ImpactMap: A Collaborative Environment to Support Impact Projection of Complex Decision

The siting of Municipal Solid Waste (MSW) landfills is a complex decision process. Existing siting methods utilize expert scores to determine criteria weights, however, they ignore the uncertainty of data and criterion weights and the efficacy of results. In this study, a coupled fuzzy Multi-Criteria Decision-Making (MCDM) approach was employed to site landfills in Lanzhou, a semi-arid valley basin city in China, to enhance the spatial decision-making process. Primarily, 21 criteria were identified in five groups through the Delphi method at 30 m resolution, then criteria weights were obtained by DEMATEL and ANP, and the optimal fuzzy membership function was determined for each evaluation criterion. Combined with GIS spatial analysis and the clustering algorithm, candidate sites that satisfied the landfill conditions were identified, and the spatial distribution characteristics were analyzed. These sites were subsequently ranked utilizing the MOORA, WASPAS, COPRAS, and TOPSIS methods to verify the reliability of the results by conducting sensitivity analysis. This study is different from the previous research that applied the MCDM approach in that fuzzy MCDM for weighting criteria is more reliable compared to the other common methods.

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A Delphi Study Investigating Clinicians’ Views on Access to, Delivery of, and Adaptations of MBCT in the UK Clinical Settings

Mindfulness ◽

10.1007/s12671-021-01706-5 ◽

2021 ◽

Author(s):

Kate Williams ◽

Samantha Hartley ◽

Peter Taylor

Keyword(s):

Delphi Study ◽

Good Practice ◽

Evidence Base ◽

Clinical Settings ◽

Training Support ◽

Decision Making Processes ◽

Complex Decision Making ◽

Complex Decision ◽

The Uk ◽

Clinical Populations

Abstract Objectives Mindfulness-based cognitive therapy (MBCT) is a well-evidenced relapse-prevention intervention for depression with a growing evidence-base for use in other clinical populations. The UK initiatives have outlined plans for increasing access to MBCT in clinical settings, although evidence suggests that access remains limited. Given the increased popularity and access to MBCT, there may be deviations from the evidence-base and potential risks of harm. We aimed to understand what clinicians believe should be best clinical practice regarding access to, delivery of, and adaptations to MBCT. Methods We employed a two-stage Delphi methodology. First, to develop statements around best practices, we consulted five mindfulness-based experts and reviewed the literature. Second, a total of 59 statements were taken forward into three survey rating rounds. Results Twenty-nine clinicians completed round one, with 25 subsequently completing both rounds two and three. Forty-four statements reached consensus; 15 statements did not. Clinicians agreed with statements regarding sufficient preparation for accessing MBCT, adherence to the evidence-base and good practice guidelines, consideration of risks, sufficient access to training, support, and resources within services, and carefully considered adaptations. The consensus was not reached on statements which reflected a lack of evidence-base for specific clinical populations or the complex decision-making processes involved in delivering and making adaptations to MBCT. Conclusions Our findings highlight the delicate balance of maintaining a client-centred and transparent approach whilst adhering to the evidence-base in clinical decisions around access to, delivery of, and adaptations in MBCT and have important wide-reaching implications.

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Priority and Dependency-Based DAG Tasks Offloading in Fog/Edge Collaborative Environment

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) ◽

10.1109/cscwd49262.2021.9437784 ◽

2021 ◽

Author(s):

Xing Fu ◽

Bing Tang ◽

Feiyan Guo ◽

Linyao Kang

Keyword(s):

Collaborative Environment

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STANDARDISING MAINTENANCE JOBS TO IMPROVE GROUPING DECISION MAKING

Proceedings of the Design Society ◽

10.1017/pds.2021.531 ◽

2021 ◽

Vol 1 ◽

pp. 2701-2710

Author(s):

Julie Krogh Agergaard ◽

Kristoffer Vandrup Sigsgaard ◽

Niels Henrik Mortensen ◽

Jingrui Ge ◽

Kasper Barslund Hansen ◽

...

Keyword(s):

Decision Making ◽

Tacit Knowledge ◽

True Variance ◽

Complex Decision ◽

Maintenance Decision ◽

Maintenance Activities

AbstractMaintenance decision making is an important part of managing the costs, effectiveness and risk of maintenance. One way to improve maintenance efficiency without affecting the risk picture is to group maintenance jobs. Literature includes many examples of algorithms for the grouping of maintenance activities. However, the data is not always available, and with increasing plant complexity comes increasingly complex decision requirements, making it difficult to leave the decision making up to algorithms.This paper suggests a framework for the standardisation of maintenance data as an aid for maintenance experts to make decisions on maintenance grouping. The standardisation improves the basis for decisions, giving an overview of true variance within the available data. The goal of the framework is to make it simpler to apply tacit knowledge and make right decisions.Applying the framework in a case study showed that groups can be identified and reconfigured and potential savings easily estimated when maintenance jobs are standardised. The case study enabled an estimated 7%-9% saved on the number of hours spent on the investigated jobs.

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Goal-driven active learning

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-021-09527-5 ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Nicolas Bougie ◽

Ryutaro Ichise

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Learning Process ◽

Real World ◽

Imitation Learning ◽

Learning Approaches ◽

Wide Range ◽

Fixed Set ◽

Complex Decision Making ◽

Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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