1,721,133 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An exact solution framework for multitrip vehicle-routing problems with time windows
Full text linkMultitrip vehicle-routing problems (MTVRPs) generalize the well-known VRP by allowing vehicles to perform multiple trips per day. MTVRPs have received a lot of attention lately because of their relevance in real-life applications - for example, in city logistics and last-mile delivery. Several variants of the MTVRP have been investigated in the literature, and a number of exact methods have been proposed. Nevertheless, the computational results currently available suggest that MTVRPs with different side constraints require ad hoc formulations and solution methods to be solved. Moreover, solving instances with just 25 customers can be out of reach for such solution methods. In this paper, we proposed an exact solution framework to address four different MTVRPs proposed in the literature. The exact solution framework is based on a novel formulation that has an exponential number of variables and constraints. It relies on column generation, column enumeration, and cutting plane. We show that this solution framework can solve instances with up to 50 customers of four MTVRP variants and outperforms the state-of-the-art methods from the literature
Distribution with Quality of Service Considerations: The Capacitated Routing Problem with Profits and Service Level Requirements
No full textInspired by a problem arising in cash logistics, we propose the Capacitated Routing Problem with Profits and Service Level Requirements (CRPPSLR). The CRPPSLR extends the class of Routing Problems with Profits by considering customers requesting deliveries to their (possibly multiple) service points. Moreover, each customer imposes a service level requirement specifying a minimum-acceptable bound on the fraction of its service points being delivered. A customer-specific financial penalty is incurred by the logistics service provider when this requirement is not met. The CRPPSLR consists in finding vehicle routes maximizing the difference between the collected revenues and the incurred transportation and penalty costs in such a way that vehicle capacity and route duration constraints are met. A fleet of homogeneous vehicles is available for serving the customers. We design a branch-and-cut algorithm and evaluate the usefulness of valid inequalities that have been effectively used for the capacitated vehicle routing problem and, more recently, for other routing problems with profits. A real-life case study taken from the cash supply chain in the Netherlands highlights the relevance of the problem under consideration. Computational results illustrate the performance of the proposed solution approach under different input parameter settings for the synthetic instances. For instances of real-life problems, we distinguish between coin and banknote distribution, as vehicle capacities only matter when considering the former. Finally, we report on the effectiveness of the valid inequalities in closing the optimality gap at the root node for both the synthetic and the real-life instances and conclude with a sensitivity analysis on the most significant input parameters of our model
Load Management with Predictions of Solar Energy Production for Cloud Data Centers
Full text linkPower supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raises also concerns in terms of sustainability. In this context, renewable energy generators can help to both reduce costs and alleviate the concerns of sustainability of big infrastructures. In this paper, we consider the case of Data Centers (DCs) composed of a few sites located in different geographical positions and powered with solar energy. Due to the intermittent nature of solar energy, different time zones and price of electricity in different locations, load management strategies are fundamental. We consider predictions of the solar energy production performed through Artificial Neural Networks and we assess the impact of predictions on load management decisions and, ultimately, on the DC performance
An efficient heuristic for very large-scale vehicle routing problems with simultaneous pickup and delivery
No full textThe paper discusses two variants of the Capacitated Vehicle Routing Problem (CVRP), in which each customer to be visited can require both pickup and delivery, or only either pickup and delivery but not both. These problems are referred to, respectively, as the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) and as the Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD). Both VRPSPD and VRPMPD are particularly relevant in practical logistics distribution scenarios, especially when dealing with a substantial number of pickup and delivery locations. The aim of this work is to provide an algorithm which extends, through a variety of specializations, the FILO framework, originally proposed and specifically designed for the CVRP. This variant, called FSPD, has been developed to accomplish two objectives: first, being competitive with the state-of-the-art algorithms for the VRPSPD and VRPMPD; second, efficiently solving new benchmark instances for these problems with a very large number of customers, while maintaining linear scalability of the computing time with respect to the problem size. The extensive computational study and detailed analysis of the algorithm components conducted in this paper demonstrate the successful achievement of both objectives
- …
