Time-Dependent Alternative Route Planning

We present a new method for computing a set of alternative origin-to-destination routes in road networks with an underlying time-dependent metric. The resulting set is aggregated in the form of a time-dependent alternative graph and is characterized by minimum route overlap, small stretch factor, small size and low complexity. To our knowledge, this is the first work that deals with the time-dependent setting in the framework of alternative routes. Based on preprocessed minimum travel-time information between a small set of nodes and all other nodes in the graph, our algorithm carries out a collection phase for candidate alternative routes, followed by a pruning phase that cautiously discards uninteresting or low-quality routes from the candidate set. Our experimental evaluation on real time-dependent road networks demonstrates that the new algorithm performs much better (by one or two orders of magnitude) than existing baseline approaches. In particular, the entire alternative graph can be computed in less than 0.384sec for the road network of Germany, and in less than 1.24sec for that of Europe. Our approach provides also "quick-and-dirty" results of decent quality, in about 1/300 of the above mentioned query times for continental-size instances

Resource constrained routing and scheduling: Review and research prospects

In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects

Pre-plant release enhanced the earlier establishment of Nesidiocoris tenuis in open field tomato

Ο εχθρός της τομάτας, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) που εισέβαλε στη χώρα μας, πολύ σύντομα έγινε σημαντικός εχθρός τόσο των υπαίθριων όσο και των θερμοκηπιακών καλλιεργειών. Η εξαπόλυση του αρπακτικού Nesidiocoris tenuis (Reuter) (Heteroptera: Miridae) στο φυτώριο έχει βρεθεί ότι μπορεί να συμβάλει στην αντιμετώπιση του T. absoluta σε καλλιέργειες τομάτας στο θερμοκήπιο. Ο στόχος της παρούσας μελέτης είναι να αξιολογηθεί κατά πόσο σε υπαίθρια καλλιέργεια τομάτας, η εξαπόλυση του N. Tenuis στο φυτώριο μπορεί να συμβάλει στην έγκαιρη εγκατάστασή του. Συμπερασματικά, η εξαπόλυση του N. tenuis συνέβαλε ουσιαστικά στην έγκαιρη αποίκηση της καλλιέργειας σε σύγκριση με τα τεμάχια του μάρτυρα. Επίσης, ο ρόλος του ιθαγενούς πληθυσμού του M. pygmaeus πρέπει να λαμβάνεται σοβαρά υπόψη στα προγράμματα ολοκληρωμένης αντιμετώπισης. Η μέθοδος αυτή είναι αποτελεσματική στην πιο έγκαιρη εγκατάσταση του N. tenuis και θα πρέπει να αξιολογηθεί περαιτέρω η συμβολή της στην αντιμετώπιση του Τ. absoluta.The invasive pest of the tomato crops, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), soon after its establishment became a major problem of outdoor and greenhouse tomato crops across the Mediterranean countries. The pre-plant release of the predator Nesidiocoris tenuis (Reuter) (Heteroptera: Miridae) has been found to substantially contribute to its establishment on the crop and efficient control of T. absoluta in greenhouses. The aim of the current study was to assess whether the pre-plant release of N. tenuis could contribute to its earlier establishment. It was concluded that the release of N. tenuis in the nursery contributed substantially in the earlier increase of its numbers in the field in comparison to the control plots. The native populations of M. pygmaeus appeared on the tomato plants before the native populations of N. tenuis. Thus, the conservation of both predators should be taken into consideration in the integrated management strategies against T. absoluta. Therefore, the application of this method in open field tomato crop enhances the earlier establishment of N. tenuis and should be further evaluated in the control of T. absoluta

The Impact of Math Anxiety on Working Memory:A Cortical Activations and Cortical Functional Connectivity EEG Study

Mathematical anxiety (MA) is defined as a feeling of tension, apprehension, or fear that interferes with mathematical performance in various daily or academic situations. Cognitive consequences of MA have been studied a lot and revealed that MA seriously affects solving the complex problem due to the corruption of working memory (WM). The corruption of WM caused by MA is well documented in behavioral level, but the involved neurophysiological processes have not been properly addressed, despite the recent attention drawn on the neural basis of MA. This is the second part of our study that intents to investigate the neurophysiological aspects of MA and its implications to WM. In the first study, we saw how MA affects the early stages of numeric stimuli processes as the WM indirectly using event-related potentials in scalp electroencephalographic (EEG) signals. This paper goes one step further to investigate the cortical activations, obtained by the multichannel EEG recordings as well as the cortical functional networks in three WM tasks with increasing difficulty. Our results indicate that the high-math anxious (HMA) group activated more areas linked with negative emotions, pain, and fear, while the low-math anxious (LMA) group activated regions related to the encoding and retrieval processes of the WM. Functional connectivity analysis also reveals that the LMAs' brain has got more structured cortical networks with increased connectivity in areas related to WM, such as the frontal cortex, while the HMAs' brain has a more diffused and unstructured network, superimposing the evidence that the structured processes of WM are corrupted

A cycle-based evolutionary algorithm for the fixed-charge capacitated multi-commodity network design problem

This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times

Creative Information Exploration in Journalism

Existing digital tools used by journalists such as content management systems and search engines focus on helping journalists find relevant information and organize their creative work; they do not provide support for discovering creative angles to investigate. To meet this need in journalism, we have researched new creative search algorithms that manipulate the journalist’s primary medium – written information – and developed an innovative creativity and productivity support tool deliberately for use by journalists. In this paper, we analyse the creative tasks journalists perform when developing new stories, we provide an overview of requirements, which have been addressed by the INJECT digital creativity support tool for journalists. Further, we explain the intended use of the system using a walkthrough scenario and present our early experiences from deploying the tool in journalist’s working environments

Engineering Graph-Based Models for Dynamic Timetable Information Systems

Many efforts have been done in the last years to model public transport timetables in order to find optimal routes. The proposed models can be classified into two types: those representing the timetable as an array, and those representing it as a graph. The array-based models have been shown to be very effective in terms of query time, while the graph-based models usually answer queries by computing shortest paths, and hence they are suitable to be used in combination with speed-up techniques developed for road networks. In this paper, we focus on the dynamic behavior of graph-based models considering the case where transportation systems are subject to delays with respect to the given timetable. We make three contributions: (i) we give a simplified and optimized update routine for the well-known time-expanded model along with an engineered query algorithm; (ii) we propose a new graph-based model tailored for handling dynamic updates; (iii) we assess the effectiveness of the proposed models and algorithms by an experimental study, which shows that both models require negligible update time and a query time which is comparable to that required by some array-based models

An adaptive memory programming framework for the resource-constrained project scheduling problem

The Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most intractable combinatorial optimisation problems that combines a set of constraints and objectives met in a vast variety of applications and industries. Its solution raises major theoretical challenges due to its complexity, yet presenting numerous practical dimensions. Adaptive memory programming (AMP) is one of the most successful frameworks for solving hard combinatorial optimisation problems (e.g. vehicle routing and scheduling). Its success stems from the use of learning mechanisms that capture favourable solution elements found in high-quality solutions. This paper challenges the efficiency of AMP for solving the RCPSP, to our knowledge, for the first time in the literature. Computational experiments on well-known benchmark RCPSP instances show that the proposed AMP consistently produces high-quality solutions in reasonable computational times