Automatic Management of 802.11 Access Points

The automatic configuration of Access Points (APs) is a new subject, since the Wi-Fi technology, which underlies hotspots by a wireless local area network, appears on the world market in 2001. The first market relevance has been in 2002. APs channel assignment at hotspots, and more generally APs configuration and management, has to be done manually, except for very recent APs. In this paper, we intend to partially solve the problem of automatic APs management. The goal to achieve is to have an autonomous system able to perform dynamic channel allocation of WLAN APs, in the context of multiple APs in a restricted area. Moreover, the solution has to be independent from sellers (manufacturers) or owners (generally service providers). Given a set of APs located nearby each other, the problem to be solved consists in assigning a channel to each AP such that the overall throughput is maximized, or, in other words, such that the overall perturbation is minimized. Moreover, the system has to adapt himself to dynamic variations of the environment, such as the number of associated users, the usage of the APs, and so on. The solution developed in this paper uses a distributed algorithm to solve the problem. One software agent manages one AP and is able to communicate with its neighbors in order to optimize the global throughput. Tests with different topologies have been done by simulation, as well as some real implementation. These experiments have given good results, even when networks get very dense and have many APs. Comparison with optimal solution on small networks has shown that the performance of the algorithm described in this paper is very close to the optimum

The waste collection VRP with intermediate facilities, a heterogeneous fixed fleet and a flexible assignment of origin and destination depot

We consider a complex recyclable waste collection problem that extends the class of vehicle routing problems with intermediate facilities by integrating a heterogeneous fixed fleet and a flexible assignment of origin and destination depot. Several additional side constraints, such as a mandated break period contingent on tour start time, multiple vehicle capacities and site dependencies are also included. This specific problem was inspired by a real-world application and does not appear in the literature. It is modeled as an MILP which is enhanced with several valid inequalities. Due to the rich nature of the problem, state-of-the-art commercial solvers are only able to tackle instances of small to medium size. To solve realistic instances, we propose a local search heuristic capable of systematically treating all problem features and general enough to respond to the varying characteristics of the case study regions for which it is intended. The results show that the heuristic achieves optimality on small random instances, exhibits competitive performance in comparison to state-of-the-art solution methods for special cases of our problem, and leads to important savings in the state of practice. Moreover, it highlights and quantifies the savings from allowing a flexible assignment of origin and destination depot. The data from the state of practice comes from a recyclable waste collection company in Geneva, Switzerland

Vehicle routing for a complex waste collection problem

We consider a complex waste collection problem, where the residents of a certain region dispose of recyclable waste, which is collected using a fixed heterogeneous fleet of vehicles with different volume and weight capacities, fixed costs, unit distance running costs and hourly driver wage rates. Each tour starts and ends at one of several depots, not necessarily the same, and is a sequence of collections followed by disposals at the available recycling plants, with a mandatory disposal before the end of the tour. There are time windows and a maximum tour duration, which is interrupted by a break after a certain interval of continuous work. Moreover, due to the specificities of different collection regions, there are occasional site dependencies. The problem is modeled as a mixed binary linear program and the formulation is enhanced with several valid inequalities and elimination rules. To solve realistic instances, we develop a local search heuristic, which currently embeds much of the functionality of the mathematical model. The heuristic performs well, as indicated by an optimality gap of 2% compared to the exact solution on small instances. Future work will see improving the model formulation to solve larger instances to optimality and expanding the heuristic to include all of the features of the model

Integration of a human risk module into a risk management software

In the scientific literature and in practice, many questionnaires based on a myriad of measures have been designed and tested to measure and evaluate perceived work stress or employee involvement. The objective of our research is to identify the most significant elements of human risks and to combine them into a single score at the level of teams and departments. Indeed, for companies, what really matters are the stress or dissatisfaction factors that lead to harmful behavior that prevent managers and their teams from achieving their objectives. Based on this research, we are developing a module that will be incorporated into the Oxial software and will also be available as a stand-alone module. This module will collect and analyze the data to calculate a single score measuring the level of human risk. This aspect is very innovative, because no risk management software currently includes a module dedicated to human risks

Modeling a waste disposal process via a discrete mixture of count data models

We propose a modeling framework for the data generating process of waste disposal in recyclable waste containers. It is based on a discrete mixture of count data models representing populations depositing dierent quantities in the containers, thus reflecting a realistic underlying behavior. It is tested on real data coming from ultrasound sensors mounted inside the containers and exhibits better in- and out-of-sample performance compared to a simple count data model assuming only one deposit quantity. The purpose of the mixture model is to forecast container waste levels at a future date when collection will take place. It thus becomes the first-step ingredient in a framework for ecient waste collection optimization