Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

Combining Lean Concepts &Tools with the DMAIC Framework to Improve Processes and Reduce Waste

Six Sigma DMAIC methodology has been applied to systematically apply lean manufacturing concepts and tools in order to improve productivity in a local company specialized in the manufacturing of safety and fire resistance metal doors, windows, and frames. In-depth analysis of the plant processes unfolded different critical processes, specifically foam injection process and sheet metal cutting process. Throughout the different project phases, various improvements had been implemented to reduce production cycle time from 216 min to 161 min;non-value added activities in the different processes were identified and eliminated. Plant layout and machine reconfiguration reduced backtracking and unutilized space. Percentage of defective doors (needing rework) dropped from 100% to only 15%. The successful implementation of this project is largely due to top management active involvement and participation of workers and operators in all stages of the project. Finally, new policies and mentoring programs are introduced to maintain improvements.

A Kind of Lean Approach for Removing Wastes From Non-Manufacturing Process With Various Facilities

It is important to identify and remove the wastes not only from manufacturing process, but also from nonmanufacturing process. In the last several decades, significant research achievements and practice benefits have been achieved about removing wastes from manufacturing process. Since the1990 s, some researchers and lean practitioners have paid more attention to removing waste from non-manufacturing process.Based on the authors' research work and industrial practice, the paper introduces a kind of lean approach for removing waste from non-manufacturing process. In its case study, the order handling process in a value chain is described with respect to a factory and its downstream distribution centers(DCs). The paper proposes a lean approach solution for creating the improved order handling process, and analyze how great improvements in performance can be achieved. As a result, the significant achievement has created a win-win scenario for both the nonmanufacturing process in a factory and non-manufacturing facilities(like DCs) across the value chain. It demonstrates that improvements have been made by removing waste from the non-manufacturing process that takes place within a factory as well as with external participants through the whole value chain. Likewise, the proposed lean approach has helped the case companies to achieve greater levels of efficiency and more benefits. Finally, some conclusions are drawn.

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have “selective” excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects;these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems

Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

Leveraging Six Sigma Tools and Methodology to Improve Student English Language Performance at Elementary School

Purpose: The purpose of this paper is to discuss how the Six Sigma (SS) methodology can be used to improve the performance of students in English as a second language and enhance the teaching process by utilizing quality tools in an educational environment. Design/Methodology/Approach: The paper uses the different quality improvement tools within the Define, Measure, Analyze, Improve and Control (DMAIC) phases to improve student performance in an English language class at a private school. Findings: Quality tools, such as cause and effect diagrams and quality function deployment, have been successfully applied within the Six Sigma DMAIC framework in the educational sector to improve the performance of students and enhance the teaching process. Practical Implications: This paper helps school administrations to analyze student performance and implement improvement actions to improve their performance. Originality/Value: The principal objective of this paper is to demonstrate how quality improvement tools can be introduced and successfully applied in an elementary school to improve student performance and communication between the school administration, teachers and parents.

Optimal Control of Microgrids with Multi-stage Mixed-integer Nonlinear Programming Guided Q-learning Algorithm

This paper proposes an energy management system(EMS)for the real-time operation of a pilot stochastic and dynamic microgrid on a university campus in Malta consisting of a diesel generator,photovoltaic panels,and batteries.The objective is to minimize the total daily operation costs,which include the degradation cost of batteries,the cost of energy bought from the main grid,the fuel cost of the diesel generator,and the emission cost.The optimization problem is modeled as a finite Markov decision process(MDP)by combining network and technical constraints,and Q-learning algorithm is adopted to solve the sequential decision subproblems.The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming(MINLP)problem into a series of single-stage problems so that each subproblem can be solved by using Bellman’s equation.To prove the effectiveness of the proposed algorithm,three case studies are taken into consideration:(1)minimizing the daily energy cost;(2)minimizing the emission cost;(3)minimizing the daily energy cost and emission cost simultaneously.Moreover,each case is operated under different battery operation conditions to investigate the battery lifetime.Finally,performance comparisons are carried out with a conventional Qlearning algorithm.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system,the operation uncertainties in either subsystem,such as wind fluctuations or component failures,could have a magnified impact on the reliability of the whole system due to energy interactions.A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system(IEGS).Therefore,this paper proposes a day-ahead security-constrained unit commitment(SCUC)model for the IEGS to schedule the operation and reserve simultaneously considering reliability requirements.Firstly,the multi-state models for generating units and gas wells are established.Based on the multi-state models,the expected unserved energy cost(EUEC)and the expected wind curtailment cost(EWC)criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS.Furthermore,the EUEC and EWC criteria are incorporated into the day-ahead SCUC model,which is nonconvex and mathematically reformulated into a solvable mixed-integer second-order cone programming(MISOCP)problem.The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system.Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.