Heuristics that dynamically alter data structures to decrease their access time /

Vita.

Computer based approaches to managerial decision making:optimism, heuristics and simulation

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Classifying clinical decision making:interpreting nursing intuition, heuristics and medical diagnosis

This is the second of two linked papers exploring decision making in nursing. The first paper, 'Classifying clinical decision making: a unifying approach' investigated difficulties with applying a range of decision-making theories to nursing practice. This is due to the diversity of terminology and theoretical concepts used, which militate against nurses being able to compare the outcomes of decisions analysed within different frameworks. It is therefore problematic for nurses to assess how good their decisions are, and where improvements can be made. However, despite the range of nomenclature, it was argued that there are underlying similarities between all theories of decision processes and that these should be exposed through integration within a single explanatory framework. A proposed solution was to use a general model of psychological classification to clarify and compare terms, concepts and processes identified across the different theories. The unifying framework of classification was described and this paper operationalizes it to demonstrate how different approaches to clinical decision making can be re-interpreted as classification behaviour. Particular attention is focused on classification in nursing, and on re-evaluating heuristic reasoning, which has been particularly prone to theoretical and terminological confusion. Demonstrating similarities in how different disciplines make decisions should promote improved multidisciplinary collaboration and a weakening of clinical elitism, thereby enhancing organizational effectiveness in health care and nurses' professional status. This is particularly important as nurses' roles continue to expand to embrace elements of managerial, medical and therapeutic work. Analysing nurses' decisions as classification behaviour will also enhance clinical effectiveness, and assist in making nurses' expertise more visible. In addition, the classification framework explodes the myth that intuition, traditionally associated with nurses' decision making, is less rational and scientific than other approaches.

Lagrangean Approximation for Combinatorial Inverse Problems

Various combinatorial problems are effectively modelled in terms of (0,1) matrices. Origins are coming from n-cube geometry, hypergraph theory, inverse tomography problems, or directly from different models of application problems. Basically these problems are NP-complete. The paper considers a set of such problems and introduces approximation algorithms for their solutions applying Lagragean relaxation and related set of techniques.

Feasibility, normative heuristics and the proper place of historical responsibility – a reply to Ohndorf et al.

In this comment, we pick up three points raised by Ohndorf et al. (2015) in their reply to our ethical assessment of the German Advisory Council’s Budget Approach (WBGUBA). First, we discuss and clarify the relationship between ethics and political feasibility, highlighting that the way Ohndorf et el. use feasibility creates an unwarranted status quo bias. Second, we explain the proper place historical responsibility should have within the WBGUBA, stressing the fact that the reasons why we choose one policy proposal over another matter. Third, we analyze the limited extent to which a normative heuristic should motivate an ethically ambitious policy proposal like the WBGUBA.

Exploring problem-framing through behavioural heuristics

Design for behaviour change aims to influence user behaviour, through design, for social or environmental benefit. Understanding and modelling human behaviour has thus come within the scope of designers’work, as in interaction design, service design and user experience design more generally. Diverse approaches to how to model users when seeking to influence behaviour can result in many possible strategies, but a major challenge for the field is matching appropriate design strategies to particular behaviours (Zachrisson & Boks, 2012). In this paper, we introduce and explore behavioural heuristics as a way of framing problem-solution pairs (Dorst & Cross, 2001) in terms of simple rules. These act as a ‘common language’ between insights from user research and design principles and techniques, and draw on ideas from human factors, behavioural economics, and decision research. We introduce the process via a case study on interaction with office heating systems, based on interviews with 16 people. This is followed by worked examples in the ‘other direction’, based on a workshop held at the Interaction ’12 conference, extracting heuristics from existing systems designed to influence user behaviour, to illustrate both ends of a possible design process using heuristics.

Heuristics for the two-stage job shop scheduling problem with a bottleneck machine

Abstract not available

Heuristics for short route job shop scheduling problems

Abstract not available

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.

Vehicle Routing Problems that Minimize the Completion Time: Heuristics, Worst-Case Analyses, and Computational Results

In the standard Vehicle Routing Problem (VRP), we route a fleet of vehicles to deliver the demands of all customers such that the total distance traveled by the fleet is minimized. In this dissertation, we study variants of the VRP that minimize the completion time, i.e., we minimize the distance of the longest route. We call it the min-max objective function. In applications such as disaster relief efforts and military operations, the objective is often to finish the delivery or the task as soon as possible, not to plan routes with the minimum total distance. Even in commercial package delivery nowadays, companies are investing in new technologies to speed up delivery instead of focusing merely on the min-sum objective. In this dissertation, we compare the min-max and the standard (min-sum) objective functions in a worst-case analysis to show that the optimal solution with respect to one objective function can be very poor with respect to the other. The results motivate the design of algorithms specifically for the min-max objective. We study variants of min-max VRPs including one problem from the literature (the min-max Multi-Depot VRP) and two new problems (the min-max Split Delivery Multi-Depot VRP with Minimum Service Requirement and the min-max Close-Enough VRP). We develop heuristics to solve these three problems. We compare the results produced by our heuristics to the best-known solutions in the literature and find that our algorithms are effective. In the case where benchmark instances are not available, we generate instances whose near-optimal solutions can be estimated based on geometry. We formulate the Vehicle Routing Problem with Drones and carry out a theoretical analysis to show the maximum benefit from using drones in addition to trucks to reduce delivery time. The speed-up ratio depends on the number of drones loaded onto one truck and the speed of the drone relative to the speed of the truck.

Heuristics-based Indoor positioning systems: a systematic literature review

Context: Many heuristics-based indoor positioning approaches havebeen developed to enhance positioning estimation. However, thereis no comprehensive survey of these heuristics information and methods.