.
- cuOpt v26.04 GPU-accelerated optimisation no longer requires expensive data-centre graphics hardware — standard lower-cost GPUs now deliver comparable performance.
- Graph-RHO AI scheduling models fail on unfamiliar factory layouts because they treat every production step as equally important during training. Placing more learning weight on the bottleneck steps that gate the entire schedule eliminates this failure and improves performance by 30%+.
- VPP Day-Ahead The optimal energy bids under uncertainty always rank in a consistent order from lowest to highest value — recognising this ordering structure replaces the standard solving method with one that is 100x faster, with no loss in solution quality.