kayros 0.3.0

KAYROS — exact & anytime solver for duration-minimization time-dependent vehicle routing (TDVRPTW / TDVRP)

KAYROS

KAYROS is an exact & anytime solver for duration-minimization time-dependent vehicle routing problems — TDVRPTW (with time windows) and TDVRP — benchmarked on the canonical MAMUT-routing TD instance families.

The name is a nod to Kairos, the ancient Greek notion of the right, opportune moment, fitting for a time-dependent solver where when each route departs is itself a decision. It is also a recursive acronym: Kayros Anytime-Yielding Routing Optimization Solver.

Status: alpha, under active development as part of a PhD. v0.3.0 ships both solving modes: the anytime heuristic stack (TD-ACO + time-dependent local search), which produced the large majority of the MAMUT store's best-known solutions, and the exact branch-price-and-cut component (kayros.lera), which certified 334 of them optimal.

Install

pip install kayros

This pulls everything, including the benchmark loaders and the reference checker (mamut-routing-lib). For development:

git clone https://github.com/0nyr/kayros && cd kayros
pip install -e . --group dev    # pip >= 25.1 (or: uv pip install -e . --group dev)

Requirements: Python ≥ 3.11. Building from source (sdist or checkout) additionally needs a C++23 compiler, CMake ≥ 3.26 (fetched automatically by the build backend when missing) and Boost.Graph headers+library; the HiGHS LP solver is fetched and built statically by CMake when no install is found.

Usage

Anytime heuristic solve — construction + ant colony + local search, streaming every new incumbent:

import kayros

# Any MAMUT-routing TD instance (.vrp.json with its .atf.json sidecar next to it)
instance_path = "benchmarks/TDVRPTW/Dabia2013/n=25/C101.vrp.json"
solution = kayros.solve(instance_path, time_limit=10.0, seed=42)
print(solution.duration, solution.num_routes, solution.status)

# Anytime: react to every new incumbent while the solve keeps running
def on_incumbent(incumbent, routes):
    print(f"[{incumbent.seconds:7.2f}s] {incumbent.value:.6f} ({incumbent.origin})")

solution = kayros.solve(instance_path, time_limit=60.0, on_incumbent=on_incumbent)

Exact solve — branch-price-and-cut with checker-exact certificates, optionally warm-started from a known solution (the fast path when certifying near-optimal solutions, e.g. stored best-known ones):

from mamut_routing_lib.td import load_td_instance
from kayros.lera import solve_duration

loaded = load_td_instance(instance_path)
result = solve_duration(loaded, time_limit_s=600.0,
                        initial_routes=[list(r) for r in solution.routes])
print(result["exact_log"]["status"], result["value"])
# status == "Optimum" with routes == [] means: the warm-start solution itself
# is proven optimal. On a time limit, result["exact_log"]["best_bound"] is a
# valid global lower bound when the root relaxation finished (absent otherwise).

solution.duration and result["value"] are always values computed by the reference checker (mamut_routing_lib.td.check_td_solution) — never an internal approximation.

Design

KAYROS is two solving modes on one exact time-dependent engine:

• The engine (cpp/pwlf, cpp/core) represents arrival times as non-decreasing continuous piecewise-linear functions (NDCPWLF) and evaluates routes by exact function composition — a bit-identical C++ port of the reference checker's arithmetic (gated by an equivalence suite over the full benchmark set).
• The anytime stack (kayros.solve): greedy construction, a MAX-MIN TD ant colony, and a time-dependent local-search layer using LCA-BST move evaluation (Blauth et al. 2024) — tree-ranked relocate/swap/2-opt* where every accepted move is repriced by the checker-identical fold before it counts.
• The exact component (kayros.lera): the branch-price-and-cut solver of Lera-Romero, Rönnqvist & Ljungqvist (2020), vendored under cpp/lera/ (see its NOTICE.md) on the open-source HiGHS LP backend, extended with deadline-compliant anytime behavior, warm starts through columns, TDVRP support, and honest time-limit gap reporting. Every column entering the master problem is repriced in the checker's arithmetic, so reported values — and optimality certificates — are checker-exact: optimal under checker-exact route costs and standard LP/pricing tolerances, completeness modulo the search engine's epsilon dominance.

Core principles

• The checker is the referee. Every solution and every certificate is priced by the reference checker of mamut-routing-lib; the checker's value is the value.
• Exact arithmetic. Plain IEEE-754 doubles, no epsilon comparisons in the engine, no FMA contraction (-ffp-contract=off); results are bit-reproducible across machines.
• Anytime first. Time budgets are hard deadlines honored by every component (heuristics and exact search alike), and incumbents stream out as they are found — a solver that only answers at the end is not a solver you can interrupt.
• One-command install, no proprietary dependency. The default build — including the exact component — is pure open source; HiGHS is built statically into the wheels. The faster CPLEX backend for the BPC remains strictly a source-build opt-in (-DLERA_LP_BACKEND=cplex) and never ships in wheels.
• One run is one thread. No intra-run parallelism; parallelism belongs to the experiment layer above.
• POD core. The fresh C++ is plain structs, flat arrays and free functions — optimization-kernel style, no framework (the vendored BPC keeps its upstream style, contained under cpp/lera/).

Archival and reproducibility

kayros is archived by Software Heritage; the badge above tracks the archive status of the GitHub origin (archived origin, archival visits). For academic referencing, prefer Software Heritage identifiers (SWHIDs) of the exact archived revision or release tag over the moving repository origin — when reporting computational results, cite both the kayros release used and the MAMUT-routing benchmark artifacts it was run on.

Provenance

KAYROS is developed by Florian Rascoussier (Onyr) as part of a PhD in operations research (IMT Atlantique / INSA Lyon), under the supervision of Romain Billot, Christine Solnon and Lina Fahed. The NDCPWLF composition engine follows Visser & Spliet (2020)'s move-evaluation theorems; the local-search move evaluation follows Blauth et al. (2024); the exact component vendors the branch-price-and-cut solver of Lera-Romero, Rönnqvist & Ljungqvist (2020, MIT-licensed — provenance and local modifications documented in cpp/lera/NOTICE.md); the TD-ACO is a rewrite of the author's heuristic layer originally built on that same solver. MIT license.