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