Anne Nortcliffe

Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem

Scheduling optimization problems provide much potential for innovative solutions by genetic algorithms. The complexities, constraints and practicalities of the scheduling process motivate the development of genetic algorithm (GA) techniques to allow innovative and flexible scheduling solutions. Multiobjective genetic algorithms (MOGAs) extend the standard evolutionary-based genetic algorithm optimization technique to allow individual treatment of several objectives simultaneously. This allows the user to attempt to optimize several conflicting objectives, and to explore the trade-offs, conflicts and constraints inherent in this process. The area of MOGA performance assessment and comparison is a relatively new field, as much research concentrates on applications rather than the theory. However, the theoretical exploration of MOGA performance can have tangible effects on the development of highly practical applications, such as the process plant scheduling system under development in this work. By assessing and comparing the strengths, variations and limitations of the developing MOGA using a quantitative method, a highly efficient MOGA can develop to suit the application. The user can also gain insight into behaviour the application itself. In this work, four MOGAs are implemented to solve a process scheduling optimization problem; using two and five objectives, and two schedule building rules.

A three year case study of using audio to blend the engineer's learning environment

Abstract A blended approach to learning, teaching and assessment has been part of curriculum design in the teaching of software engineering at Sheffield Hallam University since 2003 when the Blackboard virtual learning environment (VLE) was first used alongside lectures and laboratory-based assignments. In seeking a greater degree of integration between physical and virtual learning spaces we have been keen to explore how digital audio can be used to engage and develop students. Digital audio has shown itself to be a simple medium to work with and distribute. Its essential value has proven to be in how it supports access to the disparate voices available in education. We have developed several ideas, including audio notes to support lectures, educational podcasting and audio feedback to bridge the virtual and physical environments in order to offer a richer, more meaningful and formative learning experience. Our recent work has shown that digital audio is generally accessible to, and valued by, students and that it can lead to greater ongoing engagement in the module. This paper focuses on the formative feed forward potential of this flexible medium and illustrates how we have built upon initial successes by improving the design and integration of audio feedback. It describes how the techniques have been used to promote an holistic blended learning environment and how this has affected both the student and the tutor.

Varying time delay Smith predictor process controller

One of the perceived weaknesses of the classical Smith predictor is that the time delay is fixed. For systems with a varying time delay, underestimating or overestimating the time delay significantly degrades control quality. This paper presents theoretical and practical results of applying the Smith predictor with a varying time delay when the nature of the application allows the time delay to be practically computed in real time.

The Innovative Use of Personal Smart Devices by Students to Support their Learning

Research into the autonomous use of MP3 audio recorders by students in UK higher education demonstrated that students were innovative in their autonomous use of the devices. They used them to capture learning conversations from formal and informal situations to personalise and enhance their learning. However, today smartphones and other smart devices have replaced the necessity for students to carry multiple mobile devices including MP3 recorders. This chapter builds upon the earlier work and presents a small qualitative study into how students are autonomously using their smart devices to support their learning. The research explores the hypothesis that students are being innovative in the ways in which they are using their smart devices to support their formal and informal learning. The study involved five students who own smart devices who were invited to discuss their ownership of smartphone and tablet technologies and the ways they used them in their studies. The students first completed a short questionnaire and were then interviewed in small groups. The results agree with previous research into student use of smart devices and describe autonomous engagement facilitated by personally owned smart technologies. The study identifies continuous patterns of pervasive engagement by students and concludes that more thought should be given to disruptive innovation, digital literacy and employability.

Using student generated audio to enhance learning

Abstract This paper explores the issues surrounding the use of audio in learning and offers an alternative to podcasting. It considers the practicalities of enabling students to generate their own audio recordings and the potential to enhance and personalise learning in a self directed way that suits their individual learning styles. There is some discussion of hardware and its accessibility, cost and ease of use as well as protocols on what audio can/cannot be recorded or shared amongst students. The paper explores different types of scenario where recording can be used beneficially and uses real student case studies to demonstrate its efficacy, as perceived by the students. There is also particular emphasis on the benefits to specific student groups, including those with English as an additional language or students with learning difficulties. In summary the paper gives evidence of how student generated audio can be embedded into the curriculum and the benefits it can bring.

Smartphone feedback : using an iPhone to improve the distribution of audio feedback

Smartphones combine the flexibility of MP3 recorders and the connectivity of a PC in a discrete handheld device, thereby suggesting their application as a user friendly tool for giving recorded audio feedback efficiently. This paper describes a smartphone feedback methodology and presents findings from research on its use with 130 Level 5 (2nd year of degree study) Engineering and Computing students. The study found that the smartphone was more suitable than other technologies to the various demands of feedback production and distribution, and that this helped the tutor to manage the exceptionally stressful time associated with marking and giving feedback on top of an ongoing teaching load. Its pedagogic integration also resulted in a good dialogical experience as evidenced through student testimony.

A framework for modelling in S88 constructs for scheduling purposes

ISA S88.01 [1] [ISA (ANSI/S88.01.1995), Standard batch control; part 1: models and terminology, Instrument Society of America, 1995] is a standard that provides a methodology for the dissemination of a batch into standard models and provides the terminology for defining the control requirements of batch plants. The nature of S88 is such that it is not only applicable to the development of computer control systems for a batch plant, but also could be utilised within batch management software, i.e. scheduling. This paper presents a method of interpreting multi-purpose/product batch plant into S88 constructs to provide an object oriented framework for the research and development of a batch scheduling Matlab tool-box. Such a framework provides a thorough and efficient method of articulating the model to effectively apply automatic scheduling/optimisation methods.

Interactive batch process schedule optimization and decision-making using multiobjective genetic algorithms

A multiobjective genetic algorithm (MOGA) is applied to a test batch scheduling problem to optimize five objectives simultaneously. The design of the MOGA allows an emphasis on human interaction with the optimization process, including the ability to change priorities of preferences and plant data interactively, and to allow the MOGA to make decisions regarding batch size and the rule task allocation. Experimental results demonstrate the development of this technique, allowing the combination of human expertise and MOGA optimization power to provide scheduling solutions to a highly complex problem.

Are academics ready for smart learning

Abstract: Ownership of smartphones and tablets among the student population is growing. Students are using their devices to support their learning. Employers and employees are increasingly bringing their own smart devices into private and public organisations to support their business. This is leading to employees driving the Bring Your Own Device (BYOD) agenda in organisations. It is not clear the extent to which academics are embracing smart technology to manage their workload or to enhance the student experience of learning. This paper presents a qualitative study of how engineering academics are using their own BYOD or institutionally provided smart devices. A 6Cs (connect, communicate, collaborate, curate, create and coordinate) framework has been used to analyse the results. The findings indicate that academics are primarily using devices to create materials, second to coordinate their work and third to communicate with students about their learning. However, there are a number of inhibiting and enabling factors that need to be addressed by academic institutions to develop the effective adoption of smart technologies for academic practice. Infrastructure, including developing widespread access to WiFi, and the prioritisation of opportunities to support staff to learn how to apply the technology to enhance student learning and experience are key areas of necessary development. [ABSTRACT FROM AUTHOR]

Exploring use of multiobjective genetic algorithms for constrained process scheduling

Abstract Chemical processing offers constrained, complex scheduling problems (Nott and Lee, 1997). Genetic algorithms (GAs) are evolutionary-based optimisation tools (Holland, 1975), suited to schedule optimisation search, (Davis, 1985). Multi-objective GAs (MOGA) allow multiple objective optimisation (Fonseca and Fleming, 1998), as well as a technique for constraint handling, and the exploration of the inherent trade-offs between goals and constraints. A batch process problem is introduced, and a MOGA is applied to a plant model. The MOGA acts as a flexible optimisation tool, incorporating the framework of the ISA standard S88 (Love and Bunch, 1998) and allowing the user to specify plant variables. This paper explores the MOGA as an optimisation and constraint satisfaction method.