Reference Point Method with Importance Weighted Partial Achievements, Journal of Telekommunications and Information Technology, 2008, nr 4

The reference point method (RPM) is based on the so-called augmented max-min aggregation where the worst individual achievement maximization process is additionally regularized with the average achievement. In order to avoid inconsistencies caused by the regularization, we replace it with the ordered weighted average (OWA) which combines all the individual achievements allocating the largest weight to the worst achievement, the second largest weight to the second worst achievement, and so on. Further following the concept of the weighted OWA (WOWA) we incorporate the importance weighting of several achievements into the RPM. Such a WOWA RPM approach uses importance weights to affect achievement importance by rescaling accordingly its measure within the distribution of achievements rather than by straightforward rescaling of achievement values. The recent progress in optimization methods for ordered averages allows us to implement the WOWA RPM quite effectively as extension of the original constraints and criteria with simple linear inequalities

Decision Support under Risk by Optimization of Scenario Importance Weighted OWA Aggregations, Journal of Telecommunications and Information Technology, 2009, nr 3

The problem of evaluation outcomes under several scenarios to form overall objective functions is of considerable importance in decision support under uncertainty. The fuzzy operator defined as the so-called weighted OWA (WOWA) aggregation offers a well-suited approach to this problem. TheWOWA aggregation, similar to the classical ordered weighted averaging (OWA), uses the preferential weights assigned to the ordered values (i.e., to the worst value, the second worst and so on) rather than to the specific criteria. This allows one to model various preferences with respect to the risk. Simultaneously, importance weighting of scenarios can be introduced. In this paper we analyze solution procedures for optimization problems with the WOWA objective functions related to decisions under risk. Linear programming formulations are introduced for optimization of the WOWA objective representing risk averse preferences. Their computational efficiency is demonstrated

Reference distribution based decision support platform, Journal of Telekommunications and Information Technology, 2008, nr 3

There many decision problems where numerous partial achievement functions are considered impartially which makes the distribution of achievements more important than the assignment of several achievements to the specific criteria. Such models are generally related to the evaluation and optimization of various systems which serve many users where quality of service for every individual user defines the criteria. This applies to various technical systems, like to telecommunication ones among others, as well as to social systems. An example arises in location theory, where the clients of a system are entitled to equal treatment according to some community regulations. This paper presents an implementation of decision support framework for such problems. This platform is designed for multiple criteria problems analyzed with the reference distribution approach. Reference distribution approach is an extension of the reference point method

On equitable approaches to resource allocation problems: the conditional minimax solutions, Journal of Telecommunications and Information Technology, 2002, nr 3

In this paper we introduce and analyze a solution concept of the conditional minimax as a generalization of the minimax solution concept extended to take into account the number of services (the portion of demand) related to the worst performances. Namely, for a specified portion of demand we take into account the corresponding portion of the maximum results and we consider their average as the worst conditional mean to be minimized. We show that, similar to the standard minimax approach, the minimization of the worst conditional mean can be defined by a linear objective and a number of auxiliary linear inequalities. We report some results of initial computational experience with the new solution concept

Comparison of Selected Fair-optimization Methods for Flow Maximization between Given Pairs of Nodes in Telecommunications Network, Journal of Telecommunications and Information Technology, 2016, nr 3

Dimensioning of telecommunications networks requires the allocation of the ows (bandwidth) to given trac demands for the source-destination pairs of nodes. Unit ow allocated to the given demand is associated with revenue that may vary for di erent demands. Problem the decision-making basic algorithms to maximize the total revenue may lead to the solutions that are unacceptable, due to "starvation" or "locking" of some demand paths less attractive with respect to the total revenue. Therefore, the fair optimization approaches must be applied. In this paper, two fair optimization methods are analyzed: the method of ordered weighted average (OWA) and the reference point method (RPM). The study assumes that ows can be bifurcated thus realized in multiple path schemes. To implement optimization model the AMPL was used with general-purpose linear programming solvers. As an example of the data, the Polish backbone network was used

Network Dimensioning with Maximum Revenue Efficiency for the Fairness Index, Journal of Telecommunications and Information Technology, 2016, nr 4

Network dimensioning is a specific kind of the resource allocation problem. One of the tasks in the network optimization is to maximize the total flow on given pairs of nodes (so-called demands or paths between source and target). The task can be more complicated when different revenue/profit gained from each unit of traffic stream allocated on each demand is taken into account. When the total revenue is maximized the problem of starvation of less attractive paths can appear. Therefore, it is important to include some fairness criteria to preserve connections between all the demands on a given degree of quality, also for the least attractive paths. In this paper, a new bicriteria ratio optimization method which takes into account both, the revenue and the fairness is proposed. Mathematical model is built in a form of linear programming. The solutions are analyzed with some statistical measures to evaluate their quality, with respect to fairness and efficiency. In particular, the Gini’s coefficient is used for this purpose

On MILP Models for the OWA Optimization, Journal of Telecommunications and Information Technology, 2012, nr 2

The problem of aggregating multiple outcomes to form overall objective functions is of considerable importance in many applications. The ordered weighted averaging (OWA) aggregation uses the weights assigned to the ordered values (i.e., to the largest value, the second largest and so on) rather than to the specific coordinates. It allows to evaluate solutions impartially, when distribution of outcomes is more important than assignments these outcomes to the specific criteria. This applies to systems with multiple independent users or agents, whose objectives correspond to the criteria. The ordering operator causes that the OWA optimization problem is nonlinear. Several MILP models have been developed for the OWA optimization. They are built with different numbers of binary variables and auxiliary constraints. In this paper we analyze and compare computational performances of the different MILP model formulations

Fair resource allocation schemes and network dimensioning problems, Journal of Telecommunications and Information Technology, 2003, nr 3

Resource allocation problems are concerned with the allocation of limited resources among competing activities so as to achieve the best overall performances of the system but providing fair treatment of all the competitors. Telecommunication networks are facing the increasing demand for Internet services. Therefore, a problem of network dimensioning with elastic traffic arises which requires to allocate bandwidth to maximize service flows with fair treatment of all the services. In such applications, the so-called max-min fairness (MMF) solution concept is widely used to formulate the resource allocation scheme. This guarantees the fairness but may lead to significant losses in the overall throughput of the network. In this paper we show how multiple criteria optimization concepts can be used to generate various fair resource allocation schemes. The solution concepts are tested on the network dimensioning problem and their abilities to model various preferences are demonstrated