Traditional IMU based PDR systems suffer from rapidly growing drift effects due to the inherent bias of the inertial sensor. Many existing solutions to mitigate this problem use aiding sensors or information as heuristics or map data. We propose a new optimization framework to solve the PDR estimation problem where the sensors biases are explicitly included as state variables and therefore be used to correct for bias effects in the PDR. By using a smoothing approach and exploiting the rigid structure of a MIMU array one can solve for the slowly varying sensor biases. This paper presents the method and gives an exemplary result of a walking trial. Good agreements in the position and orientation with an optical reference system were found. Moreover, accelerometer and gyroscope biases could be estimated accordingly. Further research includes the performance of more experiments under various conditions such that a more quantitative evaluation can be obtained. In addition, an exploration of a (pseudo) realtime filter version would be valuable such that the system can be applied online.
MULTIFILE
This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
An important contribution to the environmental impact of agro-food supply chains is related to the agricultural technology and practices used in the fields during raw material production. This problem can be framed from the point of view of the Focal Company (FC) as a raw material Green Supplier Selection Problem (GSSP). This paper describes an extension of the GSSP methodology that integrates life cycle assessment, environmental collaborations, and contract farming in order to gain social and environmental benefits. In this approach, risk and gains are shared by both parties, as well as information related to agricultural practices through which the FC can optimize global performance by deciding which suppliers to contract, capacity and which practices to use at each supplying field in order to optimize economic performance and environmental impact. The FC provides the knowledge and technology needed by the supplier to reach these objectives via a contract farming scheme. A case study is developed in order to illustrate and a step-by-step methodology is described. A multi-objective optimization strategy based on Genetic Algorithms linked to a MCDM approach to the solution selection step is proposed. Scenarios of optimization of the selection process are studied to demonstrate the potential improvement gains in performance.
