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The current trends in education for distance and personalized learning, calls for further investigation on the educational benefits of a Virtual Reality (VR) software, regarding laboratory science courses. In this study, we investigated the teaching effectiveness of a VR-oriented innovative method vs a more traditional pedagogical approach, by measuring the Pre-Post change, commonly called as Learning Gain (LG). An Item Response Theory model, The Rasch Model (RM), was used to assess the LG as the difference between the students’ ability before and after the educational treatment. The participants, (N= 54) graduate students enrolled in the Department of Primary Education, at the University of Patras, in Greece, were divided into two groups and followed two different scenarios to be educated on the topic of microscopy. Our findings provided evidence in favor of using simulations as a supplementary tool to the learning procedure. According to the LG analysis, the students that interacted with the VR software showed a higher change in ability, compared to their fellow students, who followed a more classic teaching methodology and obtained no LG between the Pre and Posttesting situation.

This work is aiming to contribute to the field of Distance Learning through Learning Analytics. We propose a methodological framework based on network analysis metrics to provide multiple indicators for Course Learning Analytics. Social Network Analysis is proposed for this purpose due to its capacity to provide an integrated representation of students’ interaction, where individual behavior is expressed within the context of a learning community. We perform experimental evaluation on real-life data from anonymized forum posts of postgraduate students and their tutors in the School of Science and Technology at the Hellenic Open University. Initially, we create and examine two-mode networks (participant-discussion) for two different modules. Subsequently, these networks are transformed into one-mode networks. Key measures are estimated and compared and the differences between their pedagogical interpretations are highlighted. We conclude that the choice between working with a bimodal network or projecting it into a unimodal one is determined by the nature of the research questions because of the distinct features that each one of them exhibits.

This paper aims to provide an overview of the current state of the art in molecule and material design and a perspective on open questions. First, we discuss the interplay between process design and the design of molecules and materials and then present the basic trade-offs and interdependencies that need to be considered in this integrated design problem. Second, we introduce methods and open questions in the area of processing materials, with special emphasis placed on heterogeneous catalysts due to their importance in the chemical industry. Finally, we discuss materials for photovoltaic cells as one example of a chemical product that is likely to help supply sustainable energy and address climate change concerns. What is presented here is based on presentations and discussions during the FIPSE-4 meeting in June 2018 (https://www.fi-in-pse.org/fipse-4-2018).

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach.

Permanent-magnet motors constitute a favored choice for electric drive applications in high-speed ranges due to their increased efficiency, compact rotor structure, and relative robustness. The respective design involves analysis of coupled electromagnetic, thermal, and mechanical phenomena imposing respective limitations. Direct coupling of such a multiphysics problem results in computational onerous optimization schemes; in order to tackle this difficulty, an innovative weak coupling design procedure is introduced considering the interdependence of the phenomena involved during both preliminary and final design stages foreseen. The proposed methodology has been applied in a surface-mounted permanent-magnet motor case.

The ability of online optimization to improve the dynamic performance of complex systems, particularly in production scheduling and process control applications, has led to a significant interest in, and adoption of, online optimization algorithms and techniques for a range of industries. However, these algorithms' ability to account for subtle features in complex systems may cause nonintuitive and rapid alterations to the optimal trajectory for the human operators often observing, evaluating, and, particularly in scheduling applications, executing the tasks prescribed by the optimization algorithm. In practice, these alterations are often restricted in some manner by the scheduling algorithm to alleviate operators' concerns and improve overall facility operation. Although numerous rescheduling penalties and constraints for production scheduling algorithms have been proposed, theoretical properties concerning the dynamics of these modified systems have been largely ignored. In this paper, we provide a gen...

We present a general mixed-integer programming model for periodic production scheduling. The formulation, which is based on the state-task network (STN) representation, allows us to model (1) accur...

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value i...

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods.

In this paper, we address the problem of incorporating knowledge of the automation system in a chemical plant into the online scheduling problem. Optimization models for online scheduling necessarily omit some of the plant dynamics to ensure sufficiently fast solution times for use in online rescheduling. This can result in the computed schedules not being feasible when executed in the plant. To overcome this difficulty, we propose to use online analysis of a formal model of the automation logic to detect these infeasible schedules and avoid them when rescheduling. Model checking is applied to an abstraction of the automation system’s dynamics to detect infeasible schedules, and a state-space resource task network scheduling model is used to account for the associated delay information. We demonstrate the techniques using an illustrative running example, and show the utility of the integrated approach using a case study involving multiple batch reactions.

The Discrete-Continuous Algorithm (DCA) is a novel framework that harnesses the strengths of discrete- and continuous-time scheduling formulations (Lee and Maraveloas,2018). Its flexibility in the selection of two user-defined parameters, namely discretization step length (δ) and horizon relaxation (η), can lead to significantly improved computational performance and solution quality. In this paper, we propose systematic methods to determine these parameters. Specifically, we evaluate the parameters based on error evaluation functions and cumulative error functions that consider various aspects of the scheduling instances. Through an extensive computational study, we show that the proposed methods bring up to × 104 speedups, while leading to identical or better solutions in the majority of the instances compared to traditional methods.

Virtual reality-based instruction is becoming an important resource to improve learning outcomes and communicate hands-on skills in science laboratory courses. Our study attempts first to investigate whether a Markov chain model can predict the students’ performance in conducting an experiment and whether simulations improve learner achievement in handling lab equipment and conducting science experiments in physical labs. In the present study, three cohorts of graduate students are trained on a microscopy experiment using different teaching methodologies. The effectiveness of the teaching strategies is evaluated by observing the sequences of students’ actions, while engaging in the microscopy experiment in real-lab situations. The students’ ability in performing the science experiment is estimated by sequential analysis using a Markov chain model. According to the Markov chain analysis, the students who are trained via a virtual reality software exhibit a higher probability to perform the steps of the experiment without difficulty and without assistance than their fellow students who attend more traditional training scenarios. Our study indicates that a Markov chain model is a powerful tool that can lead to a dynamic evaluation of the students’ performance in science experiments by tracing the students’ knowledge states and by predicting their innate abilities.

Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.

International audience; This paper investigates a general framework for synchronous educational language games that simultaneously allows researchers to crowdsource learner answers in a controlled environment. Our prototype Substituto allows teachers and students to interact in real-time while undergoing language learning exercises; ensuring that the learner’s progress is not hurt by the introduction of crowdsourcing elements. We evaluate Substituto with a small-scale user study that focuses on training the use of English verb-particle constructions (VPCs), such as break down or take over, and test their use with second language learners of English of different proficiency levels over five pilot sessions. With the study we aim to ensure that our prototypical implementation behaves as expected and to identify any major design flaws that should be addressed. The preliminary results we achieved in order to evaluate the educational value, the user experience and the crowdsourcing capacity of XYZ-Bot confirm that it has the potential to become a valuable asset for language learning, a pleasant learning instrument and a crowdsourcing tool for collecting linguistic knowledge.

Due to time-varying utility prices, peak demand charges, and variable-efficiency equipment, optimal operation of heating ventilation, and air conditioning systems in campuses or large buildings is nontrivial. Given forecasts of ambient conditions and utility prices, system energy requirements can be reduced by optimizing heating/cooling load within buildings and then choosing the best combination of large chillers, boilers, etc., to meet that load while accounting for switching constraints and equipment performance. With the presence of energy storage, utility costs can be further reduced by temporally shifting production, which adds an additional layer of complexity. Furthermore, due to changes in market and weather conditions, it is necessary to revise a given schedule regularly as updated information is received, which means the problem must be tractable in real time (e.g., solvable within 15 min). In this paper, we present a mixed-integer linear programming model for this problem along with reformulations, decomposition approaches, and approximation strategies to improve tractability. Simulations are presented to illustrate the effectiveness of these methods. By removing symmetry from identical equipment, decomposing the problem into subproblems, and approximating longer-timescale behavior, large instances can be solved in real time to within 1% of the true optimal solution.

The integration of structural and network-based systems biology is paramount for the improved understanding of how the proteins interact to modulate the behavior of complex biological systems. This review presents the current literature on the computational studies that combine these two scientific fields focusing on two main approaches: network-based analysis of the structure and dynamics of proteins and the in silico reconstruction of protein-protein interaction (PPI) networks or expansion of existed protein interactomes driven by structural annotations. Last, to enrich the current knowledge of the topological properties of the protein structure networks and evaluate the capacity of the public structural annotations of protein domains to predict novel PPIs, further computational analyses, missing so far from the literature, were performed.

A novel algorithm that exploits the strengths of discrete- and continuous-time scheduling mathematical programming formulations is proposed. It consists of three stages, in which (i) an approximate solution is obtained using a discrete-time mixed-integer programming scheduling model, (ii) the solution is mapped onto continuous-time grids via a mapping algorithm, and (iii) the accuracy of the solution is improved by solving a continuous-time linear programming model. Two types of continuous-time grids are introduced, namely unit- and material-specific grids, to ensure feasible unit utilization and material balance in the model. An extensive computational study is performed, showing that the algorithm is capable of finding high quality solutions orders of magnitudes faster than traditional methods.

We present a framework to study quality of schedules obtained iteratively and online (real-time) in the presence of demand uncertainty Using this framework, we carry out a computational study, and make interesting observations. First, we find that uncertainty plays a less important role as a manufacturing facility is operated close to capacity. Second, the choice of the horizon for the online iterations, is dependent on the mean load, but independent of the accuracy of the forecasts. Finally, feedback, in the form of re-optimization, plays a very important role in mitigating the impact of uncertainty. Thus, through the analysis presented in this work, we gain insights that are applicable to all general rescheduling approaches.

We propose mixed-integer linear programming models for utility targeting and heat exchanger network synthesis that account for variable stream temperatures and flow rates as well as unclassified streams and thus can be integrated with process synthesis optimization models. By projecting temperature information onto a fixed discrete temperature grid, the model remains linear even under variable temperatures and flow rates, leading to a more tractable optimization model. The proposed model is well suited to address problems in the area of process intensification where the close interaction between different phenomena naturally leads to process synthesis problems with unclassified process streams. Several extensions are presented, including nonisothermal mixing as well as phase changes. Furthermore, we show how a nonuniform grid can be adopted to reduce the complexity of the model for medium and large-size problems.

In this work, we present mixed integer linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams in systems where the composition of the reactor inlet streams can vary substantially, thereby making the determination of the limiting component as well as the calculation of the stream heating/cooling and power requirements challenging. First, towards the modeling of reactors, we develop an extent-based method which detects the limiting reactant of each reaction occurring in parallel with others, based on the inlet flows of the reactants. Second, we develop a computationally tractable method for the calculation of the work and heating/cooling duty needed to condition any stream of a process based on simple calculations that can be performed offline. Finally, we present how the two aforementioned components can be integrated in an optimization model generated based on a process superstructure. We demonstrate the application of the developed methods for the synthesis of a biorefinery.

We propose a general method for the solution of chemical production scheduling problems in network environments. The method consists of three stages. In the first stage, a discrete-time mixed-integer programming (MIP) model is solved to quickly obtain an approximate solution. In the second stage, the solution is mapped onto newly introduced unit- and material-specific continuous-time grids, using a mapping algorithm. In the third stage, a continuous-time linear programming (LP) model is solved to improve the accuracy of the mapped discrete-time solution by refining the timing of events and batch sizes. The proposed method takes advantage of the complementary strengths of discrete- and continuous-time formulations, which enables us to not only handle various processing features (e.g., intermediate deliveries and orders, time-varying resource availability and cost, variable processing times), but also obtain order of magnitude speedups in the solution of large-scale instances.

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals-"bio-based chemicals." However, it is still unclear which chemicals have the highest economic prospect. To this end, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome-scale constraint-based metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity, and residence time for each) from the intersection of the high-production-volume chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical's profit margin, market volume, and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as Escherichia coli and Saccharomyces cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio-based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.

This paper proposes a multicriteria design optimization methodology for permanent magnet (PM) motors used in electric vehicle (EV) applications. In the process, an adaptive-network-based fuzzy inference system (ANFIS) is utilized, coupled with a multiobjective optimization algorithm, as a surrogate model of the electric motor. This allows for the consideration of the full drive cycle and respective efficiency map for every motor design. The prediction error of the ANFIS is minimized by employing appropriate membership functions, initial training data, and an adaptive learning scheme via iterative training. The efficiency map is then implemented in a vehicle dynamic model to compute the total consumed energy over the driving cycle. The optimization profile accounts for total energy efficiency, torque density, and additionally considers complementary design criteria via an a posteriori selection procedure on the resulting Pareto set. The methodology developed is applied to optimize a surface PM motor with concentrated fractional slot winding, mounted on a light EV that competes in fuel economy races. The selected motor design has been validated through measurements on a prototype.

With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We show a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed $485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications.

We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous chemical process and heat exchanger network synthesis. The model allows process stream inlet/outlet temperatures and flow rates to vary and can be extended to handle unclassified streams, thereby facilitating integration with a process synthesis model. The proposed model is based on a generalized transshipment approach in which the heat cascade is built upon a “dynamic” temperature grid. Both hot and cold streams can cascade heat so that exchanger inlet and outlet temperature, heat duty, and area can be calculated at each temperature interval. We develop mixed-integer constraints to model the number of heat exchangers in the network. Finally, we present several solution strategies tailored to improve the computation performance of the proposed models.

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.

We present a framework that allows us to utilize high-resolution spatial data to design biomass-to-fuel supply chains. Specifically, we first present how to extract crop data from the Agricultural Model Intercomparison and Improvement Project packages, and then develop a method to combine these data with a historical cropland data layer, to generate spatial data with user-specified resolution. Next, we develop a general approach to determine the potential depot and biorefinery locations, and calculate the actual flow path distance between facilities using Geographic Information Systems methods. Since spatially explicit data lead to large-scale supply chain networks, we develop preprocessing algorithms that allow us to remove arcs that will never be used in an optimal solution, thereby reducing the size of the network under consideration. We then present a multi-period mixed-integer linear programming model that accounts for the selection of depot and biorefinery locations and their capacities, shipping and inventory planning, as well as the selection of pretreatment and conversion technologies, and transportation modes. Finally, we demonstrate the application of our framework using a case study of corn stover-to-ethanol supply chain in Wisconsin.

Receiver operating characteristic (ROC) analysis is the methodological framework of choice for the assessment of diagnostic markers and classification procedures in general, in both two‐class and multiple‐class classification problems. We focus on the three‐class problem for which inference usually involves formal hypothesis testing using a proxy metric such as the volume under the ROC surface (VUS). In this article, we develop an existing approach from the two‐class ROC framework. We define a hypothesis‐testing procedure that directly compares two ROC surfaces under the assumption of the trinormal model. In the case of the assessment of a single marker, the corresponding ROC surface is compared with the chance plane, that is, to an uninformative marker. A simulation study investigating the proposed tests with existing ones on the basis of the VUS metric follows. Finally, the proposed methodology is applied to a dataset of a panel of pancreatic cancer diagnostic markers. The described testing procedures along with related graphical tools are supported in the corresponding R‐package trinROC, which we have developed for this purpose.

We propose a generalized superstructure-based distillation network synthesis model with improved modeling capabilities resulting in an extended solution space. The model can assign multiple mixtures to be separated (e.g., reactor network effluents) to different columns of the network while considering the interactions among different separation steps. In terms of outlets, products with general specifications, including pure components and multi-component mixtures, as well as streams without strict specifications (e.g., reactor recycle streams) can be readily handled. Furthermore, stream bypass is considered to avoid unnecessary separations, and thermal coupling is considered to yield novel solutions. The proposed model enables seamless integration with reactor network synthesis models thereby leading to solutions that are superior to the ones obtained by conventional sequential approaches.

We present new discrete-time mixed-integer linear programming formulations for short-term scheduling in multi-purpose batch plants, the most general sequential production environment. We first discuss how multi-purpose batch plants can be expressed using State-Task Network and Resource-Task Network representations through batch-based definition of states (resources) and tasks. We then develop two models based on each representation that account for limited intermediate storage, and discuss extensions such as limited shared resources and time-varying resource availability/cost. Finally, we present several case studies to illustrate the applicability and performance of the proposed models.

We present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L2 regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization. A multistart approach is used to explore the solution space, and a hierarchical clustering procedure is implemented for statistically classifying potentially competing solutions. An example of methanol synthesis through hydrogenation of CO and CO2 on a Cu-based catalyst is used t...

We develop an optimization-based web application for assessing biomass-to-fuels strategies based on the Biomass Utilization Superstructure (BUS) framework. This web application allows researchers with limited knowledge of optimization to assess different technologies employed at different strategies and identify the major cost drivers. The user must only provide the necessary parameters and create an optimization run after identifying the question to be addressed and the assessment metric. The web application generates visual representations of the results once the optimal solution is obtained. Finally, we demonstrate the applicability of the web application through an example. © 2017 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.

We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous utility and heat exchanger area targeting with variable stream conditions. The model represents the composite-curve-based area targeting method by constructing the hot and cold composite curves mathematically. We introduce a “dynamic” enthalpy grid onto which the stream inlet/outlet temperatures and enthalpies are mapped. By calculating the temperatures at each grid point and the stream heat duties at each interval, the utility consumption and heat exchanger areas are simultaneously optimized using an economic criterion. We discuss preprocessing methods tailored to aid the solution of the proposed MINLP model. The model is applied to two illustrative examples as well as an example where it is integrated with a process synthesis model.

Changeover times can have a significant impact on the scheduling of manufacturing operations. Unfortunately, accounting for changeovers in mixed-integer programming (MIP) scheduling formulations makes the resulting models computationally more expensive. We propose five new formulations for sequence-dependent changeovers, applicable to a wide range of scheduling problems. We generate constraints for different sets of time points and sets of tasks. We also propose valid inequalities for makespan minimization. Furthermore, we prove results regarding the relative tightness of each formulation. Finally, we perform a computational study. Interestingly, we find that tighter formulations do not always lead to faster solution times, and we show that some of the new formulations perform better than the previously proposed ones.

A novel method for solving vehicle-based inventory routing problems (IRPs) under realistic constraints is presented. First, we propose a preprocessing algorithm that reduces the problem size by eliminating customers and network arcs that are irrelevant for the current horizon. Second, we develop a decomposition method that divides the problem into two subproblems. The upper level subproblem considers a simplified vehicle routing problem to minimize the distribution cost while satisfying minimum demands, which are calculated based on consumption rate, initial inventory and safety stock. In the lower level, a detailed schedule with drivers is acquired using a continuous-time MILP model, by adopting the routes selected from the upper level. Finally, an iterative approach based on the upper and lower levels is presented, including the addition of different types of integer cuts and parameter updates. Different options of implementing this iterative approach are discussed, and computational results are presented.

This paper proposes a hybrid, multiobjective optimization algorithm enabling global optimum tracking in permanent-magnet (PM) traction motor design. The methodology developed is based on the Artificial Bee Colony technique, strength Pareto evolutionary algorithm, and differential evolution strategy ensuring fast and reliable convergence to the optimal Pareto front. The effectiveness of the derived methodology is compared with other well-established and powerful algorithms from the literature through both appropriate test functions and an application example concerning an unequal teeth surface-mounted PM wheel motor design.

This paper proposes a robust optimization algorithm, enabling global optimum tracking and manufacturing uncertainty factors handling, for high-speed permanent magnet motor design. A new robustness criterion is introduced, considering effectively the impact of the construction tooling uncertainties on the design variables in all objectives. An adaptive-network-based fuzzy inference system is adopted acting as a surrogate for the time-consuming finite-element (FE) analyses and results in computationally fast robust electric machine design procedure. The procedure has been validated through FE high-speed motor design.

We formally introduce the concept of “state” of a batch in production scheduling, we examine how this state impacts the scheduling process, and propose an approach to estimate it based on real-time plant information. We present an online scheduling algorithm based on a state-space resource task network (RTN) formulation and show why and how the resource-task interaction coefficients (which have always been thought to be constants) should be updated based on the state of the executed batches, using real-time information, to obtain better implemented solutions.