An evidential approach to SLAM,path planning,and active exploration

Probability theory has become the standard framework in the field of mobile robotics because of the inherent uncertainty associated with sensing and acting. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages over probabilistic approaches in the context of robotics. We do so by presenting solutions to the essential problems of simultaneous localization and mapping (SLAM) and planning based on belief functions. For SLAM, we show how the joint belief function over the map and the robot's poses can be factored and efficiently approximated using a Rao-Blackwellized particle filter, resulting in a generalization of the popular probabilistic FastSLAM algorithm. Our SLAM algorithm produces occupancy grid maps where belief functions explicitly represent additional information about missing and conflicting measurements compared to probabilistic grid maps. The basis for this SLAM algorithm are forward and inverse sensor models, and we present general evidential models for range sensors like sonar and laser scanners. Using the generated evidential grid maps, we show how optimal decisions can be made for path planning and active exploration. To demonstrate the effectiveness of our evidential approach, we apply it to two real-world datasets where a mobile robot has to explore unknown environments and solve different planning problems. Finally, we provide a quantitative evaluation and show that the evidential approach outperforms a probabilistic one both in terms of map quality and navigation performance.     

Fair optimization of fortnightly physician schedules with flexible shifts

This research addresses a shift scheduling problem in which physicians are assigned to demand periods. We develop a reduced set covering approach that requires shift templates to be generated for a single day and compare it to an implicit modeling technique where shift-building rules are implemented as constraints. Both techniques allow full flexibility in terms of different shift starting times and lengths as well as break placements. The objective is to minimize the paid out hours under the restrictions given by the labor agreement. Furthermore, we integrate physician preferences and fairness aspects into the scheduling model. Computational results show the efficiency of the reduced set covering formulation in comparison to the implicit modeling approach.     

Principles of Combinatorial Optimization Applied to Container-Ship Stowage Planning

In this paper, a methodology for generating automated solutions to the container stowage problem is shown. The methodology was derived by applying principles of combinatorial optimization and, in particular, the Tabu Search metaheuristic. The methodology progressively refines the placement of containers, using the Tabu search concept of neighbourhoods, within the cargo-space of a container ship until each container is specifically allocated to a stowage location. Heuristic rules are built into objective functions for each stage that enable the combinatorial tree to be explored in an intelligent way, resulting in good, if not optimal, solutions for the problem in a reasonable processing time.     

电热耦合综合能源系统双阶段多目标规划研究

多能耦合系统是未来分布式能源供给方式的重要发展方向。为了实现电热耦合能源供给系统的合理规划、促进能源供给与消费的经济与环保的协调发展,提出一种面向电热耦合能源系统的综合能源系统双阶段规划优化方法。模型的第一阶段是在投资和环境最优的目标下实现电热耦合综合能源系统的合理规划,第二阶段是在考虑设备运行特性的基础上对规划的结果进行运行优化,以获得能源系统的最优运行方案,并从多个指标验证规划方案的合理性。利用NSGA-II算法求得模型进的帕累托解集,使用多准则妥协优化法从帕累托解集中决策出最优配置方案。仿真结果表明,提出的双阶段多目标综合能源规划方法能够实现能源供给系统的经济与环保双优。     

公交车调度问题的数学模型

对 2 0 0 1年全国大学生数学建模竞赛的 B题——公交车调度问题进行了分析 ,建立了调度的目标规划模型及 0— 1规划模型 .在假设各站上、下车人数服从均匀分布的条件下 ,通过对模型的求解 ,求出了公交公司的最小运行车辆数 5 2辆 ,并给出了发车时刻表 ,其中上行方向运行 2 2 5班次 ,下行方向运行 2 2 0班次 .该模型简单 ,求解容易 ,能较好地考虑各方利益     

面向远海岛礁群的双向物流网络规划

针对若干远海岛礁群系统封闭性的分布特点,将中心岛礁选址、各岛礁建设泊位数量和规模、仓库储存量、运输船型和数量以及航线设置、班期组织等作为规划内容,以整个运输网络系统总成本最低为原则,综合考虑了仓库存储理论,建立了兼顾“选址-库存-路径”的规划模型。结合所建模型特点,提出了双层并行搜索遗传算法,构建了以海上运输为基本交通方式的双向物流系统。最后,以我国南海为例进行建模并求解,采用不同算法并通过多组不同规模的算例进行演算比较,验证了所建模型和算法的有效性与合理性。     

Global facility network design in the presence of competition

We study the facility network design problem for a global firm that is a monopolist seller in its domestic market but faces local competition in its foreign market. The global firm produces in the face of demand and exchange rate uncertainty but can postpone localization and distribution of the output until after uncertainties are resolved. The competitor in the foreign market, however, enjoys the flexibility of postponing all production activities until after uncertainties are resolved. The two firms engage in an ex-post Cournot competition in the foreign market. We consider three potential network configurations for the global firm. Under a linear demand function, we provide the necessary and sufficient condition that one of the three networks is the global firm’s optimal choice, and explore how the presence of foreign competition affects the sensitivity of the global firm’s design to various cost parameters and market uncertainties.