A new modeling approach for the biobjective exact optimization of satellite payload configuration. (8th February 2017)

• Record Type: Journal Article
• Authors: Kieffer, Emmanuel; Danoy, Grégoire; Bouvry, Pascal; Nagih, Anass
• Is Part Of: International transactions in operational research. Volume 26:Number 1(2019)
• Page Start: 180
• Page End: 199
• Publication Date: 2017-02-08
• Subjects: bi‐objective optimization -- integer linear programming -- adaptive ε‐constraint -- satellite payload optimization; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12386
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: Communication satellites have the crucial role to forward signals to customers. They filter and amplify uplink signals coming from Earth stations to improve the signal quality before reaching customers. These operations are performed by the payload component of the satellite that embeds reconfigurable components (e.g., switches). These components route signals to appropriate signal processing components (e.g., amplifiers, filters) and lead amplified signals to the output antenna. In order to route the channels that compose signals, satellite engineers can remotely modify switch states. These are typically updated when one or more new channels must be connected or when failures occur. However, satellites embed always more switches to answer customer demands, which makes their reconfiguration time consuming and error prone without appropriate decision aid tools. Power transmission is a crucial objective to ensure a maximum quality of service at reasonable cost. This is why satellite operators aim at minimizing incoming power signals while guaranteeing a maximum factor of amplification at the output antenna. This problem is referred to as the "satellite payload power problem." Previous works have outlined the difficulty to solve exactly large instances of this problem. This work proposes to improve the existing mathematical formulation of the switch network. We show that it can be modeled as a static network and switch states can be deduced after optimization, thus limiting the combinatorial explosion. Computational experiments on different sizes of realistic instances using the adaptive ε‐constraint method demonstrate the computational time gain with this new model and the possibility to solve larger instances.