scholarly journals Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Markus Rabe ◽  
Majsa Ammouriova ◽  
Dominik Schmitt ◽  
Felix Dross
Keyword(s):  
Reinforcement Learning ◽  
Response Time ◽  
Real World ◽  
Computation Time ◽  
Service Level ◽  
Specific Information ◽  
Real World Data ◽  
Domain Specific ◽  
Distribution Process ◽  
The One

The distribution process in business-to-business materials trading is among the most complex and in transparent ones within logistics. The highly volatile environment requires continuous adaptations by the responsible decision-makers, who face a substantial number of potential improvement actions with conflicting goals, such as simultaneously maintaining a high service level and low costs. Simulation-optimisation approaches have been proposed in this context, for example based on evolutionary algorithms. But, on real-world system dimensions, they face impractically long computation times. This paper addresses this challenge in two principal streams. On the one hand, reinforcement learning is investigated to reduce the response time of the system in a concrete decision situation. On the other hand, domain-specific information and defining equivalent solutions are exploited to support a metaheuristic algorithm. For these approaches, we have developed suitable implementations and evaluated them with subsets of real-world data. The results demonstrate that reinforcement learning exploits the idle time between decision situations to learn which decisions might be most promising, thus adding computation time but significantly reducing the response time. Using domain-specific information reduces the number of required simulation runs and guides the search for promising actions. In our experimentation, defining equivalent solutions decreased the number of required simulation runs up to 15%.

scholarly journals Meta Reinforcement Learning with Task Embedding and Shared Policy

2019 ◽  
Author(s):  
Lin Lan ◽  
Zhenguo Li ◽  
Xiaohong Guan ◽  
Pinghui Wang
Keyword(s):  
Reinforcement Learning ◽  
The Other ◽  
Specific Information ◽  
Significant Progress ◽  
Learning To Learn ◽  
Learning Capacity ◽  
Shared Information ◽  
Meta Learning ◽  
The One ◽  
High Level

Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner from a set of RL tasks such that a novel but related task could be solved quickly. Though specific in some ways, different tasks in meta-RL are generally similar at a high level. However, most meta-RL methods do not explicitly and adequately model the specific and shared information among different tasks, which limits their ability to learn training tasks and to generalize to novel tasks. In this paper, we propose to capture the shared information on the one hand and meta-learn how to quickly abstract the specific information about a task on the other hand. Methodologically, we train an SGD meta-learner to quickly optimize a task encoder for each task, which generates a task embedding based on past experience. Meanwhile, we learn a policy which is shared across all tasks and conditioned on task embeddings. Empirical results on four simulated tasks demonstrate that our method has better learning capacity on both training and novel tasks and attains up to 3 to 4 times higher returns compared to baselines.

scholarly journals Multi-View Multi-Label Learning with View-Specific Information Extraction

2019 ◽  
Author(s):  
Xuan Wu ◽  
Qing-Guo Chen ◽  
Yao Hu ◽  
Dengbao Wang ◽  
Xiaodong Chang ◽  
...  
Keyword(s):  
Information Extraction ◽  
Real World ◽  
State Of The Art ◽  
Specific Information ◽  
Learning Approach ◽  
Data Sets ◽  
Learning Approaches ◽  
Real World Data ◽  
Learning Techniques ◽  
Shared Information

Multi-view multi-label learning serves an important framework to learn from objects with diverse representations and rich semantics. Existing multi-view multi-label learning techniques focus on exploiting shared subspace for fusing multi-view representations, where helpful view-specific information for discriminative modeling is usually ignored. In this paper, a novel multi-view multi-label learning approach named SIMM is proposed which leverages shared subspace exploitation and view-specific information extraction. For shared subspace exploitation, SIMM jointly minimizes confusion adversarial loss and multi-label loss to utilize shared information from all views. For view-specific information extraction, SIMM enforces an orthogonal constraint w.r.t. the shared subspace to utilize view-specific discriminative information. Extensive experiments on real-world data sets clearly show the favorable performance of SIMM against other state-of-the-art multi-view multi-label learning approaches.

scholarly journals Learning an Optimization Algorithm Through Human Design Iterations

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Thurston Sexton ◽  
Max Yi Ren
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayesian Optimization ◽  
Search Performance ◽  
Human Beings ◽  
Domain Specific ◽  
Human Participants ◽  
The One ◽  
Lack Of Commitment ◽  
And Control

Solving optimal design problems through crowdsourcing faces a dilemma: On the one hand, human beings have been shown to be more effective than algorithms at searching for good solutions of certain real-world problems with high-dimensional or discrete solution spaces; on the other hand, the cost of setting up crowdsourcing environments, the uncertainty in the crowd's domain-specific competence, and the lack of commitment of the crowd contribute to the lack of real-world application of design crowdsourcing. We are thus motivated to investigate a solution-searching mechanism where an optimization algorithm is tuned based on human demonstrations on solution searching, so that the search can be continued after human participants abandon the problem. To do so, we model the iterative search process as a Bayesian optimization (BO) algorithm and propose an inverse BO (IBO) algorithm to find the maximum likelihood estimators (MLEs) of the BO parameters based on human solutions. We show through a vehicle design and control problem that the search performance of BO can be improved by recovering its parameters based on an effective human search. Thus, IBO has the potential to improve the success rate of design crowdsourcing activities, by requiring only good search strategies instead of good solutions from the crowd.

scholarly journals Dynamic Repositioning to Reduce Lost Demand in Bike Sharing Systems

2017 ◽  
Vol 58 ◽  
pp. 387-430 ◽  
Author(s):  
Supriyo Ghosh ◽  
Pradeep Varakantham ◽  
Yossiri Adulyasak ◽  
Patrick Jaillet
Keyword(s):  
Real World ◽  
Traffic Congestion ◽  
Computation Time ◽  
Significant Loss ◽  
Base Stations ◽  
Data Sets ◽  
Nonrenewable Resources ◽  
Real World Data ◽  
World Data ◽  
Bike Sharing

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing data has shown that congestion/starvation is a common phenomenon that leads to a large number of unsatisfied customers resulting in a significant loss in customer demand. In order to tackle this problem, we propose an optimisation formulation to reposition bikes using vehicles while also considering the routes for vehicles and future expected demand. Furthermore, we contribute two approaches that rely on decomposability in the problem (bike repositioning and vehicle routing) and aggregation of base stations to reduce the computation time significantly. Finally, we demonstrate the utility of our approach by comparing against two benchmark approaches on two real-world data sets of bike sharing systems. These approaches are evaluated using a simulation where the movements of customers are generated from real-world data sets.

scholarly journals Learning to Follow Directions in Street View

2020 ◽  
Vol 34 (07) ◽  
pp. 11773-11781 ◽  
Author(s):  
Karl Moritz Hermann ◽  
Mateusz Malinowski ◽  
Piotr Mirowski ◽  
Andras Banki-Horvath ◽  
Keith Anderson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Reinforcement Learning ◽  
Real World ◽  
Real World Data ◽  
Simulated Environments ◽  
Extensive Analysis ◽  
Street View ◽  
Generalisation Ability ◽  
Real Places

Navigating and understanding the real world remains a key challenge in machine learning and inspires a great variety of research in areas such as language grounding, planning, navigation and computer vision. We propose an instruction-following task that requires all of the above, and which combines the practicality of simulated environments with the challenges of ambiguous, noisy real world data. StreetNav is built on top of Google Street View and provides visually accurate environments representing real places. Agents are given driving instructions which they must learn to interpret in order to successfully navigate in this environment. Since humans equipped with driving instructions can readily navigate in previously unseen cities, we set a high bar and test our trained agents for similar cognitive capabilities. Although deep reinforcement learning (RL) methods are frequently evaluated only on data that closely follow the training distribution, our dataset extends to multiple cities and has a clean train/test separation. This allows for thorough testing of generalisation ability. This paper presents the StreetNav environment and tasks, models that establish strong baselines, and extensive analysis of the task and the trained agents.

scholarly journals Response Time, Visual Search Strategy, and Anticipatory Skills in Volleyball Players

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Alessandro Piras ◽  
Roberto Lobietti ◽  
Salvatore Squatrito
Keyword(s):  
Response Time ◽  
Search Strategy ◽  
Superior Performance ◽  
Specific Information ◽  
Eye Tracker ◽  
Domain Specific ◽  
Visuomotor Task ◽  
Volleyball Players ◽  
Ball Contact ◽  
Gaze Behaviour

This paper aimed at comparing expert and novice volleyball players in a visuomotor task using realistic stimuli. Videos of a volleyball setter performing offensive action were presented to participants, while their eye movements were recorded by a head-mounted video based eye tracker. Participants were asked to foresee the direction (forward or backward) of the setter’s toss by pressing one of two keys. Key-press response time, response accuracy, and gaze behaviour were measured from the first frame showing the setter’s hand-ball contact to the button pressed by the participants. Experts were faster and more accurate in predicting the direction of the setting than novices, showing accurate predictions when they used a search strategy involving fewer fixations of longer duration, as well as spending less time in fixating all display areas from which they extract critical information for the judgment. These results are consistent with the view that superior performance in experts is due to their ability to efficiently encode domain-specific information that is relevant to the task.

scholarly journals Compression-Based Methods of Time Series Forecasting

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 284
Author(s):  
Konstantin Chirikhin ◽  
Boris Ryabko
Keyword(s):  
Time Series ◽  
Real World ◽  
Social Indicators ◽  
Computation Time ◽  
Time Series Forecasting ◽  
Real World Data ◽  
World Data ◽  
Practical Applications ◽  
Multiple Data ◽  
Universal Codes

Time series forecasting is an important research topic with many practical applications. As shown earlier, the problems of lossless data compression and prediction are very similar mathematically. In this article, we propose several forecasting methods based on real-world data compressors. We consider predicting univariate and multivariate data, describe how multiple data compressors can be combined into one forecasting method with automatic selection of the best algorithm for the input data. The developed forecasting techniques are not inferior to the known ones. We also propose a way to reduce the computation time of the combined method by using the so-called time-universal codes. To test the proposed techniques, we make predictions for real-world data such as sunspot numbers and some social indicators of Novosibirsk region, Russia. The results of our computations show that the described methods find non-trivial regularities in data, and time universal codes can reduce the computation time without losing accuracy.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data

Safety and efficacy of direct acting oral anticoagulants and vitamin K antagonists in nonvalvular atrial fibrillation – a network meta-analysis of real-world data

VASA ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 134-147 ◽  
Author(s):  
Mirko Hirschl ◽  
Michael Kundi
Keyword(s):  
Real World ◽  
Meta Analysis ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Efficacy And Safety ◽  
Nonvalvular Atrial Fibrillation ◽  
Real World Data ◽  
Hazard Ratios ◽  
Direct Acting ◽  
Event Rates

Abstract. Background: In randomized controlled trials (RCTs) direct acting oral anticoagulants (DOACs) showed a superior risk-benefit profile in comparison to vitamin K antagonists (VKAs) for patients with nonvalvular atrial fibrillation. Patients enrolled in such studies do not necessarily reflect the whole target population treated in real-world practice. Materials and methods: By a systematic literature search, 88 studies including 3,351,628 patients providing over 2.9 million patient-years of follow-up were identified. Hazard ratios and event-rates for the main efficacy and safety outcomes were extracted and the results for DOACs and VKAs combined by network meta-analysis. In addition, meta-regression was performed to identify factors responsible for heterogeneity across studies. Results: For stroke and systemic embolism as well as for major bleeding and intracranial bleeding real-world studies gave virtually the same result as RCTs with higher efficacy and lower major bleeding risk (for dabigatran and apixaban) and lower risk of intracranial bleeding (all DOACs) compared to VKAs. Results for gastrointestinal bleeding were consistently better for DOACs and hazard ratios of myocardial infarction were significantly lower in real-world for dabigatran and apixaban compared to RCTs. By a ranking analysis we found that apixaban is the safest anticoagulant drug, while rivaroxaban closely followed by dabigatran are the most efficacious. Risk of bias and heterogeneity was assessed and had little impact on the overall results. Analysis of effect modification could guide the clinical decision as no single DOAC was superior/inferior to the others under all conditions. Conclusions: DOACs were at least as efficacious as VKAs. In terms of safety endpoints, DOACs performed better under real-world conditions than in RCTs. The current real-world data showed that differences in efficacy and safety, despite generally low event rates, exist between DOACs. Knowledge about these differences in performance can contribute to a more personalized medicine.

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