Metaheuristics for solving a multimodal home-healthcare scheduling problem

Abstract We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds

A cognitive framework for object recognition with application to autonomous vehicles

Autonomous vehicles or self-driving cars are capable of sensing the surrounding environment so they can navigate roads without human input. Decisions are constantly made on sensing, mapping and driving policy using machine learning techniques. Deep Learning – massive neural networks that utilize the power of parallel processing – has become a popular choice for addressing the complexities of real time decision making. This method of machine learning has been shown to outperform alternative solutions in multiple domains, and has an architecture that can be adapted to new problems with relative ease. To harness the power of Deep Learning, it is necessary to have large amounts of training data that are representative of all possible situations the system will face. To successfully implement situational awareness in driverless vehicles, it is not possible to exhaust all possible training examples. An alternative method is to apply cognitive approaches to perception, for situations the autonomous vehicles will face. Cognitive approaches to perception work by mimicking the process of human intelligence – thereby permitting a machine to react to situations it has not previously experienced. This paper proposes a novel cognitive approach for object recognition. The proposed cognitive object recognition algorithm, referred to as Recognition by Components, is inspired by the psychological studies pertaining to early childhood development. The algorithm works by breaking down images into a series of primitive forms such as square, triangle, circle or rectangle and memory based aggregation to identify objects. Experimental results suggest that Recognition by Component algorithm performs significantly better than algorithms that require large amounts of training data

New cellular tools reveal complex epithelial–mesenchymal interactions in hepatocarcinogenesis

To enable detailed analyses of cell interactions in tumour development, new epithelial and mesenchymal cell lines were established from human hepatocellular carcinoma by spontaneous outgrowth in culture. We obtained several hepatocarcinoma (HCC)-, B-lymphoblastoid (BLC)-, and myofibroblastoid (MF)-lines from seven cases. In-depth characterisation included cell kinetics, genotype, tumourigenicity, expression of cell-type specific markers, and proteome patterns. Many functions of the cells of origin were found to be preserved. We studied the impact of the mesenchymal lines on hepatocarcinogenesis by in vitro assays. BLC- and MF-supernatants strongly increased the DNA replication of premalignant hepatocytes. The stimulation by MF-lines was mainly attributed to HGF secretion. In HCC-cells, MF-supernatant had only minor effects on cell growth but enhanced migration. MF-lines also stimulated neoangiogenesis through vEGF release. BLC-supernatant dramatically induced death of HCC-cells, which could be largely abrogated by preincubating the supernatant with TNFβ-antiserum. Thus, the new cell lines reveal stage-specific stimulatory and inhibitory interactions between mesenchymal and epithelial tumour cells. In conclusion, the new cell lines provide unique tools to analyse essential components of the complex interplay between the microenvironment and the developing liver cancer, and to identify factors affecting proliferation, migration and death of tumour cells, neoangiogenesis, and outgrowth of additional malignancy

Solving the post enrolment course timetabling problem by ant colony optimization.

Abstract In this work we present a new approach to tackle the problem of Post Enrolment Course Timetabling as specified for the International Timetabling Competition 2007 (ITC2007), competition track 2. The heuristic procedure is based on Ant Colony Optimization (ACO) where artificial ants successively construct solutions based on pheromones (stigmergy) and local information. The key feature of our algorithm is the use of two distinct but simplified pheromone matrices in order to improve convergence but still provide enough flexibility for effectively guiding the solution construction process. We show that by parallelizing the algorithm we can improve the solution quality significantly. We applied our algorithm to the instances used for the ITC2007. The results document that our approach is among the leading algorithms for this problem; in all cases the optimal solution could be found. Furthermore we discuss the characteristics of the instances where the algorithm performs especially well

Applications of evolutionary computing

This book constitutes the joint refereed proceedings of six workshops on evolutionary computing, EvoWorkshops 2004, held together with EuroGP 2004 and EvoCOP ..

Applications of evolutionary computing

This book constitutes the joint refereed proceedings of six workshops on evolutionary computing, EvoWorkshops 2004, held together with EuroGP 2004 and EvoCOP ..