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

Decomposition, Reformulation, and Diving in University Course Timetabling

In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft constraint violation. The goal is then to minimise a linear combination of such measures. This paper studies an approach to such problems, which can be thought of as multiphase exploitation of multiple objective-/value-restricted submodels. In this approach, only one computationally difficult component of a problem and the associated subset of objectives is considered at first. This produces partial solutions, which define interesting neighbourhoods in the search space of the complete problem. Often, it is possible to pick the initial component so that variable aggregation can be performed at the first stage, and the neighbourhoods to be explored next are guaranteed to contain feasible solutions. Using integer programming, it is then easy to implement heuristics producing solutions with bounds on their quality. Our study is performed on a university course timetabling problem used in the 2007 International Timetabling Competition, also known as the Udine Course Timetabling Problem. In the proposed heuristic, an objective-restricted neighbourhood generator produces assignments of periods to events, with decreasing numbers of violations of two period-related soft constraints. Those are relaxed into assignments of events to days, which define neighbourhoods that are easier to search with respect to all four soft constraints. Integer programming formulations for all subproblems are given and evaluated using ILOG CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table

Emerging applications of fluorescence spectroscopy in medical microbiology field

There are many diagnostic techniques and methods available for diagnosis of medically important microorganisms like bacteria, viruses, fungi and parasites. But, almost all these techniques and methods have some limitations or inconvenience. Most of these techniques are laborious, time consuming and with chances of false positive or false negative results. It warrants the need of a diagnostic technique which can overcome these limitations and problems. At present, there is emerging trend to use Fluorescence spectroscopy as a diagnostic as well as research tool in many fields of medical sciences. Here, we will critically discuss research studies which propose that Fluorescence spectroscopy may be an excellent diagnostic as well as excellent research tool in medical microbiology field with high sensitivity and specificity

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.The authors wish to thank three anonymous referees for their comments and valuable suggestions to improve the paper. The first author acknowledges Ciˆencia 2007 of FCT (Foundation for Science and Technology) Portugal for the fellowship grant C2007-UMINHO-ALGORITMI-04. Financial support from FEDER COMPETE (Operational Programme Thematic Factors of Competitiveness) and FCT under project FCOMP-01-0124-FEDER-022674 is also acknowledged

Abstract Automating Branch-and-Bound for Dynamic Programs

Dynamic programming is a powerful technique for solving optimization problems efficiently. We consider a dynamic program as simply a recursive program that is evaluated with memoization and lookup of answers. In this paper we examine how, given a function calculating a bound on the value of the dynamic program, we can optimize the compilation of the dynamic program function. We show how to automatically transform a dynamic program to a number of more efficient versions making use of the bounds function. We compare the different transformed versions on a number of example dynamic programs, and show the benefits in search space and time that can result

TIP - Training intensivmedizinischer Unterstützung im Pandemie-Fall

The COVID-19 pandemic has led to a short-term sharp increase in the demand for auxiliary staff in emergency rooms and intensive care units. Against this background student tutors of the Medical Faculty Erlangen have developed a training concept. The aim was to familiarize students in the clinical section quickly and effectively with skills that are particularly important in a clinical assignment as (student) assistant in the care of corona patients (e.g.: personal protective equipment, intubation assistance, arterial blood collection, assessment of blood gas values and ventilation parameters). In a blended learning concept, learning materials were prepared in advance and then implemented and deepened in a presence phase. The selection of learning materials and the low supervision ratio (1:2) made it possible to realize an internally differentiated approach. The offer met with great interest among students of all clinical semesters and was evaluated very positively. The skills learned can be applied widely even independently of a pandemic.Die COVID-19-Pandemie hat zu einem kurzfristig stark erhöhten Bedarf an Hilfskräften in Notaufnahmen und auf Intensivstationen geführt. Vor diesem Hintergrund haben studentische TutorInnen der Medizinischen Fakultät Erlangen ein Schulungskonzept entwickelt. Ziel war es, Studierende im klinischen Abschnitt schnell und effektiv mit Fertigkeiten vertraut zu machen, die in einem klinischen Einsatz als (studentische) Hilfskraft bei der Betreuung von Corona-Patienten besonders wichtig sind (z.B.: persönliche Schutzausrüstung, Intubationsassistenz, arterielle Blutentnahme, Beurteilung von Blutgaswerten und Beatmungsparametern). In einem Blended Learning-Konzept wurden Lernmaterialien vorab bearbeitet und in einer Präsenzphase praktisch umgesetzt und vertieft. Durch die Auswahl der Lernmaterialien und den niedrigen Betreuungsschlüssel (1:2) konnte ein binnendifferenzierter Ansatz realisiert werden. Das Angebot stieß auf großes Interesse bei den Studierenden aller klinischen Semester und wurde sehr positiv evaluiert. Die erlernten Fertigkeiten sind auch unabhängig von einer Pandemie breit einsetzbar

A Brief Survey on Hybrid Metaheuristics

The combination of components from different algorithms is currently one of the most successful trends in optimization. The hybridization of metaheuristics such as ant colony optimization, evolutionary algorithms, and variable neighborhood search with techniques from operations research and artificial intelligence plays hereby an important role. The resulting hybrid algorithms are generally labelled hybrid metaheuristics. The rising of this new research field was due to the fact that the focus of research in optimization has shifted from an algorithm-oriented point of view to a problem-oriented point of view. In this brief survey on hybrid metaheuristics we provide an overview on some of the most interesting and representative developments