Original title: Efficient Algorithms for Energy-Aware Single-Machine Scheduling with Battery Storage
Luca Forte, Stijn De Vuyst, Pieter Leyman · September 2026
Factories want to cut electricity costs and carbon emissions. Power prices change throughout the day under variable rates, which makes planning difficult. Adding a large battery helps save money, but it makes scheduling much harder. Running machines and controlling the battery depend on each other. The researchers studied how to run a set of jobs on one machine within a fixed time horizon for the lowest power cost. Under their setup, power prices follow a Time-of-Use tariff, meaning prices shift over time. The battery charges from the electrical grid and discharges during high-price hours to lower expenses. To solve this tricky puzzle, the authors created and tested two matheuristic algorithms, which are hybrid problem-solving methods, to coordinate machine jobs and battery usage together.
You are planning when to run your dishwasher and clothes dryer over a day. Electricity costs more from 4 to 7 PM, so you want to avoid those hours. You also have a power bank that can store cheap electricity from the morning and power your appliances later. But deciding when to run each appliance changes how much power you need from the bank, and how you charge the bank changes which hours are best for the appliances. In this paper, the jobs are like the appliances, the machine is your home, and the battery is the power bank. The algorithms decide both the appliance schedule and the charging schedule together to minimize your electric bill.
Factories pay for electricity at different rates throughout the day. By using a battery to store cheap energy and discharge it during peak hours, they can cut costs and reduce emissions. But coordinating when to run jobs and when to use the battery is complex. This work provides efficient algorithms to make that coordination practical, helping manufacturers save money and operate greener.
The abstract does not state limitations explicitly. The work focuses on a single machine with a fixed time horizon and deterministic electricity prices. It does not address multiple machines, stochastic prices, battery degradation, or real-world uncertainties. The proposed algorithms are matheuristic, meaning they are not guaranteed to find the absolute optimal solution.
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Efficient Algorithms for Energy-Aware Single-Machine Scheduling with Battery Storage — Luca Forte, Stijn De Vuyst, Pieter Leyman
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.