Fair Optimization and Networks: A Survey

Ogryczak, Włodzimierz; Luss, Hanan; Pióro, Michał; Nace, Dritan; Tomaszewski, Artur

doi:10.1155/2014/612018

Cited by 60 publications

(55 citation statements)

References 152 publications

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“…Let us start by introducing formally the notion of equity as discussed in this paper. The equity notion is closely related to lexicographic optimization as remarked in numerous studies such as [7,15,17,23,26,30,31]. We recall some definitions on lexicographic ordering, useful for a better understanding of the study.…”

Section: Preliminariesmentioning

confidence: 98%

Proportional and maxmin fairness for the sensor location problem with chance constraints

Santos

Luss²,

Nace

et al. 2019

Discrete Applied Mathematics

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In this paper we present a study on the Equitable Sensor Location Problem and we focus on the stochastic version of the problem where the surveying capacity of some sensors is measured as probability of intrusions detection. The Equitable Sensor Location Problem, which is an extension of the Equitable Facility Location Problem, considers installing surveying facilities as cameras/sensors in order to monitor and protect some important locations. Each location can be simultaneously protected by multiple facilities. Clearly this problem falls into the category of Maximal Coverage Location Problem and we focus on the equitable variant. The objective of the Equitable Sensor Location Problem is to provide equitable protection to all locations when the number of sensors that can be placed is limited. We study the resilient and ambiguous versions of this problem. The resilient sensor location problem considers the case when some sensors are assumed to fail partially or completely. The ambiguous version studies the case when the surveying probabilities are uncertain and represented by independent Bernouilli random variables with the corresponding ambiguity set containing the Bernouilli probability distributions. For each problem we consider two popular fairness measures which are the lexicographic optimal and proportionally fair solutions and provide an integer linear formulation together with the solution methodology. Numerical results for each studied problem are provided at the end of the paper.

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Section: Preliminariesmentioning

confidence: 98%

Proportional and maxmin fairness for the sensor location problem with chance constraints

Santos

Luss²,

Nace

et al. 2019

Discrete Applied Mathematics

Self Cite

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“…Many resource allocation rules are proposed in the literature and each of them is characterized by a set of properties that justify the use of the given rule in order to find a solution of the allocation problem [6]. In computer networks, the most well-known rules are: the proportional rule and the weighted proportional rule [13], the max-min fair allocation (MMF) [14], [15] , and the α-fair allocation [12]. Each of these allocation rules, result of an optimization problem and/or an iterative algorithm, follows a fairness criterion.…”

Section: A Classical Resource Allocation Rulesmentioning

confidence: 99%

“…The max-min fairness (MMF) allocation rule is the only feasible allocation such that, if the allocation of some users is increased, the allocation of some other users with smaller or equal amount is decreased [14], [15]. If we order the claimants according to their increasing demand, i.e., c 1 ≤ c 2 ≤ • • • ≤ c n , then MMF allocation for user i is given by:…”

Section: A Classical Resource Allocation Rulesmentioning

confidence: 99%

Fair Resource Allocation in Systems With Complete Information Sharing

Fossati¹,

Hoteit

Moretti

et al. 2018

IEEE/ACM Trans. Networking

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In networking and computing, resource allocation is typically addressed using classical resource allocation protocols as the proportional rule, the max-min fair allocation, or solutions inspired by cooperative game theory. In this paper, we argue that, under awareness about the available resource and other users demands, a cooperative setting has to be considered in order to revisit and adapt the concept of fairness. Such a complete information sharing setting is expected to happen in 5G environments, where resource sharing among tenants (slices) needs to be made acceptable by users and applications, which therefore need to be better informed about the system status via ad-hoc (northbound) interfaces than in legacy environments. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness in systems with complete information sharing and, consequently, a more appropriate resource allocation algorithm. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games and we adapt to it the fairness indices. Accordingly, we propose a new allocation rule we call Mood Value: for each user, it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and a new fairness index through extensive simulations about the cellular frequency scheduling use-case, showing how they better support the fairness analysis. We complete the paper with further analysis on the behavior of the mood value in the presence of multiple competing providers and with cheating users.

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“…If one fails to consider equity, it might be detrimental to an otherwise well-designed air traffic flow management program. Ogryczak et al (2014) provides a nice overview of the various areas of application of fairness and the most important models and methods of fair optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Fair task allocation in transportation

Zhang

Dekker

2017

Omega

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Task allocation problems have traditionally focused on cost optimization. However, more and more attention is being given to cases in which cost should not always be the sole or major consideration. In this paper we study a fair task allocation problem in transportation where an optimal allocation not only has low cost but more importantly, it distributes tasks as even as possible among heterogeneous participants who have different capacities and costs to execute tasks. To tackle this fair minimum cost allocation problem we analyze and solve it in two parts using two novel polynomial-time algorithms. We show that despite the new fairness criterion, the proposed algorithms can solve the fair minimum cost allocation problem optimally in polynomial time. In addition, we conduct an extensive set of experiments to investigate the trade-off between cost minimization and fairness. Our experimental results demonstrate the benefit of factoring fairness into task allocation. Among the majority of test instances, fairness comes with a very small price in terms of cost

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Fair Optimization and Networks: A Survey

Cited by 60 publications

References 152 publications

Proportional and maxmin fairness for the sensor location problem with chance constraints

Proportional and maxmin fairness for the sensor location problem with chance constraints

Fair Resource Allocation in Systems With Complete Information Sharing

Fair task allocation in transportation

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