75,384 research outputs found
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
A Benchmark Environment Motivated by Industrial Control Problems
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Autonomously training interpretable control strategies, called policies,
using pre-existing plant trajectory data is of great interest in industrial
applications. Fuzzy controllers have been used in industry for decades as
interpretable and efficient system controllers. In this study, we introduce a
fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning
(FGPRL) that can select the relevant state features, determine the size of the
required fuzzy rule set, and automatically adjust all the controller parameters
simultaneously. Each GP individual's fitness is computed using model-based
batch reinforcement learning (RL), which first trains a model using available
system samples and subsequently performs Monte Carlo rollouts to predict each
policy candidate's performance. We compare FGPRL to an extended version of a
related method called fuzzy particle swarm reinforcement learning (FPSRL),
which uses swarm intelligence to tune the fuzzy policy parameters. Experiments
using an industrial benchmark show that FGPRL is able to autonomously learn
interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018
(GECCO '18
An investigation into current production challenges facing the Libyan cement industry and the need for innovative Total Productive Maintenance (TPM) strategy
Purpose - The purpose of this paper is to investigate maintenance and production problems in the cement industry in Libya with particular emphasis on future implementation of Total Productive Maintenance (TPM). Methodology/ Approach - The paper presents the use of case study approach of production data and history, field visits, a survey methodology using a detailed questionnaire with employees and interviews with top and middle managers in four cement factories. Findings - It has been found that the four factories under investigation have low productivity and production levels when compared with the design values. There is no clear TPM strategy and it has been also found that the lack of training and personal development is the main cause of this problem. In addition, employees are found not to be motivated as a result of the lack of poor management strategy and reward structure. Implications - Based on the findings, a new framework for TPM has been developed. This TPM strategy could be implemented in other Libyan factories as a result of the potential similarities in the cultural and environmental aspects. Practical implications - The current challenges have been identified and comparative analysis is developed into a model for the implementation of TPM. Originality/Value of pape r- The paper highlights limitation is the cement factories in Libya in relation to TPM and production strategies. The importance of adopting a realistic strategy and framework by managers is discussed. This work is developed as collaboration between Academia and Libyan Cement industry for solving productivity problems and develop a strategic framework of TPM for improving the Libyan industry
Assessment Report 2014 KTC Limited, China AA0000000364
This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.FLA_2014_KTC_AR_China_AA0000000364.pdf: 31 downloads, before Oct. 1, 2020
Batch Reinforcement Learning on the Industrial Benchmark: First Experiences
The Particle Swarm Optimization Policy (PSO-P) has been recently introduced
and proven to produce remarkable results on interacting with academic
reinforcement learning benchmarks in an off-policy, batch-based setting. To
further investigate the properties and feasibility on real-world applications,
this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a
novel reinforcement learning (RL) benchmark that aims at being realistic by
including a variety of aspects found in industrial applications, like
continuous state and action spaces, a high dimensional, partially observable
state space, delayed effects, and complex stochasticity. The experimental
results of PSO-P on IB are compared to results of closed-form control policies
derived from the model-based Recurrent Control Neural Network (RCNN) and the
model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not
only of interest for academic benchmarks, but also for real-world industrial
applications, since it also yielded the best performing policy in our IB
setting. Compared to other well established RL techniques, PSO-P produced
outstanding results in performance and robustness, requiring only a relatively
low amount of effort in finding adequate parameters or making complex design
decisions
Corporate Restructuring and Bondholder Wealth
This paper provides an overview of existing research on how corporate restructuring affects the wealth of creditors.Restructuring is defined as any transaction that affects the firm's underlying capital structure.Thus, it reaches well beyond asset restructuring and includes transactions such as leveraged buyouts, security issues and exchanges, and the issuance of stock options.The analysis identifies significant gaps in the literature, emphasizes the potential differences between creditor wealth changes in market- and network-oriented governance systems, and provides valuable insights into methodological advances.Many issues obviously remain, as empirical evidence is still incomplete and focuses exclusively on the US.In network-oriented regimes, the potential for research remains constrained by the lesser development of bond markets that disclose information on creditor wealth shocks.Still, on-going debt securitization should now allow for the investigation of at least some critical issues.This is imperative, as the position of creditors in the firm differs substantially across governance systems despite the gradual convergence of these regimes across the world.bondholder wealth;corporate restructuring;mergers and acquisitions;event studies;bond returns
Pay-performance Sensitivity and Firm Size: Insights From the Mutual Fund Industry
I examine the ex ante decision to make an agent\u27s pay-performance sensitivity an inverse function of organization size. I focus on mutual funds and their decision to use compensation contracts that reduce the advisor\u27s marginal compensation as the fund grows (a declining-rate contract) over the dominant contract type, where marginal compensation is unrelated to fund size (a single-rate contract). I find evidence consistent with the view that declining-rate contracts are a mechanism to keep marginal compensation in line with the advisor\u27s declining marginal product. Specifically, I find that funds with greater exposure to diseconomies of scale are more likely to use a declining-rate contract and to specify a greater amount of compensation decline in their contracts. Consistent with optimal contracting, I find no evidence of a performance difference between funds with declining-rate contracts and funds with single-rate contracts
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
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