一种自动布线方法：多级线探索法

本文介绍了一种在微机上实现的印制板自动布线方法──多级线探索法及其编程方法。这种方法是基于完备布线算法的概念，通过分析李氏算法和传统线探索法的不足之处而得到的。虽然它也是一种线探索法，但有较传统线探索法更强的探索能力，不需要结合李氏算法而能单独完成布线任务，是一种较理想的算法，通过实验获得了较为满意的效果。     

民间传统御寒器具——火桶设计研究

＂火桶＂是南方传统的御寒器物,它具有造型简单,取材方便,功能实用多样的特点,凝聚了民间朴素造物的思想。同时,火桶所具有着深厚的人文特点和文化内涵。文章以黔东南镇远地区民间火桶为例,分析其形态、结构、功能及人机关系等内容,探究民间造物的设计巧思。     

资源生产供应链履行社会责任的内在动力研究

运用博弈论的基本思想,通过构造一个资源生产供应链,从资源保护这个角度出发,以资源生产供应链中的节点企业——资源开采商开采自然资源为例,对资源生产供应链履行社会责任的内在动力进行了研究。结果表明：对资源开采商合作开采带来的收益增量进行合理分配,既能使资源开采商进行合作,又能实现供应链系统与各资源开采商的帕累托最优。     

A social interaction system based on cloud computing for mobile video telephony

As the use of smartphones become popular,people heavily depend on smartphone applications to deal with their social activities.For this reason,traditional message texting between mobile applications does not fulfill the versatile requirements of social networking.Many mobile applications use multimodality to deliver multimedia messages including sticker,voice and photo message,video call,and snap movie to enhance the communicative capability.However,without face-to-face interaction,people may fail to detect the other side’s non-verbal social behavior such as fine-grain facial expressions,body movements,or hand gesture.During social interaction,non-verbal behavior conveys information about the involved individuals and help the speakers express their social emotion in an implicit way.It is so important for real-world face-to-face interaction but is often blocked on the mobile telephony.To cope with this problem,we propose an afective computing model to assist the representation of social emotion and then help the progress of social interaction on the mobile telephony.In this model,for the purpose of real-time afective analysis,we delegate the computing loading to the cloud side service and enhance the system’s scalability and availability.The result of this experiment approves the feasibility of our system design for the applications of social intelligent.Also,the system provides a research framework of the social intelligent system on the mobile telephony.     

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.     

A computer-enabled solution procedure for food wholesalers’ distribution decision in cities with a circular transportation infrastructure

This paper addresses the food distribution decision from a wholesaler's perspective, as the wholesalers are often faced with thorny issues such as when, where, how to deliver products at the lowest costs to best satisfy the retailers’ changing needs. This research relies on a distribution problem encountered at Northern Grocery Company in Beijing as an example to develop a two-stage solution procedure and subsequent computer-enabled programs for identifying efficient vehicle routing alternatives. The solution process, which integrates heuristic search algorithms and an integer programming model, can be applied to a wide range of distribution problems with specified route durations and a circular transportation network structure as exemplified by City of Beijing.     

Primal or dual: which promises faster spatiotemporal search?

The existing predictive spatiotemporal indexes can be classified into two categories, depending on whether they are based on the primal or dual methodology. Although we have gained considerable empirical knowledge about various access methods, currently there is only limited understanding on the theoretical characteristics of the two methodologies. In fact, the experimental results in different papers even contradict each other, regarding the relative superiority of the primal and dual techniques. This paper presents a careful study on the query performance of general primal and dual indexes, and reveals important insight into the behavior of each technique. In particular, we mathematically establish the conditions that determine the superiority of each methodology, and provide rigorous justification for well-known observations that have not been properly explained in the literature. Our analytical findings also resolve the contradiction in the experiments of previous work.     

A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms. As packing tasks arecombinatorial problems with very large searchspaces, the recent literature encourages theuse of meta-heuristic search methods, inparticular genetic algorithms. The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems.The focus is hereby on the analysis of themethods involving genetic algorithms. Anoverview of the methods applying othermeta-heuristic algorithms including simulatedannealing, tabu search, and artificial neuralnetworks is also given.