Gurobi Optimization Revenue

[Beronike Watkin]

GurobiOptimization is in the business of helping companies solve their hardest problems by providing the best optimization solver possible, with outstanding support, and no surprises pricing. Founded by arguably the most experienced and respected team in optimization today, Gurobi is rapidly growing as more and more companies see the benefits of working with a partner so focused on providing the best solver and the best support possible.With regards to performance, independent benchmarks consistently show Gurobi finding both feasible and proven optimal solutions faster than competing solvers. This is possible in part because we aren't saddled with legacy code. We've built each of our solvers from the ground up using the latest algorithmic developments and implementation techniques. You can see the results in Gurobi performance, particularly on larger, more difficult models. In fact, Gurobi recently solved 11 models in the MIPLIB2010 library that had not previously been solved by any other solver. In addition, since reliability is just as important as performance, every feature and version is fully tested against literally thousands of real-world instances, including both standard public benchmark test sets and extremely challenging, large-scale, industrial models from our customers.Visit www.gurobi.com to learn more, view flexible licensing, download a free trial, and/or request a free, no size limit, evaluation copy.

Gurobi Optimization, LLC (Gurobi), provider of the leading math programming solver, announced that Thompson Street Capital Partners (TSCP), a private equity firm based in St. Louis, has made a significant financial investment in Gurobi to help it to continue its growth as a leader in the rapidly ...

Gurobi Optimization, LLC, a Houston-based provider of an optimization platform for the the software industry, has been recapitalized by Thompson Street Capital Partners.The terms of the transaction were not disclosed.Led by Ed Rothberg, CEO and co-founder, Gurobi has developed a suite of opt ...

I am trying to implement a piecewise-linear objective in a gurobi optimization problem in python. When trying to get familiar with this method using the example from the gurobi web-site ( -scheduling/)

The mentioned example optimizes the profit from the production of several goods with limited production capacity, especially a limited amount of working hours of the staff. However, the latter constraint is not hard, but, for some bonus payment, the possible work-hours can be expanded (this is the piecewise-linear function). In the general problem formulation, the problem looks like this:

, where r depicts the specific revenues for good i" and x the share of this good, while the cost term provides potential extra cost for overtime.However, in the actual code, the objective misses the cost term, at least according to my understanding:

Are PWLObj always substracted from the main objective? Is there a way to e.g. multiply the PWLObj to the main objective function?I hope that I not just missed some trivial point (I am still pretty new to programming and optimizing alike). Thanks a lot for your time and support!

I am writing an optimization model in python, using Gurobi, with the objective to maximize the revenue of a solar power plant by using a battery storage. My goal is to optimize when I will sell the produced energy immediately to the power market or store it in a battery storage and sell it at a later hour when the market price is higher. However, after running my model and analyzing the results I have noticed that the model's solution is unconventional.

For example, in hour 7 I have a high price of 104.54 EUR and my battery is filled with 1 MWh. Instead of selling the whole 1 MWh in that hour, the model decided to sell only 0.5 MWh for that price. Then in the next hour when the prices is lower (103.39 EUR) sells additional 0.25. Then at next hour sells 0.125 MWh at price 95 EUR, the next hour 0.0625 MWh at price 80.15 EUR. I can't figure out what constraint causes a result like this unfortunately. So I was wondering if someone could maybe pinpoint where the problem is?

I went through all my constraints multiple times and I can't figure out why is this happening. Also tried to force my model to discharge the battery less often by subtracting the binary value (1 when the battery is discharging) from the optimization criteria, it just resulted in everlasting calculation, which I had to interrupt.

I am always looking the values at the end of the hour, so naturally at the end of hour 7, if the battery was discharged for 1MWh, my battery_status would have been 0MWh, which clearly breaks the constraint above.

Pasco Shikishima, a leading Japanese food company ($1.5 billion in annual revenue, 4,000 employees), has chosen Hexaly Optimizer to solve a vast, global supply chain optimization problem. Hexaly won the benchmark against Gurobi, Cplex, and Xpress. The software solution was developed by the optimization experts of the service company Future Architect, with support from MSI, a reseller of Hexaly in Japan, and the Hexaly team.

We developed the Pasco supply chain optimization model using Hexaly Optimizer within a few days of work. Hexaly Optimizer provides outstanding solutions, as considered by Pasco planners, in just a few minutes of running times, while the number of variables is gigantic (tens of millions). We could not imagine such a feat being possible initially since state-of-the-art MIP solvers like CPLEX, Xpress, and Gurobi could not tackle the problem in hours. Now we know that using Hexaly, it's possible!

Airport gate assignment is an important factor in airline operations. This presentation introduces a model for determining whether a schedule can be gated at key stations. It assesses core gating considerations, including towing, as well as bespoke constraints like remote parking restrictions. When a full gating solution is infeasible, results are then used as a basis for retiming recommendations. By providing a quick spot check for gate assignment feasibility, the model helps integrate gating information throughout the network planning pipeline.

In January of 2023, a new center called ATL@GT was established at Georgia Tech under the direction of Laurie Garrow. The mission of ATL@GT is to lead research and education activities related to airline revenue management. One of the key activities of the center is the development of a competitive revenue management simulator that can be used to test different business strategies. The competitive RM simulator, called PassengerSim, is being designed with a highly flexible architecture that will enable students, researchers, and industry partners to write customized code to test out their own ideas while interfacing with the core simulator. In this presentation we will give an overview of our progress developing PassengerSim. Given our interest in using the simulator to explore both revenue management and scheduling planning applications, we present our vision for incorporating and calibrating passenger schedule preferences into the simulator and look forward to receiving audience feedback!

Airline scheduling for large networks poses significant challenges due to numerous variables and constraints. In addition to optimizing profitability and operational feasibility, airlines prioritize crew productivity as a secondary objective. Effective crew scheduling is pivotal for operational efficiency and crew satisfaction. Our proposed approach incorporates crew-friendly considerations into scheduling while managing other objectives with minimal complexity increase, thereby delivering promising crew outcomes without compromising operational goals. Factors such as duty time regulations, rest intervals, and crew pairing preferences are integrated to enhance crew well-being and performance. Although the exact formulation, while available, is not scalable for large networks, we employ approximations to model crew-friendly factors. We illustrate this approach using scheduling data from a leading European carrier.

As we develop a replacement for the Passenger Origin-Destination Simulator (PODS), we study the impact of passenger preferences for schedule on the performance of advanced revenue management (RM) methods. At the core of PODS sits the Boeing Decision Window model that is used to represent passenger preferences for schedule including preferred departure and arrival times and the attractiveness of shorter itineraries. We show that disabling these preferences shifts revenues from airlines with a superior schedule (more nonstops, shorter connections) to carriers that offer less convenient itineraries. Without modeling these preferences, simulation results substantially under-estimate the benefits of advanced RM algorithms such as network O-D control. A proper representation of passenger preferences for schedule will then be instrumental to provide a realistic environment to evaluate the performance of RM algorithms in the new simulator.

The impact of airport congestion on aircraft upgauging is frequently contested, as there is considerable debate as to what extent this trend is driven by economic incentives versus airport slot constraints. As more airports are forecast to reach capacity limits in the future, there is a need to better understand this phenomenon. By analyzing flight data, airport capacity, and airline scheduling practices, we aim to dissociate between the two drivers in order to understand their relative contributions. In doing so, we gain insights into how this effect could be incorporated into forecast models, in order to better anticipate aircraft size requirements.

In the complex world of airline scheduling, achieving optimal flight schedules is crucial yet challenging and often time-consuming. This presentation introduces an optimization model developed to tackle this challenge for a hub-and-spoke airline. The model attempts to optimize flight itineraries, considering key operational constraints such as block times, minimum ground times, maintenance requirements, slots, airport curfews, minimum connecting time at the hub, and flight separation criteria to avoid hub congestion. It focuses on maximizing network revenue by enhancing flight connectivity within banks, considering that not all arrivals can connect with all departures in each bank. Attendees will learn about the model's formulation, constraint integration, and the practical implications of its implementation in network planning and scheduling.

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