People Efficiently Explore the Solution Space of the Computationally Intractable Traveling Salesman Problem to Find Near-Optimal Tours

Humans need to solve computationally intractable problems such as visual search, categorization, and simultaneous learning and acting, yet an increasing body of evidence suggests that their solutions to instantiations of these problems are near optimal. Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal. We hypothesize that these two ideas largely account for the good performance of humans on computationally hard problems. We tested part of this hypothesis by studying the solutions of 28 participants to 28 instances of the Euclidean Traveling Salesman Problem (TSP). Participants were provided feedback on the cost of their solutions and were allowed unlimited solution attempts (trials). We found a significant improvement between the first and last trials and that solutions are significantly different from random tours that follow the convex hull and do not have self-crossings. More importantly, we found that participants modified their current better solutions in such a way that edges belonging to the optimal solution (“good” edges) were significantly more likely to stay than other edges (“bad” edges), a hallmark of structural exploitation. We found, however, that more trials harmed the participants' ability to tell good from bad edges, suggesting that after too many trials the participants “ran out of ideas.” In sum, we provide the first demonstration of significant performance improvement on the TSP under repetition and feedback and evidence that human problem-solving may exploit the structure of hard problems paralleling behavior of state-of-the-art heuristics

A realist analysis of hospital patient safety in Wales:Applied learning for alternative contexts from a multisite case study

Background: Hospital patient safety is a major social problem. In the UK, policy responses focus on the introduction of improvement programmes that seek to implement evidence-based clinical practices using the Model for Improvement, Plan-Do-Study-Act cycle. Empirical evidence that the outcomes of such programmes vary across hospitals demonstrates that the context of their implementation matters. However, the relationships between features of context and the implementation of safety programmes are both undertheorised and poorly understood in empirical terms. Objectives: This study is designed to address gaps in conceptual, methodological and empirical knowledge about the influence of context on the local implementation of patient safety programmes. Design: We used concepts from critical realism and institutional analysis to conduct a qualitative comparative-intensive case study involving 21 hospitals across all seven Welsh health boards. We focused on the local implementation of three focal interventions from the 1000 Lives+ patient safety programme: Improving Leadership for Quality Improvement, Reducing Surgical Complications and Reducing Health-care Associated Infection. Our main sources of data were 160 semistructured interviews, observation and 1700 health policy and organisational documents. These data were analysed using the realist approaches of abstraction, abduction and retroduction. Setting: Welsh Government and NHS Wales. Participants: Interviews were conducted with 160 participants including government policy leads, health managers and professionals, partner agencies with strategic oversight of patient safety, advocacy groups and academics with expertise in patient safety. Main outcome measures: Identification of the contextual factors pertinent to the local implementation of the 1000 Lives+ patient safety programme in Welsh NHS hospitals. Results: An innovative conceptual framework harnessing realist social theory and institutional theory was produced to address challenges identified within previous applications of realist inquiry in patient safety research. This involved the development and use of an explanatory intervention–context–mechanism–agency–outcome (I-CMAO) configuration to illustrate the processes behind implementation of a change programme. Our findings, illustrated by multiple nested I-CMAO configurations, show how local implementation of patient safety interventions are impacted and modified by particular aspects of context: specifically, isomorphism, by which an intervention becomes adapted to the environment in which it is implemented; institutional logics, the beliefs and values underpinning the intervention and its source, and their perceived legitimacy among different groups of health-care professionals; and the relational structure and power dynamics of the functional group, that is, those tasked with implementing the initiative. This dynamic interplay shapes and guides actions leading to the normalisation or the rejection of the patient safety programme. Conclusions: Heightened awareness of the influence of context on the local implementation of patient safety programmes is required to inform the design of such interventions and to ensure their effective implementation and operationalisation in the day-to-day practice of health-care teams. Future work is required to elaborate our conceptual model and findings in similar settings where different interventions are introduced, and in different settings where similar innovations are implemented. Funding: The National Institute for Health Research Health Services and Delivery Research programme

Design and analysis of stochastic local search for the multiobjective traveling salesman problem

Stochastic local search (SLS) algorithms are typically composed of a number of different components, each of which should contribute significantly to the final algorithm's performance. If the goal is to design and engineer effective SLS algorithms, the algorithm developer requires some insight into the importance and the behavior of possible algorithmic components. In this paper, we analyze algorithmic components of SLS algorithms for the multiobjective travelling salesman problem. The analysis is done using a careful experimental design for a generic class of SLS algorithms for multiobjective combinatorial optimization. Based on the insights gained, we engineer SLS algorithms for this problem. Experimental results show that these SLS algorithms, despite their conceptual simplicity, outperform a well-known memetic algorithm for a range of benchmark instances with two and three objectives. © 2008 Elsevier Ltd. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe