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  • 1
    Publication Date: 2020-08-05
    Description: Sports rankings are obtained by applying a system of rules to evaluate the performance of the participants in a competition. We consider rankings that result from assigning an ordinal rank to each competitor according to their performance. We develop an integer programming model for rankings that allows us to calculate the number of points needed to guarantee a team the ith position, as well as the minimum number of points that could yield the ith place. The model is very general and can thus be applied to many types of sports. We discuss examples coming from football (soccer), ice hockey, and Formula~1. We answer various questions and debunk a few myths along the way. Are 40 points enough to avoid relegation in the Bundesliga? Do 95 points guarantee the participation of a team in the NHL playoffs? Moreover, in the season restructuration currently under consideration in the NHL, will it be easier or harder to access the playoffs? Is it possible to win the Formula~1 World Championship without winning at least one race or without even climbing once on the podium? Finally, we observe that the optimal solutions of the aforementioned model are associated to extreme situations which are unlikely to happen. Thus, to get closer to realistic scenarios, we enhance the model by adding some constraints inferred from the results of the previous years.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 2
    Publication Date: 2020-08-05
    Description: The Frankl conjecture, also known as the union-closed sets conjecture, states that there exists an element in at least half of the sets of any (non-empty) union-closed family. From an optimization point of view, one could instead prove that 2a is an upper bound to the number of sets in a union-closed family with n elements where each element is in at most a sets, where a and n are non-negative integers. Formulating these problems as integer programs we observe that computed optimal values do not vary with n. We formalize these observations as conjectures, and show that they are not equivalent to the Frankl conjecture while still having wide-reaching implications if proven true. Finally, we partially prove the new conjectures and discuss possible approaches to solve them completely.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 3
    Publication Date: 2020-08-05
    Description: A central assumption in classical optimization is that all the input data of a problem are exact. However, in many real-world problems, the input data are subject to uncertainty. In such situations, neglecting uncertainty may lead to nominally optimal solutions that are actually suboptimal or even infeasible. Robust optimization offers a remedy for optimization under uncertainty by considering only the subset of solutions protected against the data deviations. In this paper, we provide an overview of the main theoretical results of multiband robustness, a new robust optimization model that extends and refines the classical theory introduced by Bertsimas and Sim. After introducing some new results for the special case of pure binary programs, we focus on the harvest scheduling problem and show how multiband robustness can be adopted to tackle the uncertainty affecting the volume of produced timber and grant a reduction in the price of robustness.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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  • 4
    Publication Date: 2020-08-05
    Language: English
    Type: article , doc-type:article
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  • 5
    Publication Date: 2020-08-05
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 6
    Publication Date: 2020-08-05
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 7
    Publication Date: 2020-08-05
    Language: English
    Type: conferenceobject , doc-type:conferenceObject
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  • 8
    Publication Date: 2020-08-05
    Description: We provide an overview of new theoretical results that we obtained while further investigating multiband robust optimization, a new model for robust optimization that we recently proposed to tackle uncertainty in mixed-integer linear programming. This new model extends and refines the classical Gamma-robustness model of Bertsimas and Sim and is particularly useful in the common case of arbitrary asymmetric distributions of the uncertainty. Here, we focus on uncertain 0-1 programs and we analyze their robust counterparts when the uncertainty is represented through a multiband set. Our investigations were inspired by the needs of our industrial partners in the research project ROBUKOM.
    Language: English
    Type: reportzib , doc-type:preprint
    Format: application/pdf
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