Use case
Buyer selection and service pricing in an electric fleet supply chain
This study focuses on the correct dimensioning of infrastructures in the determination of charging rates and the resolution of a mixed-integer nonlinear programming. The aim is to evaluate the impact of the combined parameters of the problem, and to describe a modeling framework and its managerial implications for taxicabs selection and pricing contracts.

As a part of the initiative to improve local air quality, cities have encouraged fleets of vehicles, in particular of taxicabs, to adopt alternative technologies like electric vehicles (EVs).

In this context, operators of EV charging infrastructure need to dimension their infrastructure and design charging tariffs. On their side, each taxicab company has to decide whether to pay for these services or not to adopt EVs.

The resulting problem can be modeled using mixed-integer nonlinear programming (MINLP). Thanks to its performance, Artelys Knitro was chosen among other off-the-shelf optimization solvers. It allows the authors of this article to derive insights on how problem parameters, such as service cost, taxicab companies’ fleet size and miles-driven, inconvenience cost for recharging EV batteries, impact the service provider’s profits and the participating set of taxicab companies.


Start with a tutorial!


You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.

Free trial


Get your trial license to test Artelys Knitro’s performances on your own mathematical optimization problem. The trial package includes free support and maintenance. You can have access to Artelys Knitro for free with a 1-month unlimited version or a 6-month limited version.

Artelys Knitro has unmatched performance

Best Nonlinear Solver

Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.

Technical support

The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.

Updates and new features

The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.

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