NETWORK DESIGN · Strategic optimizerHexaly · Local search · LSFRP

Re-optimize a full
global shipping network
in the time it takes to run a board meeting.

Port sequences, vessel classes, speed profiles and cargo flows — all designed from scratch. Suez closure? Panama drought? Alliance under review? Network Design returns a complete, profitable network in ~60 minutes for a problem with 14M binary variables. The final word before you publish a schedule or sign an alliance.

~60 min

Full network re-solve

14M+

Binary variables

Hours

Not months

$50M+

Per bad decision

Why this matters right now

The network you ship today isn't the network you designed.

Four disruptions are forcing every carrier to redraw its service network. The slow way takes months. The cost of getting it wrong runs into tens of millions.

+10–14 days

Suez closures

Houthi attacks (2024-present) force Cape of Good Hope rerouting. Asia-Europe transit time blows out by 10–14 days per voyage.

Capacity cut

Panama Canal drought

Vessel transit restrictions reducing throughput across one of the world's most critical shipping corridors.

Lanes disrupted

Red Sea security

Asia-Europe trade lanes severely disrupted. Network designs that worked in 2023 don't work in 2026.

Rapid response

Geopolitical reconfig

Tensions across multiple corridors. Carriers need to redraw the network in days, not the months traditional planning requires.

Strategic, not operational

In operations you save 100.
In strategy you save 1,000.

Short-term schedules are operational. Network design is strategic — where corridors get re-drawn, alliances get stress-tested, and vessel classes get reassigned across an entire trade.

The final word before you publish a schedule or sign an alliance.

The stakes

$50M+

Annual cost from a single bad network decision. A wrongly-sized vessel class on the wrong corridor compounds across every voyage, every week of the year.

What this means for your network P&L

Months → hours

Network re-evaluation that used to take a quarter of manual work returns a complete plan in a single working day.

14M variables, one solver

Local search on list variables replaces brittle column generation. The problem fits in a single optimization run.

Decisions cost millions less

Bad network choices compound across thousands of voyages. A single corrected vessel-class assignment can return $50M+ a year.

Strategy + ops aligned

Strategic network design feeds straight into operational planners (OptiFleet, OptiBox). One source of truth across both horizons.

What Network Design tackles

A full global container shipping network.
From scratch. In hours.

Port sequences, vessel classes, speed profiles and cargo flows — all designed in a single solver run, at the scale of the world's largest container alliances.

400+

Vessels

15+ vessel classes (1K–24K TEU)

100+

Ports

Hub + spoke topology

~600K

TEU / week

10,000+ origin-destination pairs

14

Regions

East Asia to Latin America

100+

Services

Concurrent weekly services

35–40

Trade corridors

Distinct trade lanes to cover

Solver architecture

Hexaly local search.
Not the MIP solver everyone else runs.

Traditional Mixed-Integer Programming (Gurobi / CPLEX) on 14M binary variables takes days to converge. Local search on list variables returns a high-quality solution in minutes — and scales near-linearly.

Feature
MIP solver (Gurobi)
Hexaly
Variables
Binary matrix x[port][service][position]
list(113) per service · ordered subset
Assignment
Thousands of binary constraints
Built-in partition / disjoint operators
Search method
Branch-and-bound on LP relaxation
Intelligent local search (insert / swap / reverse)
Time to solution
Hours / Days
Minutes ✓
Optimality
Guaranteed (with enough time)
Heuristic — very good in practice
Scalability
Degrades exponentially
Near-linear with problem size ✓

Key insight: list variables replace column generation. route[k] ← list(113 ports) · vc[k] ← int(0, 16) · speed[k] ← int(0, 4)

How it works

Three phases. One pipeline.

Warm-start seeding, then network design, then cargo routing — with a feedback loop back to Phase 1 that re-balances oversupplied and undersupplied legs. Converges in 3–5 iterations.

Python

PHASE 0

TSP seed generation

  • 40 corridor pendulum templates
  • Demand-driven gap-fill seeds
  • ULCV injection for large vessels
  • ~80–100 warm-start services
Hexaly HXM

PHASE 1

Service Network Design

  • Takes warm-start seeds
  • Sub-phase 1 (60%): Geographic loop exploration
  • Sub-phase 2 (40%): Vessel-size refinement
  • 5-tier lexicographic objective
  • Outputs service definitions
Python Dijkstra

PHASE 2

Cargo routing

  • Routes 10,000+ origin-destination demands
  • Handles own-service + feeder hybrid
  • Transshipment & slot purchase
  • Per-leg utilization computed
  • Feeds back to Phase 1

Feedback loop: Oversupplied legs dampen the signal; undersupplied legs boost it. Converges in 3–5 iterations.

The objective function

Five tiers. Lexicographic priority.

Network Design doesn't blend objectives into a single weighted score — it ranks them. Feasibility before coverage. Coverage before structure. Structure before profit. Profit before utilization. The order is the design.

TIER 1

MIN

Fleet feasibility

Cannot exceed vessels owned per class or 400 total. Hard constraint.

TIER 2

MAX

Demand coverage

#1 commercial priority — route as much cargo through the network as possible.

TIER 3

MIN

Structural compliance

Min 10 services, 8 mainline (≥10K TEU), 20 feeders (≤6K TEU), corridor coverage.

TIER 4

MAX

Profitability

Revenue at $500 / TEU-leg minus fuel, charter, port costs and transshipment penalties.

TIER 5

MIN

Utilization quality

Tiebreaker — drives toward 90% headhaul, 60% backhaul utilization targets.

What-if scenarios

Stress-test the network before it ships.

Six scenario types covered out of the box. Change one input, re-solve in 5–60 minutes, compare the new network to the baseline.

Suez Canal closure

Remove PortSaid, re-solve → Cape routing across the affected services.

Panama restrictions

Reduce Panama-compatible port max TEU. Network adapts via alternative corridors.

New port opening

Add port to the network with coordinates. Re-solve picks up the new hub or spoke.

Fleet change

Modify vessel counts per class. Network rebalances around the new fleet mix.

Demand shock

Scale O-D demands (e.g. +20% Asia-Europe). The plan reflects the new commercial reality.

Fuel price change

Modify fuel price input. Vessel-class assignment and speed profiles re-optimize.

Solver budget tiers

~5 min

Quick test

1 iteration

~30 min

Standard

5 iter × 5 min

~1 hr

Production

5 iter × 10 min

Output of a single solver run

82 services. 0% rejected.
Thirty minutes.

Quick-test result on a global-alliance-scale network. Standard runs (5 iterations) deliver tighter optimality bounds in ~30 minutes. The cargo all moves; the question is just how.

82

Services deployed

85%

Of fleet utilized

0%

Cargo rejected

~30 min

Solver time

Cargo movement breakdown

Own-service
34.4%
Feeder-hybrid
59.5%
Slot purchase
6.1%
Rejected
0%

Services by tier

Mainline (≥10K TEU)
9
Mid (6K–10K TEU)
26
Feeder (≤6K TEU)
47

Network capacity: 632,000 TEU / week · avg slot cost ~$700 / TEU

What Network Design puts on the table

~60 min

Network re-solve

14M+

Binary variables

5-tier

Lexicographic objective

6

Scenario types