RinSim
Open-source Java simulator for dynamic pickup-and-delivery problems
RinSim is a solid, research-grade simulator for PDP and related problems. Its focus on reproducibility and modular design makes it a standard choice in academic settings, but it lacks modern APIs and integrations for commercial deployment. For scientific simulation experiments, it is a reliable open-source option; for production logistics, consider alternatives like Google OR-Tools or commercial routing APIs.
- Researchers studying logistics algorithms and dynamic PDP
- Educators teaching multi-agent systems and vehicle routing
- Students conducting reproducible simulation experiments
- Developers implementing and comparing centralized/decentralized PDP solvers
- Production-scale logistics operations requiring real-time data feeds
- Non-researchers seeking a plug-and-play routing solution
- Users needing cloud-hosted SaaS or web-based deployment
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Skip RinSim if you need a cloud-hosted routing API or real-time traffic integration for production logistics.
There are no hidden costs; RinSim is fully open-source under Apache 2.0 license.
RinSim is free (Apache 2.0), making it an excellent choice for academic labs on a budget. Commercial routing APIs (e.g., Google Maps, Routific) charge per request; OR-Tools is also free but focuses more on optimization. RinSim's simulation-specific features give it an edge for controlled experiments over generic optimization libraries.
In short
RinSim — Open-source Java simulator for dynamic pickup-and-delivery problems. Best for Researchers studying logistics algorithms and dynamic PDP, Educators teaching multi-agent systems and vehicle routing, Students conducting reproducible simulation experiments. Free to start; paid plans from $2/mo.
Viability Score
How likely is RinSim to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Discrete time simulation with real-time and simulated time
- Explicit separation of problem and solution
- Flexible graphical user interface
- Experiment framework for factorial designs
- Distributed computing via JPPF
- Statistics gathering capabilities
- Save and load problem scenarios as event sequences
- Scenario generation API with probability distributions
- Centralized and decentralized algorithm support
- Dynamic/static pickup-and-delivery problems
- Dynamic/static vehicle routing problems
- Dynamic/static multi-agent route planning
- Modular and configurable architecture
- Maven Central distribution
- Java 7+ compatibility
About RinSim
RinSim is an extensible logistics simulator developed at imec-DistriNet, KU Leuven, Belgium. It supports centralized and decentralized algorithms for dynamic pickup-and-delivery problems (PDP), vehicle routing, and multi-agent route planning. The simulator operates on a discrete-time model with support for both real-time and simulated time. It provides explicit separation of problem and solution, a flexible GUI, and an experiment framework for factorial experiments, including distributed computing via JPPF. Users can gather statistics, save/load problem scenarios, and create scenarios using probability distributions. RinSim is thoroughly tested, well-documented, and modular. It requires Java 7 or higher and is available on Maven Central. The focus on scientific correctness and reproducibility makes it ideal for academic experiments. Unlike commercial simulators or cloud-based routing APIs, RinSim is fully open-source and tailored for reproducible research. It lacks modern web interfaces or real-time traffic data, but for controlled simulation studies it remains a standard choice in academia.
Behind the Verdict
RinSim excels in controlled simulation studies due to its discrete-time model, explicit separation of problem and solution, and built-in experiment framework for factorial designs. The ability to run distributed experiments via JPPF is a standout for researchers needing to scale compute. The simulator's Java-only codebase and lack of web interface or real-time data integration limit its appeal for modern commercial logistics operations. Community activity is low (last site update 2018), so expect limited support beyond GitHub issues. For reproducible academic research, RinSim is well-suited; for teams needing a production routing engine or cloud-based API, alternative tools like OR-Tools or Routific will be more practical.
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Real-world workflow fit
Concrete scenarios for the personas RinSim actually fits — and what changes day-one when you adopt it.
Wants to benchmark a new decentralized PDP algorithm against known baselines.
Outcome: Sets up a factorial experiment using RinSim's experiment framework, runs simulations on a cluster via JPPF, and gathers statistics for publication.
Needs a hands-on tool to teach multi-agent route planning.
Outcome: Uses RinSim's GUI to demonstrate centralized vs decentralized routing on the same problem scenario, helping students grasp concepts visually.
Wants to simulate AGV routing strategies before deploying in a real facility.
Outcome: Models the warehouse layout and pick-up/delivery points in RinSim, runs simulations with different algorithms, evaluates performance metrics, and selects the best strategy.
Use Cases
- Simulate dynamic pickup-and-delivery scenarios for algorithm benchmarking
- Compare centralized vs decentralized vehicle routing strategies
- Generate reproducible experimental data for academic publications
- Teach multi-agent system concepts with a hands-on simulator
- Optimize AGV routing in warehousing experiments
- Evaluate fleet coordination algorithms under dynamic conditions
Limitations
- No hosted service or API; requires Java development skills.
- Community support is limited to GitHub issues and Stack Overflow.
- The site last generated in 2018 suggests infrequent updates.
- No real-time traffic data or GPS integration.
as of 2026-07-18
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published RinSim tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0 (Apache 2.0)
Ideal for
Academic researchers and educators needing a free, reproducible simulation platform for logistics algorithms.
What this tier adds
Starting tier: free, full-featured simulator with Apache 2.0 license, no paid upgrades.
Where the pricing makes sense
The company stage and team size where RinSim's pricing actually pencils out — and where peers do it cheaper.
RinSim is free (Apache 2.0), making it an excellent choice for academic labs on a budget. Commercial routing APIs (e.g., Google Maps, Routific) charge per request; OR-Tools is also free but focuses more on optimization. RinSim's simulation-specific features give it an edge for controlled experiments over generic optimization libraries.
Setup time & first value
How long it actually takes to get something useful out of RinSim — broken out by persona, not the marketing-page minute.
A Java developer familiar with Maven can set up a RinSim project in under an hour by adding the Maven dependency and running the provided examples. For non-developers, learning Java basics and the RinSim API may take a few days.
Switching to or from RinSim
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Google OR-Tools: Port your PDP models to RinSim's event-based scenarios and utilize the built-in experiment framework for simulation-specific analysis.
- ↗To OR-Tools: If you need production-level optimization without simulation, export your problem parameters and use OR-Tools' Java bindings.
Integrations
Resources & Guides
Official links
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Common stack mates teams adopt alongside RinSim, with the specific reason each pairing earns its keep.
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