PULPO is a Python package for Life Cycle Optimization (LCO) based on life cycle inventories. It is designed to serve as a platform for optimization tasks of varying complexity.
The package builds on top of the Brightway LCA framework and the Pyomo optimization modeling framework.
Applying optimization is recommended when the system of study has (1) many degrees of freedom that would otherwise prompt the manual assessment of a large number of scenarios, or (2) any of the following capabilities is relevant to the goal and scope of the study:
- Specify technology and regional choices throughout the entire supply chain (fore- and background), such as the production technology of electricity or the origin of metal resources. Consistently accounting for background changes in large-scale decisions can be significant.
- Specify constraints on any activity in the life cycle inventories, interpreted as tangible limitations such as raw material availability, production capacity, or environmental regulations.
- Optimize for or constrain any impact category for which characterization factors are available.
- Specify supply values instead of final demands, which is relevant when only production volumes are known (e.g. here).
- Optimize under uncertainty via a dedicated pipeline: import and filter uncertain LCI parameters, apply uncertainty strategies, run Global Sensitivity Analysis (Sobol), perform Monte Carlo sampling, or solve Chance-Constrained programs to obtain Pareto-optimal solutions at user-defined probability levels.
Features recently completed:
ℹ️ Optimization under uncertainty [chance-constraints, Monte Carlo, global sensitivity analysis]ℹ️ Time-dependent optimization [time-indexed formulation with inter-timestep storage/carry-over]ℹ️ Development of a GUI for simple optimization tasksLinkℹ️ Enable PULPO to work on both bw2 and bw25 projectsℹ️ Thorough documentation hosted on flechtenberg.github.io/pulpo/ℹ️ Goal-programming objective (average transgression of soft impact limits)
Features currently under development:
ℹ️ Integration of economic and social indicators in the optimization problem formulation
Feature requests are more than welcome!
PULPO is available on PyPI. Depending on the version of Brightway you want to work with, install either the bw2 or bw25 variant:
pip install "pulpo-dev[bw2]"or
pip install "pulpo-dev[bw25]"Add the uncertainty extra (SALib, stats_arrays, seaborn) if you plan to use the pulpo_unc module for Monte Carlo, Chance-Constrained optimization, or Global Sensitivity Analysis:
pip install "pulpo-dev[bw25,uncertainty]"PULPO is organized into three optimizer classes, one per module, each covering a different use case with its own reference notebook:
pulpo.pulpo.PulpoOptimizer— the core LCO framework: technology/region choices, constraints, single- and multi-objective optimization (including goal programming), and supply-driven optimization. Start with the PULPO showcase notebook, a complete walkthrough built around a methanol production case.pulpo.pulpo_time.PulpoOptimizerTime— the time-indexed extension: per-timestep demands and limits, impact budgets aggregated across the horizon, and inter-timestep storage/carry-over. See the time-dependent toy notebook for hourly and daily battery-dispatch examples.pulpo.pulpo_unc.PulpoOptimizerUnc— the uncertainty extension: import and filter uncertain LCI parameters, apply gap-filling strategies, run Monte Carlo sampling, Chance-Constrained optimization, and Global Sensitivity Analysis. See the uncertainty toy notebook.
Additional example notebooks are available for a hydrogen case, an electricity case, and a plastic case.
There is also a workshop repository (here) created for the Brightcon 2024 conference, with guided notebooks and exercises.
The test suite runs with pytest against dedicated virtual environments for the modern (bw25) and legacy (bw2) Brightway stacks. See the testing README for setup instructions and the exact commands.
- Goal-programming objective —
objective='goal'minimizes the average transgression of user-defined soft impact limits (imp_goals), suited e.g. to Planetary-Boundary-based budgets. Unlike a hardupper_imp_limit, a goal can be exceeded — the solver stays feasible and reports the transgression level per category viaextract_results()["Transgressions"]. Works in bothPulpoOptimizerand the time-extendedPulpoOptimizerTime(goals apply to impacts aggregated over the whole horizon). - Hardening — several
default_limitsinteractions with goal categories, Monte Carlo re-instantiation (now forwards the fullinstantiate()signature, includingtime_steps/storage), and bw25 uncertainty (GSA under SALib 1.5/numpy 2, deterministic scaling-vector construction) were fixed. - From 1.6.1: Windows solve-hang fix & Python 3.13 support — unspecified limits are now truly infinite (no more HiGHS/pyomo deadlock), and numpy 2 / pyomo
>=6.8are supported.
See the changelog for the full details and earlier releases.
Contributions are very welcome. To request a feature or report a bug, please open an Issue. If you are confident in your coding skills, feel free to implement your suggestions and send a Pull Request.
This project is licensed under the ℹ️ BSD 3-Clause License. See the LICENSE file for additional info.
Copyright (c) 2026, Fabian Lechtenberg. All rights reserved.
We would like to express our gratitude to the authors and contributors of the following packages that PULPO builds upon:
We also acknowledge the pioneering ideas and contributions from the following works:
The development of PULPO culminated in the following publication, which details the approach and outlines its implementation:
Fabian Lechtenberg, Robert Istrate, Victor Tulus, Antonio Espuña, Moisès Graells, and Gonzalo Guillén‐Gosálbez.
“PULPO: A Framework for Efficient Integration of Life Cycle Inventory Models into Life Cycle Product Optimization.”
Journal of Industrial Ecology, October 10, 2024.
https://doi.org/10.1111/jiec.13561
Please cite this article if PULPO is used to produce results for a publication or project.
- @flechtenberg
- @robyistrate
- @vtulus
- Bartolomeus Haeussling Loewgren