Optimizing the investments in mobile networks and subscriber migrations for a telecommunication operator

We consider the context of a telecommunications company that is at the same time an infrastructure operator and a service provider. When planning its network expansion, the company can leverage over its knowledge of subscribers dynamic to better optimize the network dimensioning, therefore avoiding unnecessary costs. In this work, the network expansion represents the deployment and/or reinforcement of several technologies (e.g. 2G,3G,4G), assuming that subscribers to a given technology can be served by this technology or older ones. The operator can influence subscribers dynamic by subsidies. The planning is made over a discretized time horizon while some strategic guidelines requirements are demanded at the end of the time horizon. Following classical models, we consider that the behavior of customers follows an S-shape piecewise constant function. We propose a Mixed-Integer Linear Programming formulation and a heuristic algorithm for the multi-year planning problem. The scalability of the formulation and the quality of the heuristic are assessed numerically on real instances for a use-case with two generations

Plannification optimale des réseaux mobiles

In this thesis, we consider different mobile investments strategies problems, corresponding to different key questions for a telecommunication company, and integrating three decision levers: network and marketing investment, and spectrum holding management. For each of these problems, we provide mixed integer linear formulations. We reinforce these formulations thanks to RLT cuts and context-specific valid inequalities. We introduce a generic scheme for building heuristics, based on subscribers reaction enumeration. Finally, we consider uncertainty in subscribers reaction for two generations and two periods problem. We model this uncertainty thanks to adjustable robust optimization and domination scenario properties.Dans cette thèse, nous considérons différents problèmes de stratégies d'investissements dans le domaine des réseaux mobiles, correspondant à différentes questions clefs pour une entreprise de télécommunication et intégrant trois leviers décisionnels : investissements réseaux et marketing, et gestion des actifs de spectre. Nous modélisons chacun de ces problèmes sous forme de programmes linéaires en variables mixtes. Nous renforçons ces programmes grâce à des coupes RLT, ainsi qu'à des inégalités valides spécifiques à notre problème. Nous introduisons un schéma générique pour construire des heuristiques, basé sur l'énumération de la réaction des utilisateurs. Enfin, nous considérons l'incertitude sur la réaction des utilisateurs, pour le problème à deux générations et deux périodes. Nous modélisons cette incertitude grâce à l'optimisation robuste ajustable et à des propriétés de dominations de scénarios

Optimal planning of mobile networks

Dans cette thèse, nous considérons différents problèmes de stratégies d'investissements dans le domaine des réseaux mobiles, correspondant à différentes questions clefs pour une entreprise de télécommunication et intégrant trois leviers décisionnels : investissements réseaux et marketing, et gestion des actifs de spectre. Nous modélisons chacun de ces problèmes sous forme de programmes linéaires en variables mixtes. Nous renforçons ces programmes grâce à des coupes RLT, ainsi qu'à des inégalités valides spécifiques à notre problème. Nous introduisons un schéma générique pour construire des heuristiques, basé sur l'énumération de la réaction des utilisateurs. Enfin, nous considérons l'incertitude sur la réaction des utilisateurs, pour le problème à deux générations et deux périodes. Nous modélisons cette incertitude grâce à l'optimisation robuste ajustable et à des propriétés de dominations de scénarios.In this thesis, we consider different mobile investments strategies problems, corresponding to different key questions for a telecommunication company, and integrating three decision levers: network and marketing investment, and spectrum holding management. For each of these problems, we provide mixed integer linear formulations. We reinforce these formulations thanks to RLT cuts and context-specific valid inequalities. We introduce a generic scheme for building heuristics, based on subscribers reaction enumeration. Finally, we consider uncertainty in subscribers reaction for two generations and two periods problem. We model this uncertainty thanks to adjustable robust optimization and domination scenario properties

Optimizing the investments in mobile networks and subscriber migrations for a telecommunication operator

International audienceWorldwide telecommunications groups are both infrastructure operator and service provider. Hence, when planning the network expansion, these groups must also consider the subscribers dynamics, which they can influence through subsidies. Addressing both aspects together enables them to better optimize the network dimensioning, therefore avoiding unnecessary costs. In this work, the network expansion represents the deployment and/or reinforcement of several technologies (e.g. 2G,3G,4G), assuming that subscribers to a given technology can be served by this technology or older ones. The objective of the resulting optimization problem is to minimize network investments costs and subsides, while being subject to both capacity and strategical constraints, such as minimum coverage and users averaged throughput. We model the customer behavior in response to subsides with S-shape piecewise linear functions, which are linearized. We assess numerically the resulting Mixed-Integer Linear Programming (MILP) formulation on real-life instances focusing on 3G/4G migrations. Our results show the scalability of the MILP model for 2 network generations and 100 sites. Moreover, they underline the cost-benefit of solving a unique optimization problem over the whole time-horizon (5 years) compared to decomposing the problem year by year

Optimizing subscriber migrations for a telecommunication operator in uncertain context

International audienceWe consider a telecommunications company expanding its network capacity to face an increasing demand. The company can also invest in marketing to incentivize clients to shift to more recent technologies, hopefully leading to cheaper overall costs. To model the effect of marketing campaigns, previous works have relied on the Bass model. Since that model only provides a rough approximation of the actual shifting mechanism, the purpose of this work is to consider uncertainty in the shifting mechanism through the lens of robust optimization. We thus assume that the (discrete) shifting function can take any value in a given polytope and wish to optimize against the worst-case realization. The resulting robust optimization problem possesses integer recourse variables and non-linear dependencies on the uncertain parameters. We address these difficulties as follows. First, the integer recourse is tackled heuristically through a piece-wise constant policy dictated by a prior partition of the uncertainty polytope. Second, the non-linearities are handled by a careful analysis of the dominating scenarios. The scalability and economical relevance of our models are assessed through numerical experiments performed on realistic instances. In particular, we choose one of these instances to perform a case study with simulations illustrating the possible benefit of using robust optimization