Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

Coordination of pricing and inventory control across products

We consider the joint pricing and inventory‐control problem for a retailer who orders, stocks, and sells two products. Cross‐price effects exist between the two products, which means that the demand of each product depends on the prices of both products. We derive the optimal pricing and inventory‐control policy and show that this policy differs from the base‐stock list‐price policy, which is optimal for the one‐product problem. We find that the retailer can significantly improve profits by managing the two products jointly as opposed to independently, especially when the cross‐price demand elasticity is high. We also find that the retailer can considerably improve profits by using dynamic pricing as opposed to static pricing, especially when the demand is nonstationary. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Production planning with resources subject to congestion

A fundamental difficulty in developing effective production planning models has been accurately reflecting the nonlinear dependency between workload and lead times. We develop a mathematical programming model for production planning in multiproduct, single stage systems that captures the nonlinear dependency between workload and lead times. We then use outer linearization of this nonlinear model to obtain a linear programming formulation and extend it to multistage systems. Extensive computational experiments validate the approach and compare its results to conventional models that assume workload‐independent planning lead times. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

A model to design recreational boat mooring fields

This article develops a mathematical model and heuristic algorithm to design recreational boating mooring fields. The boating industry is important to the Florida economy, and boat storage is becoming a concern among those in the industry. The mooring field design problem is formulated to maximize the total number of boat feet moored in the mooring field. In the model, we allow two adjacent moorings to overlap, which introduces a risk that under certain conditions the boats on these moorings could contact each other. We identify the conditions when contact is possible and quantify the probability of contact. The mooring field design problem is formulated as a nonlinear mixed‐integer programming problem. To solve the problem, we decompose it into two separate models, a mooring radii assignment model and a mooring layout model, which are solved sequentially. The first is solved via exhaustive enumeration and the second via a depth‐first search algorithm. Two actual mooring fields are evaluated, and in both cases our model leads to better layouts than ones experts developed manually. The mooring field design model rationalizes the mooring field design and shows that in one case by increasing the risk from 0 to 1%, the mooring efficiency increases from 74.8% to 96.2%. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

基于状态观测器的混沌系统鲁棒同步设计

研究了在有界干扰情况下一类非线性反馈混沌系统的鲁棒同步状态观测器设计问题。基于Sylvester矩阵方程的参数化解,将非线性反馈混沌系统的鲁棒同步状态观测器设计问题转化为带有约束条件的优化问题,通过解决该优化问题得到鲁棒同步状态观测器的增益矩阵,从而达到了干扰信号解耦目的。数值算例及其仿真结果表明:该非线性反馈混沌系统的鲁棒同步状态观测器的设计方法是简单有效的。     

基于A*算法的坦克CGF全局路径规划

提出了一种应用A*启发式搜索算法对虚拟战场中坦克CGF(Computer Generated Forces)实体进行全局路径规划的方法,同时对A*算法进行了改进,利用二叉堆的结构组织OPEN表,大大加速了A*算法的搜索效率。所提方法可以处理较大规模的战场地形,具有较高的效率。     

支持向量顺序回归机的线性规划算法

支持向量顺序回归机是标准支持向量分类机的一个推广，它是一个凸的二次规划问题。本文根据l1范数与l2范数等价关系和优化问题的对偶原理，把凸的二次规划转化成线性规划。由此提了支持向量顺序回归机的线性规划算法，进一步用数值实验验证了此算法的可行性和有效性。并与支持向量顺序回归机相比，它的运行时间缩短了，而且误差i不超过支持向量顺序回归机；