基于聚类和动态规划的组合路径策略

为提高电商仓库的拣货作业效率,本文提出了基于聚类和动态规划的组合路径策略,实现了生成路径消耗时间和路径长度之间的平衡,并将这一策略成功地应用到多区型仓库。该策略分四步:首先,根据待拣储位分布特征,运用聚类分析法对其进行分类;然后,以各类的首末储位作为节点,运用动态规划法对已得分类进行排序,得到相应的类序;其次,得到各类内部路径;最后,依次拣取待拣商品,并返回出发点完成拣货作业。在提出新的路径策略后,通过仿真方法将新策略与三种传统路径策略(穿越策略、最大间隙策略和混合策略)和一种优质算法(蚁群算法)进行了对比分析,结果表明:该策略具备良好的适用性和实用性。     

New lower bounds on the size of (n,r)‐arcs in PG(2,q)

An $\left(n,r\right)$‐arc in $\text{PG}\left(2,q\right)$ is a set of $n$ points such that each line contains at most $r$ of the selected points. It is well known that $\left(n,r\right)$‐arcs in $\text{PG}\left(2,q\right)$ correspond to projective linear codes. Let $m_r\left(2,q\right)$ denote the maximal number $n$ of points for which an $\left(n,r\right)$‐arc in $\text{PG}\left(2,q\right)$ exists. In this paper we obtain improved lower bounds on $m_r\left(2,q\right)$ by explicitly constructing $\left(n,r\right)$‐arcs. Some of the constructed $\left(n,r\right)$‐arcs correspond to linear codes meeting the Griesmer bound. All results are obtained by integer linear programming.     

On stochastic auctions in risk-averse electricity markets with uncertain supply

This paper studies risk in a stochastic auction which facilitates the integration of renewable generation in electricity markets. We model market participants who are risk averse and reflect their risk aversion through coherent risk measures. We uncover a closed form characterization of a risk-averse generator’s optimal pre-commitment behaviour for a given real-time policy, both with and without risk trading.     

基于不同条件的旅游路线规划问题研究

运用2015年全国研究生数学建模竞赛F题的数据资料,针对旅游路线合理规划问题的第一问展开研究.以F题的问题一为起点进行了分析研究,是因为第一问的完成是解决后续问题的关键.首先通过地图搜集并补全了缺失数据并对数据进行合理的处理,然后采用将旅游年数最少目标转化为该最小生成树的"最少圈覆盖"方法来进行对问题一的求解,也可以理解为一个广义的多旅行商问题,以旅行商的人数(即年数)最少为目标.采用Dijkstra算法、最少圈覆盖法、智能算法和图论聚类等方法,通过这些方法建立了单目标优化模型,并运用旅行商问题和模型之间的转换来对问题进行分析与求解.     

A support for the identification of non-tryptic peptides based on low resolution tandem and sequential mass spectrometry data: The INSPIRE software

A simple software, to be used as an aid in the identification of non-tryptic peptides based on low resolution (3D-ion trap) tandem (MS/MS) and sequential (MS³) mass spectrometry data, is presented.     

A semidefinite programming approach to the hypergraph minimum bisection problem

The hypergraph minimum bisection (HMB) problem is the problem of partitioning the vertices of a hypergraph into two sets of equal size so that the total weight of hyperedges crossing the sets is minimized. HMB is an NP-hard problem that arises in numerous applications – for example, in digital circuit design. Although many heuristics have been proposed for HMB, there has been no known mathematical program that is equivalent to HMB. As a means of shedding light on HMB, we study the equivalent, complement problem of HMB (called CHMB), which attempts to maximize the total weight of non-crossing hyperedges. We formulate CHMB as a quadratically constrained quadratic program, considering its semidefinite programming relaxation and providing computational results on digital circuit partitioning benchmark problems. We also provide results towards an approximation guarantee for CHMB.     

Further bounds for waiting time distributions and their means in the case ρ<1

In the paper exponential bounds for the instationary and stationary waiting time d.f.'s in GI/GI/1/∞, derived by several authors partly using martingale theory arguments, are improved with the help of monotonicity properties. Further it is shown that Ne?eptjrenko's bound for the rate of convergence of the mean waiting time in single server queues Is also valid for many server queues with ρ < 1.     

Computationally Efficient Changepoint Detection for a Range of Penalties

In the multiple changepoint setting, various search methods have been proposed, which involve optimizing either a constrained or penalized cost function over possible numbers and locations of changepoints using dynamic programming. Recent work in the penalized optimization setting has focused on developing an exact pruning-based approach that, under certain conditions, is linear in the number of data points. Such an approach naturally requires the specification of a penalty to avoid under/over-fitting. Work has been undertaken to identify the appropriate penalty choice for data-generating processes with known distributional form, but in many applications the model assumed for the data is not correct and these penalty choices are not always appropriate. To this end, we present a method that enables us to find the solution path for all choices of penalty values across a continuous range. This permits an evaluation of the various segmentations to identify a suitable penalty choice. The computational complexity of this approach can be linear in the number of data points and linear in the difference between the number of changepoints in the optimal segmentations for the smallest and largest penalty values. Supplementary materials for this article are available online.     

OBSERVATIONS ON INFEASIBILITY DETECTORS FOR CLASSIFYING CONIC CONVEX PROGRAMS

Abstract

New insights are presented from a convex duality classification theory of over 25 years ago for recent computational complexity results developed for recognizing some of the asymptotic duality states that prevail for an arbitrary convex programming pair.