We present a novel algorithm to match GPS trajectories onto maps offline (in batch mode) using techniques borrowed from the field of force-directed graph drawing. We consider a simulated physical system where each GPS trajectory is attracted or repelled by the underlying road network via electrical-like forces. We let the system evolve under the action of these physical forces such that individual trajectories are attracted towards candidate roads to obtain a map matching path. Our approach has several advantages compared to traditional, routing-based, algorithms for map matching, including the ability to account for noise and to avoid large detours due to outliers in the data whilst taking into account the underlying topological restrictions (such as one-way roads). Our empirical evaluation using real GPS traces shows that our method produces better map matching results compared to alternative offline map matching algorithms on average, especially for routes in dense, urban areas.
Distributed data center architectures have been recently developed for a more efficient and economical storage of data. In many models of distributed storage, the aim is to store the data in such a way so that the storage costs are minimized and increased redundancy requirements are maintained. However, many approaches do not fully consider issues relating to delivering the data to the end user and the associated costs that this creates. We present an integer programming optimization model for determining the optimal allocation of data components among a network of Cloud data servers in such a way that the total costs of additional storage, estimated data retrieval costs and network delay penalties is minimized. The method is suitable for periodic dynamic reconfiguration of the Cloud data servers, so that the when localized data request spikes occur the data can be moved to a closer or cheaper data server for cost reduction and increased efficiency.
This article presents a mixed-integer programming model for solving the university timetabling problem which considers the allocation of students to classes and the assignment of rooms and time periods to each class. The model was developed as part of our participation in the International Timetabling Competition 2019 and produced a ranking of second place at the competition. Modeling a timetabling problem as a mixed-integer program is not new. Our contribution rests on a number of innovative features adapted to this problem which allow for a reduction in the number of variables and constraints of the mixed-integer program to manageable levels achieving a reasonable computational performance. The proposed algorithm consists of a first-stage method to obtain an initial feasible solution and a second-stage local search procedure to iteratively improve the solution value, both of which involve the optimization of mixed-integer programming problems.
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