Berth Optimizer Heuristic — Datasheet

Overview

Overview

Berth Optimizer (Heuristic) is the AI behind your berth-allocation desk. It takes a vessel queue, berth catalogue, crane pool and constraint pack, and returns an optimised berth-and-time plan that minimises total vessel wait while maximising berth and crane utilisation — subject to length, depth, tide, hazmat, line-priority and shift constraints. Built as a companion to the Berth Scheduling product, but works against any berth model.

The optimiser is heuristic-based (simulated annealing + genetic + local search) so it solves problems too big for pure MILP — a 14-day, 6-berth, 80-vessel plan in seconds. Planners stay in the driver's seat: every proposal is reviewable, tweak-able and explainable.

• Typical impact — vessel wait ↓30–40%, berth utilisation ↑8–15%, throughput ↑10–18%
• Constraint-aware — length, depth, tide, crane span, hazmat, gang continuity, line weights
• Explainable — every proposed move tagged with cost delta and constraint reasoning
• Reactive — re-optimises on ETA changes, breakdowns, weather closures

How It Works

1. Problem load — reads current schedule, vessel queue, berth + crane catalogue, constraint pack.
2. Cost model — weighted sum of (vessel wait) + (idle berth time) + (crane shifts) + (line-priority penalties).
3. Simulated annealing — explores broad solution space with cooling temperature.
4. Genetic crossover — takes top candidates, swaps assignment chromosomes, mutates.
5. Local search — final refinement with greedy swap-and-shift moves.
6. Tabu list — avoids cycling back to recently-tried bad assignments.
7. Publish — proposes assignments to the planner; on accept, pushes to Berth Scheduling / TOS.

Features

• Hybrid solver — SA + GA + local search + tabu, with OR-Tools MILP fallback for small problems
• Constraint pack — declarative YAML: length, depth, tide, crane, gang, hazmat, line priorities
• Multi-objective — planner sets weights for wait vs util vs cost in a slider UI
• What-if mode — A/B compare two solver runs side-by-side
• Reactive re-optimisation — auto-trigger on ETA delta, breakdown, weather window
• Explainability — every move comes with cost delta, alternatives considered, constraints satisfied
• Audit trail — full history of solver runs, parameters, planner acceptance
• Closed-loop learning — planner overrides feed back into cost weight tuning

Use Cases

• Container terminals — 4–12 berth, 30–120 vessel/week problems
• Bulk & multi-purpose ports — mixed-cargo with tight tide and bunker windows
• Port authorities — central berthing optimiser across multiple operators
• Greenfield design — what-if simulations for berth count, length, crane fleet sizing
• Disruption recovery — storm, strike or breakdown re-plans in seconds
• Decision support for planners — runs in background, proposes when better solutions found

Specifications

• Solve time: 14-day / 6-berth / 80-vessel problem <15 s on 8 CPU; 21-day / 12-berth <60 s
• Quality: typically within 1–3% of MILP optimum on tractable problems
• Inputs: JSON / REST / direct DB; live ETAs from AIS or agency feeds
• Constraints: 50+ built-in; custom Python plugin API
• Engine: Python + Cython hot loops; multi-core parallel exploration
• Deployment: Docker image, k8s helm chart, or managed SaaS
• APIs: REST + WebSocket for live re-opt; Webhook for proposal delivery
• Integrations: SGT Berth Scheduling, Navis N4, OPUS, CATOS, custom EDI

Product information

Customisation options

• Solver mode — pure heuristic (fast, large problems), MILP (small/optimal), or hybrid auto-select
• Objective weights — planner-tunable sliders for wait, utilisation, throughput, line priority, cost
• Constraint pack — standard, extended (tide / bunker / hazmat), or fully bespoke YAML rules
• Re-optimisation triggers — manual only, on ETA changes, scheduled cron, or event-driven
• Acceptance workflow — auto-publish, planner-approve, or two-person sign-off for major shifts
• Integration target — SGT Berth Scheduling, Navis N4, OPUS, CATOS, or custom REST / EDI bridge
• Solver hosting — SaaS, your private cloud, or on-prem with dedicated solver worker
• Compute tier — 4 / 8 / 16 CPU worker pools; auto-scale on demand

Ordering & lead time

Standard lead time depends on current stock. For volume orders, please contact our team for a structured quote including BOQ, freight, installation and warranty terms.

Support

Engineering support, installation guidance and warranty claims are handled by our customer success team. Reach us at support@sgtsystems.com or via the channels listed on our Contact page.