Lane covering with partner bounds in collaborative truckload transportation procurement

In collaborative truckload transportation procurement, the collaborating shippers aim to jointly identify and submit tours with little or no asset repositioning to a carrier, as opposed to submitting individual lanes, in return for more favorable rates. In order to maximize savings, the shippers must solve a Lane Covering Problem (LCP), which in more mathematical terms means to cover a subset of arcs in a directed graph by a set of constrained cycles with minimum total cost. Previous works in the literature have proposed effective greedy algorithms or the solution of the NP-Hard LCP variants. This paper incorporates a new constraint into the LCP, motivated by the need to limit the number of partners with whom the collaborative tours must be coordinated. An integer programming model is formulated, and column generation and branch-and-price approaches are developed for the solution of the resulting LCP variant.     

Large-scale coordination: developing a framework to evaluate socio-technical and collaborative issues

Collaborative logistics, logistics-based networks, focused logistics, and distributed adaptive logistics refer to innovative approaches to logistics planning/execution encompassing automated, adaptive technologies, and proactive human collaboration. Because these approaches are collaborative and distributed in nature, they create novel challenges for coordination and collaboration within socio-technical systems, which encompass the collaborative technologies, the people or machines engaged in collaboration, and the social context of collaboration and coordination. The present challenges need to be met by theoretical models incorporating multidisciplinary perspectives in the study of collaboration. The current paper introduces a collaboration framework which may inform the design and implementation of collaborative technologies and systems, to include distributed logistics teams, from a socio-technical perspective.     

Uncapacitated single allocation p-hub maximal covering problem

The hub location problem is to find a set of hub nodes on the network, where logistics transportation via the hubs is encouraged because of the cost or distance savings. Each node that has a specified amount of demands can be connected to one of p hubs. The uncapacitated single allocation p-hub maximal covering problem is to maximize the logistics covered, where the logistics of demand is said to be covered if the distance between two nodes is less than or equal to the specified range in consideration of the distance savings between hubs. The aim of our model is to locate the hub, and to allocate non-hub nodes to the located hub nodes; the hub can maximize the demand covered by deadline traveling time. It is presented an integer programming formulation for the new hub covering model, and a computational study based on several instances derived from the CAB (Civil Aeronautics Board) data set. Two heuristics, distance based allocation and volume based allocation methods, are suggested with a computational experiment on the CAB data set. Performances of heuristics are evaluated, and it is shown that good solutions are found in a relatively reasonable computation time for most of instances.     

An optimization algorithm for the inventory routing problem with continuous moves

The typical inventory routing problem deals with the repeated distribution of a single product from a single facility with an unlimited supply to a set of customers that can all be reached with out-and-back trips. Unfortunately, this is not always the reality. We focus on the inventory routing problem with continuous moves, which incorporates two important real-life complexities: limited product availabilities at facilities and customers that cannot be served using out-and-back tours. We need to design delivery tours spanning several days, covering huge geographic areas, and involving product pickups at different facilities. We develop an integer programming based optimization algorithm capable of solving small to medium size instances. This optimization algorithm is embedded in local search procedure to improve solutions produced by a randomized greedy heuristic. We demonstrate the effectiveness of this approach in an extensive computational study.     

多车场物流协同运输调度问题研究

物流协同运输是现代物流模式的发展趋势,而利润则是驱动物流协同的关键因素。针对多车场物流协同运输中的调度问题,基于汽车配件物流运输的相关约束,建立了由利润驱动的配送任务模型。通过模糊聚类建立基于集货中心点的遗传种群,通过遗传算法的交叉、变异操作进行迭代进化,在一定的迭代次数内得到一个最优解。通过一个实例说明了该模型的求解结果可以使得不同物流企业的运输车辆之间发生协同的同时达到增加物流企业运输利润的目标,从而验证了该模型的正确性和合理性。     

Inventory routing with continuous moves

The typical inventory routing problem deals with the repeated distribution of a single product from a single facility with an unlimited supply to a set of customers that can all be reached with out-and-back trips. Unfortunately, this is not always the reality. We introduce the inventory routing problem with continuous moves to study two important real-life complexities: limited product availabilities at facilities and customers that cannot be served using out-and-back tours. We need to design delivery tours spanning several days, covering huge geographic areas, and involving product pickups at different facilities. We develop an innovative randomized greedy algorithm, which includes linear programming based postprocessing technology, and we demonstrate its effectiveness in an extensive computational study.     

Application of covering rough granular computing model in collaborative filtering recommendation algorithm optimization

Data sparseness will reduce the accuracy and diversity of collaborative filtering recommendation algorithms. In response to this problem, using granular computing model to realize the nearest neighbor clustering, and a covering rough granular computing model for collaborative filtering recommendation algorithm optimization is proposed. First of all, our method is built on the historical record of the user's rating of the item, the user’s predilection threshold is set under the item type layer to find the user's local rough granular set to avoid data sparsity. Then it combines the similarity between users. Configuring the covering coefficient for target user layer, it obtained the global covering rough granular set of the target user. So it solved the local optimal problem caused by data sparsity. Completed the coarse–fine-grained conversion in the covering rough granular space, obtain a rough granular computing model with multiple granular covering of target users, it improved the diversity of the recommendation system. All in all, predict the target users’ score and have the recommendation. Compared experiments with six classic algorithms on the public MovieLens data set, the results showed that the optimized algorithm not only has enhanced robustness under the premise of equivalent time complexity, but also has significantly higher recommendation diversity as well as accuracy.     

Collaborating with writing tools: An instrumental perspective on the problem of computer-supported collaborative activities

This paper presents an analysis of the modifications that a synchronous computer support for collaborative writing introduces into the organization of co-authors' writing. The analysis is grounded in case studies of different groups of co-authors writing a report together face to face and at a distance through a collaborative writing computer system. Drawing from these studies I suggest that the problems with using a collaborative writing computer system to provide a fully collaborative writing environment derive from underlying assumptions concerning collaboration within the co-authoring activity. I point out that a more thorough understanding of how co-authors organize their writing can provide resources to envisage more radical solutions to the problem of computer support for collaboration. I conclude by considering ways that might be adequate to reconfigure collaborative writing systems in order to provide more satisfactory support for collaboration in writing environments.     

群组协作社交网络关系的分析及可视化

社交网络的研究应用领域广泛,大多数的研究主要关注于社交网络结构中节点和链接的变化,研究角度较为单一.而群组协作关系在社交网络中较为普遍,为更好地探索群组协作过程中群组级别网络结构的演变,首先将组级任务添加到用于图可视化的任务分类中,针对协作关系网络的特性,设置一个适用于群组协作关系分析的任务分类法,根据此任务分类法设置探究群组协作需完成的任务;然后设计相关可视化视图,以更直观的方式展示群组之间以及群组内部协作关系的特征以及随时间推进的演变模式;最后在学术协作文献数据集上进行具体案例分析,通过用户评估实验,采用李克特量表对调查问卷进行分析,验证了设计可视化视图的有效性和实用性,可为社交网络关系分析人员提供群组协作的可视化分析方法,更便于对群组协作网络关系的进一步探究和应用.     

Application of machine learning techniques to analyse student interactions and improve the collaboration process

In e-learning environments that use the collaboration strategy, providing participants with a set of communication services may not be enough to ensure collaborative learning. It is thus necessary to analyse collaboration regularly and frequently. Using machine learning techniques is recommended when analysing environments where there are a large number of participants or where they control the collaboration process. This research studied two approaches that use machine learning techniques to analyse student collaboration in a long-term collaborative learning experience during the academic years 2006–2007, 2007–2008 and 2008–2009. The aims were to analyse collaboration during the collaboration process and that it should be domain independent. Accordingly, the intention was to be able to carry out the analysis regularly and frequently in different collaborative environments. One of the two approaches classifies students according to their collaboration using unsupervised machine learning techniques, clustering, while the other approach constructs metrics that provide information on collaboration using supervised learning techniques, decision trees. The research results suggest that collaboration can be analysed in this way, thus achieving the aims set out with two different machine learning techniques.     

Integrating carrier selection with supplier selection decisions to improve supply chain security

Supply chain security is a major concern for logistics managers who have responsibility for inbound and outbound shipments to and from both domestic and international locations. We propose here that logistics decisions concerning security in the supply chain will be made more effectively when made in concert with decisions in related supply chain processes, especially supplier and carrier selection. Indeed, managers may minimize cost, transit time, and security risk by integrating decision processes internally, as well as with their carrier's and supplier's operations. Thus, we account for both intra‐firm collaboration between logistics and purchasing managers, as well as inter‐firm collaboration among buyers, suppliers, and carriers in a supply chain. In this paper, we propose a decision process that features a set of security rules and a multi‐objective optimization model to accomplish this aim. We then provide an illustration to demonstrate the potential usefulness of these concepts in practice.     

考虑单位车辆运力的运输-库存随机需求决策模型

针对提前期和客户需求不确定的生产、运输和库存协调控制问题, 基于整车直达运输策略, 从优化系统物流成本角度, 建立了在决策中明确体现单位车辆运力影响的运输-库存系统协调模型, 设计了求解模型的优化搜索机制并从数学上证明了其有效性. 最后, 进一步对单位车辆运力进行敏感性分析并得出以下结论: 在其他条件不变时, 单位车辆的运力会影响系统决策结果, 该运力既不是越大越好, 也不是越小越好, 而是某个适中值.

     

A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem

The two-echelon location-routing problem (LRP-2E) is raised by the design of transportation networks with two types of trips: first-level trips serving from one main depot a set of satellite depots, to be located, and second-level trips supplying customers from these satellites. In the proposed multi-start iterated local search (MS-ILS), three greedy randomized heuristics are used cyclically to get initial solutions. Each ILS run alternates between two search spaces: LRP-2E solutions, and travelling salesman (TSP) tours covering the main depot and the customers. The number of iterations allotted to a run is reduced whenever a known solution (stored in a tabu list) is revisited. MS-ILS can be reinforced by a path-relinking procedure (PR), used internally for intensification, as post-optimization, or both. On two sets with 24 and 30 LRP-2E instances, MS-ILS outperforms on average two GRASP algorithms and adding PR brings a further improvement. Our metaheuristic also surpasses a tabu search on 30 instances for a more general problem with several main depots. It is still effective on a particular case, the capacitated location-routing problem (CLRP): In a comparison with four published metaheuristics, only one (LRGTS, Prins et al., 2007) does better.     

一种基于协商的软件过程协同方法

大型软件系统的开发大多要求多人协同完成．软件过程协同的一个重要特点是协同的参与者都试图通过实施协同任务来取得最大化的获利，因而协同的决策和实施不是强制性的，而是由软件开发人员或软件组织经过协商来进行的．传统的软件过程建模方法中对软件过程协同的描述是刚性的，即在满足进入条件或者被显式调用时，协同就一定会被触发，并按照一个规定好的规则或方针来实施，这样的方法难以适应软件过程协同中所表现的协商特性．提出了一个基于协商的软件过程协同方法，将软件过程描述为一组相对独立的、自治的、理性的、协作的软件过程Agent，过程Agent之间的协同关系由过程Agent通过协商确定，相比传统的方法，具有能够更好地适应软件过程协同的特点．     

A computing theory for collaborative and transparent decision making under time constraint

In this paper, we present a computing theory on the accelerated critical point of selection of proper strategies between collaboration and competition and its mathematical analysis for collaborative and transparent decision making under “time constraint”, i.e., cost of time pressure, which decision makers face in negotiation. Web 2.0 provides a useful digital environment to support various time-stressed human behaviors for collaborative decision making. However, little research has examined the collaborative behavior under time constraint on the Internet in its designs and implementations on electronically supported decision making. Those systems are implemented in black boxes so that we need transparent designs of decision support systems to promote collaboration by time-stressed decision makers. The essential problem on collaborative decision making under time constraint is the discovery and evaluation on the critical point of selection of proper strategies between collaboration and competition. Most of the current decision support systems accept the half of maximum acceptable time for negotiation as a critical point, a priori, though that conventional point is often late for proper decision making. The proposed theory shows that a critical point is to be accelerated at the one-third of maximum acceptable time for negotiation. We have formulated the proposed computing theory based on mathematical formulation, and checked its feasibility in its applications to a case study. The proposed theory promotes collaborative and transparent decision making in consideration of the properties of stakes under time pressure by selecting the strategy of collaboration or competition at the much earlier stage.     

基于E-CARGO模型的开发者推荐方法

针对传统开发者推荐方法侧重于分析开发者的专业能力以及与任务的交互信息,而未考虑开发者间协作性的问题,提出一种基于E-CARGO(Environment-Class,Agent,Role,Group,and Object)模型的开发者推荐方法.首先结合协作开发的特点,将开发者协同开发过程描述为基于角色的协作,并使用E-C...     

基于需求链的协同商务模型研究

深入研究了需求链管理和实施中面临的问题,分析了将协同商务引入需求链管理的必要性和必然性,并在此基础上,提出基于需求链管理的四层协同商务模型.模型利用网格技术搭建信息共享平台,充分利用网格技术的分布性、高效性、跨平台性等特点,有效共享实时信息.并在此信息共享平台之上,构建协同作业层、协同管理层以及协同预测层,实现面向需求链的企业联盟的ERP、CRM系统的集成,实现企业间生产、销售、库存、物流等方面的协同管理.     

分布式协作模型及应用研究

With the development of the Web technology,the application environment has acquired many new characters such as dynamic, openness,distribution and information uncertainty.The processing mode of application systems is more complicated than ever.For example,it requires application systems to have more community processing ability,interactive ability,distributed processing ability and collaborative ability.Accordingly the research and development of the computer application system transited from client/server information processing system into distributed collaborative processing system based on Web.Especially in the environment where the information and resources are highly distributed,the accomplishment of complicated tasks is dependent more on the resources coordination,information sharing and coordinator collaboration.The collaboration is one aspect of the group behavior and its goal is to provide a optimal method to utilize the resource through the information interaction and to solve the task which couldn't be accomplished by each coordinator alone and get the more total benefits than the sum of each benefit.The collaboration problem is the important one for distributed tasks processing.This paper surveys,the research and application status of distributed collaborative models and several representative architectures of distributed collaborative processing are proposed.However,the existing problems and the future researching direction are presented.     

Risky business or sharing the load? - Social flow in collaborative mobile learning

Mobile learning has been built upon the premise that we can transform traditional classroom or computer-based learning activities into a more ubiquitous and connected form of learning. Tentative outcomes from this assertion have been witnessed in many collaborative learning activities, but few analytic observations on what triggers this collaboration have so far been made. However Social Flow, a concept framework that extends Csikszentmihalyi’s flow theory, may help us to partially explain the triggering mechanism of collaborative mobile learning. Our case study in this article, where learners together explore a built environment as part of a simulated security guard training programme, describes how the concept of social flow in a collaborative learning space might sketch out what triggers an optimal learning experience in collaboration and what can be additionally achieved in a collaborative learning experience. In this learning context, collaborative mobile learning might be seen to prompt more knowledge generation and extra learning tasks by fostering greater motivation than other learning environments.     

Profit allocation mechanisms for carrier collaboration in pickup and delivery service

In collaborative logistics, multiple carriers may form an alliance by sharing their transportation requests and vehicle capacities in order to increase vehicle utilization rates and reduce empty back hauls. In this paper, a carrier collaboration problem in pickup and delivery service (CCPPD) is studied. CCPPD has two important issues which are the optimal reallocation of transportation requests among the carriers to maximize a total profit of the alliance and a fair allocation of the profit among the carriers so as to maintain the stability of the alliance. In this study, the profit allocation among carriers is addressed under a centralized collaboration framework. We propose three profit allocation mechanisms which are based on Shapley value, the proportional allocation concept, and the contribution of each carrier in offering and serving requests, respectively. All the mechanisms ensure that the allocation is in the core if the latter is not empty. They are evaluated and compared with numerical experiments on randomly generated instances.