Assignment Problem

This paper presents an algorithm, called the Backwards Incremental System Optimum Search (BISOS) for achieving system near-optimum traffic assignment by incrementally limiting accessibility of roads for a chosen set of agents. The described algorithm redistributes traffic volumes homogeneously around the city and converges significantly faster than existing methods for system optimum computation in current literature. Furthermore, as previous methods have mainly been developed for theoretical purposes, the solutions provided by them do not contain all the necessary information for a practical implementation such as explicit paths for the commuting population. In contrast, the BISOS algorithm preserves the information about the exact paths of all commuters, throughout the whole process of computing the system optimum assignment. Furthermore, a realistic traffic scenario is simulated using Singapore as a case study by utilizing survey and GPS traffic data. The BISOS routing method needs 15 times less routing computations to get within 1% of the optimal solution for a simulated scenario compared to conventional methods for system optimum computation.

This paper presents an algorithm, called the Backwards Incremental System Optimum Search (BISOS) for achieving system near-optimum traffic assignment by incrementally limiting accessibility of roads for a chosen set of agents. The described algorithm redistributes traffic volumes homogeneously around the city and converges significantly faster than existing methods for system optimum computation in current literature. Furthermore, as previous methods have mainly been developed for theoretical purposes, the solutions provided by them do not contain all the necessary information for a practical implementation such as explicit paths for the commuting population. In contrast, the BISOS algorithm preserves the information about the exact paths of all commuters, throughout the whole process of computing the system optimum assignment. Furthermore, a realistic traffic scenario is simulated using Singapore as a case study by utilizing survey and GPS traffic data. The BISOS routing method needs 15 times less routing computations to get within 1% of the optimal solution for a simulated scenario compared to conventional methods for system optimum computation.

We consider an urban network with two traffic modes. We control the traffic lights in order to free the roads used by the public transport vehicles. To do this, we solve a flow assignement problem, from which we deduce an ideal distribution of private vehicles. Then using traffic lights, we design a regulator maintaining the car distributions in the roads around the ideal trajectory. This regulator is obtained by solving a linear quadratic problem. The result is a global feedback on the car numbers in the roads. We show the robustness of the obtained control. We compare the obtained results with those given by .

This paper presents two polynomial-complexity techniques for assigning Gray-like binary labels to arbitrary Grassmannian constellations. In the first technique, the constellation of interest, 𝒞, is matched directly to an auxiliary constellation that can be readily Gray labelled. The optimal matching in this technique can be obtained efficiently, but its application is limited to cases in which an auxiliary constellation with a geometric structure that resembles that of 𝒞 can be identified. In the second technique no auxiliary constellation is required and the labels are generated by matching the distance spectrum of 𝒞 with that of a hypothetical constellation that is assumed to be perfectly Gray labelled. Optimal matching in this case is computationally prohibitive. Instead, an efficient suboptimal matching algorithm is proposed. When compared with several existing schemes, the proposed labellings provide better performance in both uncoded and BICM-based non-coherent MIMO systems with iterative demapping and decoding (IDD). Furthermore, with the proposed labels, the Grassmannian-based BICM-IDD scheme performs better than a training-based counterpart that employs the Golden code and optimal demapping.

There is a spectrum of asymmetric assignment problems to which existing results on uniqueness of equilibrium do not apply. Moreover, multiple equilibria may be seen to exist in a number of simple examples of real-life phenomena, including interactions at priority junctions, responsive traffic signals, multiple user classes, and multi-modal choices. In contrast, recent asymptotic results on the stochastic process approach to traffic assignment establish existence of a unique, stationary, joint probability distribution of flows under mild conditions, that include problems with multiple equilibria. In studying the simple examples mentioned above, this approach is seen to be a powerful tool in suggesting the relative, asymptotic attractiveness of alternative equilibrium solutions. It is seen that the stationary distribution may have multiple peaks, approximated by the stable equilibria, or a unimodal shape in cases where one of the equilibria dominates. It is seen, however, that the convergence to stationarity may be extremely slow. In Monte Carlo simulations of the process, this gives rise to different types of pseudo-stable behaviour (flows varying in an apparently stable manner, with mean close to one of the equilibria) for a given problem, and that this may prevail for long periods. The starting conditions and random number seed are seen to affect the type of pseudo-stable behaviour, over long but finite time horizons. The frequency of transitions between these types of behaviour (equivalently, the average sojourn in a locally attractive, pseudo-stable set of states) is seen to be affected by behavioural parameters of the model. Recommendations are given for the application of stochastic process models, in the light of these issues. A slightly stronger definition of equilibrium has been used by some authors, eg . The difference is 1 discussed by . The above follows Smith's definition of `Wardrop equilibrium'. See, for example, Smith (1979b, 1984c for alternative, global definitions of stability. It is assumed henceforth that travel cost is a linear, increasing function of travel time, and that the time-weighting is link-3 independent. Hence `time' and `cost' are interchangeable in the conditions stated. An alternative existence theorem is provided in Smith (1981a), based on properties of the cost functions at individual

Il contributo analizza l’ordinanza del Tribunale per i minorenni di L'Aquila del 5 marzo 2026, evidenziando il fallimento dell’esperimento di inserimento congiunto madre-figli in comunità. Viene ricostruita l’evoluzione dei provvedimenti cautelari alla luce di una condotta materna qualificata come ostativa e refrattaria al monitoraggio istituzionale. Particolare attenzione è dedicata alla compromissione del diritto all’istruzione, accertata al di là della mera opzione per l’educazione parentale e ritenuta incompatibile con l’obbligo scolastico vigente. La misura dell’allontanamento ex art. 330, co. 2, c.c. è esaminata non come sanzione, ma come extrema ratio necessaria a garantire l’equilibrio psichico dei minori, nel rispetto del principio di proporzionalità di cui all'art. 8 CEDU e del best interest of the child. Completa il quadro una riflessione sulla tutela della riservatezza e sull’ingerenza mediatica a danno dei minori.

The goal of this work is to establish and solve the Quadratic As-signation Problem (QAP) as a combinatory optimization problem by means of GRASP (Greedy Randomized Adaptive Search Procedure) as an approximation method to QAP.Applying GRASP to QAP produces good results to obtain solutions close to the optimum or even reach the optimum in several cases. The implementation of a sequential program was successfully made in C. The robustness of GRASP obeys to the inclusions of strategic procedures for each one of the three local search neighborhood structures employed as a second phase of GRASP. Finally a parallel system was built to reduce the time cost of the CPU.

First of all, I would like to express my sincere gratitude and appreciation to my thesis advisors, Walid Ben-Ameur and Adam Ouorou, for their invaluable guidance, constant encouragement, and endless patience during my PhD study. Thanks to them, I have the opportunity to conduct and complete my thesis in Orange Labs and Telecom SudParis. I will be forever honored and grateful for working with them. My gratitude extends to Prof. Dritan Nace and Prof. Leo Liberti for their time in reviewing my manuscript and insightful comments. I also would like to thank Prof. Evripidis Bampis, Prof. Sourour Elloumi, Dr. José Neto for accepting the invitation to participate in the defense. At the initial stage of my PhD study, I got quite valuable suggestions and encouragement from José, and for that I am really grateful. It is a good opportunity to thank Eric Gourdin and Nabil Benameur for their trust and unlimited support, and other colleagues,

Stochastic multi objective programming problems commonly arise in complex systems such as portfolio analysis, medium-to long-term capacity planning and design applications under uncertainty. The identification of the candidate solution set is a main step in many applications which depends on the nature of uncertainty. This study presents a method to generate Pareto surface for multi-objective integer programs with stochastic coefficients in the objective functions based on minimum expectation and variance criteria. The objective function coefficients are represented through random discrete distributions. The methodology follows a two-phase approach where, in the first phase, the stochastic multiple objectives are converted into deterministic equivalents based on the minimum expectation and variance efficiency concepts. The second phase solves the deterministic multi objective problem, using a Pareto generation methodology which aims at generating the whole Pareto surface of multi objective integer programming problems. We present results of experimental study of applying the proposed method to an assignment problem with three objective functions.

Airline fleet assignment involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of an agent-based integer optimization algorithm to a "cold start" fleet assignment problem. Results show that the optimizer can successfully solve such highlyconstrained problems (129 variables, 184 constraints).

In this paper we will present class of new lower bounds for the Quadratic Semi-Assignment Problem (QSAP). These bounds are based on recent results about polynomially solvable cases, in particular we will consider the QSAP's whose quadratic cost coefficients define a reducible graph. Several lower bounds will be computationally compared, moreover we will present a method which improves these bounds by means of Lagrangean decomposition.

In this paper a new procedure namely Modern Zero Suffix (MOZES) method is proposed to find the IFBS it meets optimal solution for the transportation problem. A new algorithm is generated to find the optimal solution for the transportation problem with the aid of above said notion. The relevant numerical illustrations are given to justify the above proposed notion.

A technique to solve the balanced linear assignment problem is introduced using graph theory and is based on logical approach. In the method, the aim is to find a matching in which the sum of weights of the edges is as large as possible, in a weighted bipartite graph. Generally it consists of finding a minimum-weight perfect matching and is a specialization of the maximum weight matching problem for bipartite graphs. The concept of decision matrix ([1]) is applied for finding last two assignments.

Given an L-function, one of the most important questions concerns its vanishing at the central point; for example, the Birch and Swinnerton-Dyer conjecture states that the order of vanishing there of an elliptic curve L-function equals the rank of the Mordell-Weil group. The Katz and Sarnak Density Conjecture states that this and other behavior is well-modeled by random matrix ensembles. This correspondence is known for many families when the test functions are suitably restricted. For appropriate choices, we obtain bounds on the average order of vanishing at the central point in families. In this note we report on progress in determining the optimal test functions for the various classical compact groups for different support restrictions, and discuss how this relates to improved rank bounds.

AbstractIt is often frustrating for drivers to find parking spaces, and parking itself is costly in almost every major city in the world. The search for a parking place is a task which can waste a lot of time and affect the efficiency of economic activities, social interactions, and the health of the environment. The planners of transport and city traffic must pay close attention to this issue in order to achieve an efficient management of mobility in smart cities. This work is intended to serve as an aid in the search for parking seeking the general interest of a group of drivers. We present an intensive description of the parking slots assignment problem for groups and apply it to a real case study. Also, we propose a hybrid genetic algorithm for solving this case and we compare it with three other algorithms in order to evaluate its performance

. The process can be divided into three steps, i.e., agent evaluation, group role assignment, and role transfer. This paper formally identifies various group role assignment problems under acceptable assumptions; proposes solutions based on exhaustivesearch; conducts experiments by randomly creating agent qualifications; analyzes the solutions' performance based on experimental results, and indicates future work activities.

Role assignment is a critical task in Role-Based Collaboration (RBC). It can be divided into three steps, i.e., agent evaluation, group role assignment, and role transfer, where group role assignment is a timeconsuming process. This paper clarifies the problem of group role assignment; describes the general assignment problem; adapts the group role assignment problem to the general assignment problem; proposes an algorithm for group role assignment based on the Munkres algorithm; conducts an experiment by randomly creating agent qualifications, analyzes the solutions' performances, and points out the future work. The results show that the improved algorithm significantly improves the algorithm based on exhaustive search.

Nel dicembre scorso, la Corte di giustizia dell’Unione europea si è pronunciata nei casi Hamoudi c. Frontex (C-136/24 P) e WS e altri c. Frontex (C-679/23 P), relativi ad azioni di responsabilità extracontrattuale intentate nei confronti dell’Agenzia europea della guardia di frontiera e costiera. Con tali pronunce, la Corte ha parzialmente annullato le decisioni adottate dal Tribunale circa un anno prima (T-136/22; T-600/21), rinviando le cause a
quest’ultimo per un nuovo esame. Queste sentenze hanno contribuito a chiarire alcuni profili critici concernenti gli ostacoli che limitano, per le vittime di espulsioni collettive (pushback), l’effettivo accesso alla giustizia dinanzi ai giudici dell’Unione.

This paper introduces a new algorithm to deal with multiobjective combinatorial and continuous problems. The algorithm is an extension of a previous one designed to deal with single objective combinatorial problems. The original purpose of the single objective version was to study in a rigorous way the properties the search graph of a particular problem needs to hold so that a randomized local search heuristic will find the optimum with high probability. The extension of these results to better understand multi-objective combinatorial problems seems to be a promising line of research. The work presented here is a first small step in this direction. A detailed description of the multi-objective version is presented along with preliminary experimental results on a well known combinatorial problem. The results show that the algorithm has the desired characteristics.