Department of Industrial Engineering

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A data and modelling framework for strategic supply chain decision-making in the petro-chemical industry.
(Stellenbosch : Stellenbosch University, 2006-12) Van Schalkwyk, Willem Tobias; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: The research was initiated by an opportunity within the petro-chemical company Sasol to explore, improve and integrate various analytical techniques used in the modelling, design and optimisation of supply chains. Although there is already a strong focus on the use of analytical applications in this environment, the lack of both modelling integration and analytical data availability has led to less than optimal results. This document presents an exploration into the supply chain planning landscape, and in particular strategic planning in the petro-chemical environment. Various modelling methodologies and techniques that support strategic supply chain decision-making are identified, followed by an in-depth analysis of the data requirements for effectively constructing each of these models. Perhaps the biggest hurdle in the continual use of modelling techniques that support strategic supply chain decision-making, remains the extent of the data gathering phase in any such project. Supply chain models are usually developed on an ad hoc project basis, each time requiring extensive data gathering and analysis from transactional data systems. The reason for this is twofold: 1) transactional data are not configured to meet the analytical data requirements of supply chain models, and 2) projects are often done in isolation, resulting in supply chain data that end up in spreadsheets and point solutions. This research proposes an integrated data and modelling framework, that aspires to the sustainable use of supply chain data, and continual use of modelling techniques to support strategic supply chain decision-making. The intent of the framework is twofold: 1) to enable the design of new supply chains, and 2) to ensure a structured approach for capturing historical supply chain activities for continued review and optimisation. At the heart of the framework is the supply chain analytical data repository (SCADR), a database that maintains supply chain structural and managerial information in a controlled data model. The motivation behind developing a database structure for storing supply chain data is that a standard encoding method encourages data sharing among different modelling applications and analysts. In the globalised environment of the 21•t century, companies can no longer ensure its market position solely by its own functional excellence ... in the new economy, whole business ecosystems compete against each other for global survival (Moore, 1996). This motivates the ever-increasing importance of supply chain management, which necessitates the use of advanced analytical tools to assist business leaders in making ever more complex supply chain decisions. It is believed that the integration of information requirements for multiple optimisation/ modelling initiatives in a structured framework (as presented in this research) will enable sustainability and improved strategic decision-making for the petro-chemical supply chain.
The absence of a creative focus in the conventional engineering design process : identifying research opportunities to address this
(SAIIE, 2016-05) Oosthuizen, Louzanne; Vlok, P. J.
ENGLISH ABSTRACT: This paper synthesizes an overview of various models of the engineering design process with an overview of the most relevant theories within the field of creativity studies to conclude that (i) creativity plays a role throughout the engineering design process, and it is possible to incorporate creativity into the engineering design process in a systematic manner; (ii) doing so, at the very least, holds significant potential for economic benefit; and (iii) due to the complex interplay between creativity and the wide range of factors that influence it, organisational climates and management practices cannot simply be assumed to support creativity effectively. It is proposed that organisations be managed proactively to support creativity in engineering design. For this study, a structured literature search protocol was implemented to determine whether there is any evidence in the literature that engineering organisations are being managed proactively with this in mind; none was found. Two opportunities for future research are suggested based on these findings: (i) the development of a framework to guide the proactive management of engineering organisations to support creativity; and (ii) the development of mechanisms for measuring creativity in engineering organisations and engineering design.
An activity-based workload modelling approach for determining diagnostic radiographer staffing requirements within a diagnostic radiology practice.
(Stellenbosch : Stellenbosch University, 2021-03) Cloete, Cosmo; Bam, Louzanne; De Kock, Imke; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Radiology is a medical speciality that uses imaging technologies to obtain and interpret medical images. Radiology forms a crucial part in the diagnosis and treatment of diseases, thus the quality of the reports and images, together with the quick turnaround time of the results, is paramount. The radiology team consists of radiologists, who are medical specialists that interpret images to appropriately advise on treatment, while radiographers operate the imaging equipment and ensure correct patient positioning The efficiency of a radiology department is influenced by effective integration of workflow and imaging technologies as well as by appropriately aligned staffing,amongst other factors. Furthermore, diagnostic radiographers are responsible for producing the medical images that form the basis for accurate diagnosis hence, ensuring that a diagnostic radiology practice is staffed by a sufficient number of diagnostic radiographers is a vital aspect in enabling effective service delivery. However, a review of existing radiology-specific staffing approaches reveals that radiology workload models focus mainly on diagnostic radiologists within tertiary hospital environments, while research into radiographers’ workload and staffing only considers the radiation therapy practice field. This research develops a framework that can be used to accurately determine diagnostic radiographer staffing requirements. The proposed framework is developed based on requirement specifications. These requirement specifications are formulated based on a body of literature that includes both general healthcare-and radiology-specific staffing approaches. A complete evaluation approach (verification and validation) is applied to the requirement specifications and the proposed framework. A self-verification of the requirement specifications to the proposed framework is done followed by a theoretical verification of both the underlying bodies of literature and the framework that involves subject matter experts. The validation process includes a case study application of the framework to a private diagnostic radiology practice. The results of the case study are validated with subject matter experts to confirm the framework’s applicability and practicability. Insights from the case application confirm that the diagnostic radiographer staffing framework can be applied to both public and private diagnostic radiology environments.
Adapting modern portfolio theory for prioritising asset care planning in industry
(Southern African Institute for Industrial Engineering, 2014-05) Van den Honert, Andrew Francis; Vlok, Pieter-Jan
Productivity improvement within any organisation can lead to increased turnover. This study focuses on developing a maintenance productivity improvement model that is based upon an established financial investment portfolio technique known as the Modern Portfolio Theory (MPT). The model can be used as a tool to minimise and diversify the long term risk associated with variances or fluctuations in the increase in productivity in multiple maintenance service centres. This is achieved by optimising the most efficient way of splitting resources, such as time and money, between these multiple service centres, resulting in increased productivity and a more constant maintenance work load. This model is verified through the use of an efficient frontier, resulting in a graphical method to determine the link between the expected increase in productivity and the standard deviation of the increase in productivity. Ultimately this model can be adapted for use in many sectors within an organisation, over and above the application in maintenance prioritisation. This study concludes that the model offers a simple tool to aid decision-making among various combinations of assets within a maintenance context; and this model, adapted from MPT, was successfully validated with the use of an efficient frontier.
Adaptive games for learner and systems (bidirectional) learning
(Stellenbosch : Stellenbosch University, 2022-04) von Leipzig, Tanja; Schutte, Corne; Lutters, Eric; Hummel, Vera; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT:Traditional learning environments are ineffective and inefficient and are failing to adequately equip students and employees with the knowledge and skills required in today’s jobs, let alone prepare them for the jobs of tomorrow. Given the rapidly changing landscapes of technologies and business models, organisations need to be flexible and adaptable to respond to, and even pre-empt future demands. One of the primary shortcomings of existing learning environments is their inflexibility and the ‘one size fits all’ approach followed. Serious games and game-based learning are widely recognised for their potential in providing more effective learning environments, especially when designed in a personalised, adaptive manner, and are explored in this dissertation. In addition to adapting to the individual traits and preferences of users, games are also highly context dependent. Whilst there is a great deal of literature and documented case studies of game-based learning, most focus only on the implementation of one particular game in a specific context. Whilst many existing game design models and approaches focus on achieving improved learning outcomes of learners, there is an opportunity to consider the impact of gameplay on other stakeholders and drive the active development of meta-skills in various stakeholders. Bidirectional learning, where learning simultaneously takes place in a two-way direction [295], has great potential and has, to date, not been incorporated in serious game design. By integrating different perspectives and variable scenarios, the dynamic personalisation of learning trajectories may be possible. Serious games offer a potential platform to aggregate learner behaviours and results, and use these to dynamically configure, adjust and tailor the game to individuals and contexts, ultimately providing a learning environment of improved quality, effectiveness and efficiency. In this dissertation, adaptive, bidirectional games are explored as a means to provide more effective and efficient learning environments for multiple stakeholders. Moreover, an architecture is presented to support the creation of such games for specific scenarios in a faster, more effective and more efficient manner. Following a research-by-design approach, the architecture is iteratively developed and simultaneously applied in four case studies. Experiences and learnings from each case study are infused into subsequent design iterations of the architecture. The architecture allows users to explore and exploit the solution space more deliberately and better understand the various functions and the interrelations between them. The flexible and modular structure of the architecture allows users to prioritise functionalities as required in the given scenario. Furthermore, the directional relations between functions can be interpreted and prioritised as needed given the specific context and requirements. The architecture incorporates various stakeholders in the design process, leading to greater transparency and better understanding throughout the process. More importantly, it emphasises bidirectional learning whereby different stakeholders can learn from gameplay and the aggregated results and behaviours of players.
Adding multi-objective optimisation capability to an electricity utility energy flow simulator
(Stellenbosch : Stellenbosch University, 2016-03) Brits, Ryno Ockert; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: The energy flow simulator (EFS) is a strategic decision support tool that was developed for the South African national electricity utility Eskom. The advanced set of algorithms incorporated into the EFS enables various departments within Eskom to simulate and analyse the Eskom value chain from primary energy to end-use over a certain study horizon. The research in this thesis is aimed at determining whether multi-objective optimisation (MOO) capability can be added to the EFS. The study forms part of a series of research projects. This project builds on the work of Hatton (2015) in which the focus was on single-objective optimisation capability for the EFS. Inventory management at Eskom's coal- red power stations was identified as the most suitable area for the formulation of an MOO model. It was also identified that certain modifications to the existing EFS architecture can possibly improve its potential as an optimisation tool. The architecture of the EFS is studied and modifications to it are proposed. A multi-objective inventory model is then formulated for Eskom's network of coal- red power stations using the simulation outputs of the EFS. The model is based on the movement of coal between the various power stations in an attempt to maintain an optimal inventory level at each station as far as possible. To solve the model, a suitable MOO algorithm is selected and integrated with the simulation component of the EFS. Several experiments are conducted to validate the MOO model and test the e effectiveness of the algorithm in solving the optimisation problem.
Additive manufacturing costing parameter sensitivity
(Stellenbosch : Stellenbosch University, 2019-12) Van der Merwe, Hendrik Lodewyk; De Beer, D. J.; Van der Merwe, A. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Additive manufacturing (AM) offers a perfect solution for the development and manufacturing of many products, but the burning issue is to determine which products should be manufactured in such a way? Also, of extreme importance, is to understand the economy of scale for the use of AM competitively. The latter requires knowledge-based decision-making systems based on product geometry, complexity, size, tolerance, material requirements, and mechanical properties, parallel with AM machine or process capabilities. Although directly involved in the material research and platform development from the onset, the Massachusetts Institute of Technology (MIT) classified AM as only one of ten breakthrough technologies in 2013. Forbes depicts AM as the technology that will equip manufacturers with the ability to turn product development into their competitive advantage. With the advancement in computer and software capabilities, it will rapidly dominate 40% of the market share (Gartner 2015). Capability alone will not suffice, however. To increase market share, focus should be placed on the analysis of AM costing. The thesis aims to determine if a more simplistic but accurate cost determination method can be developed to augment online costing opportunities that are fully integrated with the Enterprise Resource Planning (ERP) system. Costing is one of the critical business functions of any advanced manufacturing operation. This critical business function is also known as enterprise resource planning application components. Examples of these are aspects that allow an AM unit to use a system of integrated applications to manage the business and automate various back-office functions related to technology. It also allows for services and human resources to develop the data capturing, manipulations, calculation, and validation for a unique enterprise resource-planning model that is founded in a fail-safe quality management system (QMS).
Additive manufacturing enabled drug delivery features for titanium-based total hip replacement cementless femoral stems
(Stellenbosch : Stellenbosch University, 2015-03) Bezuidenhout, Martin Botha; Dimitrov, D. M.; Dicks, Leon Milner Theodore; Van der Merwe, A. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Bacterial colonisation and biofilm formation onto total hip replacement femoral stems remain a serious complication detrimental to the success of total hip arthroplasty. Current treatment procedures are accompanied by a heavy financial burden and morbidity for the patient while failing to guarantee a successful outcome with no reinfection. In fact, infection rates after revision surgeries are typically higher than those for primary hip arthroplasties. This study investigates conceptual drug delivery channels to be incorporated within cementless femoral stems by applying additive manufacturing as enabler technology. Drug delivery from these features is aimed at both prophylaxis and treatment of infection, with the latter emphasising the concept of creating a reinforceable antimicrobial depot inside the implant. The novelty lies in facilitating the administration of multiple drug dosages from within the implant instead of the once-off implant-based release strategies currently employed. Samples containing internal channels were designed based on analogies to drug delivery studies reporting on the commercial antibiotic loaded bone cement, Palacos R+G loaded with gentamicin. These samples were manufactured by LaserCUSING® from Ti-6Al-4V ELI powder. For prophylactic proof of concept, testing the channels were filled with Palacos R+G and challenged with two clinical isolates of Staphylococcus aureus in a bacterial growth inhibition study. Gentamicin-susceptible S.aureus Xen 36 was prevented from colonising for a minimum of 72 hours, whereas gentamicin resistant S.aureus Xen 31 reached the material within 24 h, signifying the importance of drug selection according to pathogen. Hence, a solution of vancomycin in phosphate buffered saline pH 7.4 was used during in vitro reservoir release testing. Three dosage injections were made into each of six samples during a cumulative incubation period of 100 h. A biocompatible 5,000 Da molecular weight cut off polyethersulfone nanoporous membrane was employed as release rate-controlling device. Released vancomycin was quantified with reversed phase high performance liquid chromatography. The resulting release profile was characterised by means of the Korsmeyer-and-Peppas model for diffusion based drug delivery. Constraint diffusion was identified as the mechanism controlling release, implying interplay between Fickian diffusion and polymer relaxation for effecting vancomycin release from within the reservoir. The concept created in this study provides a basis towards the development of full scale intelligent implants with multiple dose in situ drug delivery capabilities. Implants incorporating this concept could aid in the perpetual struggle against infection by providing a new strategy for delivery of high level antibiotics directly to the site of infection.
Additive manufacturing for sustainable custom-designed implants
(Southern African Institute for Industrial Engineering, 2019) Booysen, G. J.; Van der Merwe, A. F.; De Beer, D. J.
ENGLISH ABSTRACT: Additive manufacturing (AM) has proven to be an attractive alternative manufacturing process compared with subtractive manufacturing (SM). Additive manufacturing has many advantages, such as mass customisation, less material wastage, and others listed in this article. However, the additive manufacturing of certified implants does not have the same degree of documentation and standardisation as the subtractive manufacturing process. As part of this research project, the problem statement is: “In offering additive manufacturing as an implant manufacturing solution, the complete process (design, manufacturing, and post-processing) had to be investigated in order to develop a certified manufacturing solution”.
Additive manufacturing for the spare part management of classic cars
(Stellenbosch : Stellenbosch University, 2022-04) Paskert, Lukas; Sacks, Natasha; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: The maintenance of older vehicles can be challenging since the supply of spare parts by the original equipment manufacturer is not guaranteed throughout the lifespan of a vehicle. Thus, there is a need to improve the current spare part supply chain for the automotive industry specifically for outdated products like classic cars. Within the automotive industry additive manufacturing (AM) technology is already being implemented in the production cycle of new cars. The need to improve the supply of spare parts for classic cars and the increased use of the AM technology raised the question whether additive manufacturing can have an impact on the spare part management of classic cars. In order to answer this question, this research study started with the review of literature. Three critical scoping literature reviews were undertaken to analyse the current performance of the spare part management of classic cars and to identify spare part attributes for the ranking of spare parts according to their potential for additive manufacturing. To fully understand the spare part management of classic cars, a first literature study on the classic car market was conducted. A second literature review on spare part management within the automotive industry utilized a performance measurement model to measure the impact of adopting additive manufacturing. Based on this, the applicability of additive manufacturing was the subject of the third literature review. Results show that a potential for additive manufacturing exists, and it can be measured with spare part attributes. Based on these results, it was decided to follow an exploratory research design approach. A survey was conducted with classic car owners to identify sourcing problems of spare parts and to assess their willingness to adopt additive manufacturing. The data from the survey was analysed using a ranking methodology from science which was modified toward the application of additive manufacturing on the spare part management of classic cars. The outcome of the ranking highlighted that small parts (e.g. switches) and batches are best suitable for additive manufacturing. A Delphi survey with subject-matter experts validated the ranking method. A case study was carried out in which a speedometer gear was reverse-engineered, additive manufactured and tested under realistic conditions. The case study highlighted that additive manufacturing is feasible to produce spare parts on demand and on a decentralized implementation strategy. Overall, this research has shown that additive manufacturing has a high potential to impact the spare part management of classic cars. The research result showed that unsatisfied customer demand is recognised. Additive manufacturing is a technologically feasible solution to produce many spare parts and has the high potential to increase the supply chain performance of classic car spare parts.
Ageing estimation models for lightly loaded distribution power transformers
(Stellenbosch : Stellenbosch University, 2018-03) Mukuddem, Mohamed Ziyaad; Jooste, J. L.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Power transformers form an integral part of present day electricity networks. They allow for power to efficiently be transported over vast distances. They are however one of the most expensive assets within the distribution network. In order to maximise return on investment for these assets, transformer owners need to ensure that they operate for as long as possible. The ageing of a transformer is based primarily on the condition of the solid insulation inside the transformer. There are various ageing models which attempt to predict the ageing of a transformer based on parameters such as hot-spot temperature, oxygen and moisture content. Typical distribution networks are designed with transformer redundancy. In these networks, the full load of a substation is typically shared across two or more transformers. This results in individual transformers being lightly loaded (<50%). This study investigates the accuracy of the ageing models presented on a fleet of twenty distribution power transformers. The study compiles an algorithm which carries out two main functions. The first is to determine the hot-spot temperature based on loading. The second is to predict the loss-of-life based on the various ageing models identified. This predicted loss-of-life value is compared to measured loss-of-life values in order to determine which model produces the most accurate results. Using these results, the study goes further to modify these ageing models in an attempt to improve the accuracy thereof. These modified model’s accuracy rates are compared to each other as well as the initial ageing models to identify if any improvement in accuracy is produced. A modified output model is produced which increases the accuracy of the loss-of-life prediction for lightly loaded transformers. The modified model utilises the historic average hot-spot operating temperature in order to determine the ageing rate. This can be utilised by asset managers of power transformers in distribution networks.
Agent based job scheduling for a vehicle engine reconditioning machine shop
(Faculty of Engineering, Department of Industrial Engineering, Stellenbosch University, 2016) Nyanga, L.; Van der Merwe, A. F.; Burawa, M.; Matope, S.; Dewa, M. T.
ENGLISH ABSTRACT: Job scheduling at a machine shop is a multi-decision criteria problem whose skills are acquired after some years of experience. For Small, Medium to Micro Enterprises (SMMEs) with limited machinery the objective when scheduling jobs should not only focus on machine utilization but also on the increase of job through put. The paper presents an agent based job scheduling system for a vehicle engine reconditioning machine shop to assist decision makers in job scheduling. The Analytic Hierarchy Process (AHP) method was used to compute the relative weights of each decision criteria used for job scheduling considering the job priority. The value of the job, the number of operations to be performed, the engine type, the frequency of the customer and the company to customer relationship rating are used to prioritize the jobs. A Multi Agent System (MAS) comprising of the provider, job allocator and machine agents is developed using the Java Agent development framework (JADE) methodology and modelled using Unified Modelling Language (UML 2). The provider agent schedules all the jobs based on job weight and earliest due dates. The job allocator agent is responsible for making sure that all the scheduled jobs are allocated to all the machines after which they are registered as complete jobs and can leave the system.
An agent-based approach to customer crowd-shipping
(Stellenbosch : Stellenbosch University, 2022-04) Malan, Phillip Christian; Searle, Christa; Jan H, Van Vuuren; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH SUMMARY: The challenge of effective last-mile deliveries is progressively becoming more important with the acceleration in the e-commerce industry that is accompanied by a growing number of doorstep deliveries. Crowd logistics provides innovative solutions whereby ordinary people become in- volved in the execution of logistics operations. A particular crowd logistics initiative, referred to as customer crowd-shipping, recently gained interest from researchers after initial implemen- tations thereof had been performed by companies such as Walmart and Amazon. The approach involves the use of a retailer’s in-store customers, in addition to regular delivery vehicles, for delivering orders to online customers. Such in-store customers, referred to as occasional drivers, are offered incentives to deliver orders on their way home after visiting the retailer. In this thesis, an agent-based simulation model is proposed for studying the highly dynamic working of the customer crowd-shipping initiative. The model encompasses a traditional last- mile delivery system, complemented by the ability to utilise autonomous occasional drivers. The modelled traditional last-mile delivery system consists of a dedicated fleet of delivery vehicles serving online customers from a single depot. The execution of deliveries is formulated as a vehicle routing problem and subsequently solved by means of well-known vehicle routing heuristics. In addition, the occasional drivers are modelled as autonomous agents who have the ability to act outside of the control of the retailer. Rather than being assigned to particular orders, occasional drivers are presented with potential orders from which they may select an order suitable for them to deliver. Their decision to participate is modelled based on self- interest, where an occasional driver agent aims to maximise the difference between the incentive offered and his or her perceived value of the additional time required to deliver the order. An integrated approach to customer crowd-shipping is developed in order to consider the benefits for both the retailer and occasional drivers. This includes an incentive scheme and a method for identifying online customers as candidates for crowd-shipping. The latter involves the dynamic calculation of the company’s cost to serve an individual customer, which is determined for all online customers. Finally, user-friendly access to the agent-based simulation model is facilitated by a graphical user interface. The proposed model is subjected to systematic verification, ensuring the correct functioning and integration of its subcomponents. Moreover, the model is evaluated under various operating conditions to gain a deeper understanding of the crowd-shipping initiative, while simultaneously validating the model as adequate. In particular, parameter variation, sensitivity analyses, and scenario analyses are conducted, followed by face validation by subject matter experts. The results of the various analyses indicate that customer crowd-shipping may successfully function as an extension to an existing last-mile delivery system, with the potential of reducing both the total delivery cost and customer waiting time. These benefits are, however, shown to be influenced by the incentive scheme and the strategy by which online customers are se- lected as crowd-shipping candidates. Finally, it is deduced that the maturity of the customer crowd-shipping system and the occasional population’s perceived value of time influence the performance of the customer crowd-shipping model.
An agent-based model of Eldana Saccharina Walker
(Stellenbosch : Stellenbosch University, 2016-12) Van Vuuren, Brian John; Potgieter, Linke; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: An agent-based simulation model is formulated in this dissertation in order to simulate the population dynamics of Eldana saccharina Walker infestation in sugarcane. The simulation model comprises four distinct building blocks, namely a graphical user interface, the implementation of the life cycle and associated influence of temperature on E. saccharina, the mating process of E. saccharina and the oviposition and dispersal of E. saccharina. These building blocks are based on existing literature pertaining to the biology and behaviour of the pest and, in cases where the relevant literature is insu cient or non-existent, expert opinion and careful assumption. In order to select areas from existing sugarcane farms on which to execute location-speci c experiments, functionality which allows Geographic Information Systems (GIS) data importation is included as a platform on which to run the simulation model. These data inform the model in respect of the shape and characteristics of the underlying sugarcane elds in which the simulated E. saccharina population interacts. The model interacts and operates within an AnyLogic simulation software environment and, in so doing, aims to emulate the behaviour of a population of E. saccharina moths in sugarcane. It is anticipated that the model implementation may serve as a basis for facilitating future design and testing of control measures in order to suppress the pest and its consequent detrimental e ect to sugarcane through infestation and feeding on interior stalk nutrients. Numerous working mathematical models of the pest exist in the literature, but, in all previous cases, intricate aspects of the stalk borer's biology have been aggregated on a population level and average population changes have been a ected at discrete time steps. The resulting analyses therefore yield conclusions that do not necessarily re ect the continuous, changing nature of E. saccharina on a localised level. Using agent-based modelling, however, the pest's behaviour may be modelled in more detail so as to facilitate more thorough investigation of potential control strategies and their expected e cacy on the pest at di erent points in its life cycle. The agent-based simulation model designed in this dissertation is subjected to a number of veri cation and validation techniques. Furthermore, a pilot sensitivity analysis is conducted to identify the most in uential parameters in the simulation model. These parameters are then considered further in a comprehensive parameter variation analysis in order to illustrate the exibility and diversity of the model in terms of the variety of scenarios pertaining to E. saccha- rina population behaviour that it can accommodate. In some cases, simpli ed implementations of control measures are also imposed on the pest within the model in order to further illustrate its implementation capabilities, as anticipated for future model development and use. In light of this exibility, the model is also presented as a computerised decision support and analysis tool, including the ability to upload and recreate a speci c user's own sugarcane farm shape le, as well as to alter a set of available parameters. This may aid in simulating speci c behaviour in a simulation run in accordance with what has typically been observed by the user, or of hypothetical scenarios which require investigation. In turn, as the model is further developed and detailed control measures are included as part of the simulation execution, it is believed that an appropriate response pertinent to the characteristics of the geographical area under consideration and the corresponding E. saccharina population present in this area may be predicted, allowing for control measure alteration and redesign so as to optimise the associated parameters or actions prior to in- eld implementation. In order to further re ne the model and improve its accuracy, as well as ensure agreement between the existing modelling approaches and actual biological processes in nature, the entire simulation model of E. saccharina is subjected to an expert panel discussion. The experts comprising the panel encompass some of the key researchers pertaining to E. saccharina and other moth behaviour and population dynamics, both in South Africa and internationally. The simulation model is updated or adjusted according to suggestions made and new information shared by the expert panel in an attempt to simulate the pest as accurately as possible in accordance with the body of knowledge currently available. Although several other approaches to modelling E. saccharina populations have been adopted in the past, no existing models implement such a low level of abstraction with respect to the biology of the pest. In addition, previous models are often case-speci c, investigating speci c control measures that are imposed on an aggregate level on a population of the pest. By actively simulating E. saccharina's biological decision-making processes, intricate aspects pertaining to one or a number of interacting control strategies, as well as the manner in which they alter the pest's biology or behaviour, may easily be incorporated using an agent-based simulation modelling approach. Adopting a low level of abstraction also requires extensive information pertaining to the pest and, as such, areas where little understanding still exists with respect to the behaviour of E. saccharina have been highlighted and, consequently, may be prioritised for future entomological research by experts in the eld. Finally, numerous options for future investigation into this problem, including model re nement, control measure design and testing and comparison to existing models, pose positive possibilities for the eventual establishment of a functional, integrated pest management programme for E. saccharina.
Agriculture sector implications of a green economy transition in the Western Cape Province of South Africa : a system dynamics modelling approach to food crop production
(Stellenbosch : Stellenbosch University, 2015-12) Van Niekerk, Jacobus Bosman Smit; Brent, Alan C.; Musango, J. K.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: The Western Cape Province of South Africa has introduced a green economy plan called "Green is Smart". This initiative has the envisaged possibility of providing the Province with a sustainable economy. The transition towards a green economy will, however, have implications on the food crop production in the Province. Agriculture is a vital part of the Province's economy and a "systems thinking" approach is required to better understand how this transition will influence food crop production. The aim of this study is then to better understand systems thinking, identify different system modelling approaches, and to better understand how the Western Cape's agriculture acts as a complex system. By achieving this, the green economy transition can be better managed within the Province's food crop production. After reviewing the literature, system dynamics modelling was identified as the preferred modelling technique to better understand the implications of a green economy transition of the Western Cape's food crop production. The model simulates the production for ten different food crops from 2001 until 2040. Food crops are produced with a combination of different framing practices, namely conventional, organic and conservation farming. There are three different green economy scenarios (pessimistic, realistic and optimistic), and one scenario where current practices are continued (business as usual). The model results indicate that all three green economy scenarios will require significant financial investment. The results also indicate that only the optimistic green scenario might be worth the financial investment when considering the potential benefits. The study further provides recommendations for stakeholders in order to help this transition to a green economy within the Western Cape food crop sector. The study highlights the usefulness of using system dynamics to model and better comprehend complex systems. The limitations of system dynamics modelling are also discussed in this study. Difficulties with obtaining historical data and modelling sporadic events are the two most noteworthy limitations.
An AHP-based evaluation of maintenance excellence cirteria
(South African Institute for Industrial Engineering, 2014) Tendayi, Tinashe George; Fourie, Cornelius
ENGLISH ABSTRACT: A state of Maintenance Excellence is when an organisation has achieved best maintenance practice standards and has reached the benchmark for the performance of maintenance operations. Various models exist in literature that highlight what elements need to be present in an organization in order to achieve maintenance excellence standards. However, these standards have to be prioritised according to the current state of the organisation’s operations. The Analytic Hierarchy Process (AHP) is a technique that is useful in establishing the priority and importance of individual decision-making alternatives through pairwise comparisons. In this study, the AHP process is used to evaluate a set of organisation-specific maintenance excellence criteria. A railway rolling stock maintenance organisation in the Western Cape region of South Africa is used as a case study for this exercise. By applying AHP to the results obtained from a survey conducted at the case study, some inconsistencies were found in the judgments made by the respondents. AHP was then used again to revise these judgments to make them more consistent. The end result of the study was a set of weighted and prioritized maintenance excellence criteria which will be useful in the organization’s endeavors to attain maintenance excellence.
Algorithm selector for dynamic AGV scheduling in a smart manufacturing environment using machine learning
(Stellenbosch : Stellenbosch University, 2022-11) Schweitzer, Felicia Cathrin; Louw, Louis Dr.; Bitsch, Günter Dr.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: Artificial intelligence is considered as significant technology for driving the future evolution of smart manufacturing environments forward. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems because they have such great potential when it comes to improving internal logistics by increasing production flexibility. Consequently, the productivity of the entire system relies on the quality of the schedule, which is capable of achieving massive cost savings by minimizing delay and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. That is why the analysis of scheduling problem classes can help to identify the most suitable algorithm depending on a given problem. Currently, the focus in the literature lies on individual algorithm approaches for specific AGV scheduling scenarios, but the influence of framework conditions to the algorithm performance lacks attention. More research is necessary in terms of the dynamic and independent reaction for optimizing the AGV scheduling procedure without human surveillance in case of failures. To develop an algorithm selection approach for AGV scheduling scenarios, this research answered the question of how machine learning approaches must be implemented so that the allocation of tasks in the context of dynamic AGV scheduling can be improved to increase performance. This study followed Design Science Research, particularly the cognition process based on Osterle et al. (2011) that builds on an analysis, design, evaluation, and diffusion phase. During the design phase, laboratory experiments unveiled the successful implementation of two constraint programming solvers for solving scheduling problems based on the Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP). OR-Tools developed by Google and CP Optimizer of IBM solved large instances in reasonable time, and the performance of the solver strongly depended on the given scheduling problem class and problem instance. Consequently, it is beneficial to make use of an algorithm selection, as the overall production performance increased by selecting the most suitable algorithm for a given instance. The field experiment within the learning factory of Reutlingen University enabled the implementation of the approach within a dynamic environment, that can react to disruptions like machine break-downs or AGV failures. As a limitation, the research considered a simplification of the AGV scheduling problem based on the JSSP and FJSSP. As such, the parameters are limited to transport orders, transport durations, sequences and AGVs. Furthermore, the training of the selector was with a limited amount of 544 benchmark instances. Nevertheless, this research showed an exemplary extension of existing scheduling approaches to develop an algorithm selection model which can be built upon in the future. This research places the focus on constraint programming solutions for scheduling problems and emphasizes the benefits of applying machine learning for algorithm selection on a per-instance base. In this way, scheduling systems can be computationally faster and more efficient in the future and help to achieve the desired overall performance of smart manufacturing systems.
Alignment of a functional innovation strategy
(Stellenbosch : Stellenbosch University, Department of Industrial Engineering, 2011-09) Katz, B. R.; Du Preez, N. D.; Schutte, Corne S.L.
For a strategy to be successful it first needs to be the correct strategy then it needs to be aligned with the business values, structures, capabilities and other strategies and finally it needs to be executed [1]. This paper focuses, specifically, on the alignment of a company’s functional innovation strategy. The connection between the different levels of innovation strategy and other business strategies are developed and explained. A range of different business strategy classifications are presented from the literature and a golden thread of alignment is sewn from a company’s grand, strategic perspective down to its functional strategies. A theoretical framework is presented, which contains the strength of the relationships between different generic strategic perspectives and the components of a functional innovation strategy. The end result of this paper is a suggested decision framework which a company can use to align its functional innovation strategy with its strategic perspective, which in turn should be aligned with the company’s mission and vision. This will enable the company to assign the correct resources to its innovation activities so that innovation enables it to achieve its overall objectives. The decision framework is tested in theory by focusing on the insurance industry and understanding the interaction between the strategic perspectives of a company and that company’s focus on innovation. Evidence is gathered to support assumptions about the company’s innovation focus.
Alignment of internal and external business & innovation domains
(Stellenbosch : Stellenbosch University, 2017-03) Katz, Bernard; Du Preez, Niek; Louw, Louis; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: The concept of strategic alignment, the “golden thread”, lies at the heart of this research. The idea that all available resources are simultaneously attracted by the magnetic pull of a company’s strategy, like a million small compass needles, is both extremely desirable and incredibly difficult for a company to achieve. This dissertation, however, takes the concept of strategic alignment and pulls it and twists it and eventually shapes it so that it may be applied to the highly uncertain and complex environment of the innovation domain. It has been well established, that in the world today, adaptability is a key competitive advantage. Innovation has long been seen as a differentiator in company competitiveness. However, researchers and companies continue to struggle with the dichotomy between the open, unstructured and creative nature of innovation and the need to maximise the opportunity for innovation success through structure. The structuring of innovation activities was, for a long time, a controversial issue with the idea that strategy kills innovation being a widely expressed concern. Today, the value of an Innovation Strategy, which governs the innovation activities in a company, is widely accepted. This dissertation aims to contribute to the ongoing quest for a structured approach to innovation without harming the essence of innovation, human ingenuity. In working towards achieving this aim the Strategic Alignment Model was recognised as a wellestablished, logical and elegant model to manage the alignment between the overall business domain and a support function domain. Through analysis of the strategic alignment and innovation management literature, the Strategic Alignment Model was adapted to represent the alignment between the business and innovation domains. The adapted model is called the innovation Strategic Alignment Model (iSAM). The iSAM is a unique framework which integrates concepts from the field innovation management such as innovation scope, innovation maturity, innovation governance and innovation models with a well-established strategic alignment approach. No other model in the identified literature provided this link between these two research fields. The structure and content of iSAM was validated with a panel of experts and their feedback is included in various sections of the dissertation. The implementation of the iSAM in a real company requires a structured implementation framework. Such a framework is presented in this dissertation and both the iSAM and the implementation framework are tested through a multi-year case study. A key consideration is that it was not the aim of the case study to generalise the results for other industries or company types, but rather to demonstrate how such a model may be used to understand the alignment between the innovation and business domains and whether or not the model can be practically applied in order to generate alignment recommendations for a real company. Feedback from senior managers in the focus company was that the iSAM made the innovation landscape clearer and that through iSAM a greater understanding of the components of an Innovation Strategy was gained. This dissertation concludes with the summary of findings from the expert validation exercise and the multi-year case study. There seemed to be a general consensus amongst the experts that the iSAM does provide a valuable framework for Innovation Strategy alignment and that the model comprehensively addresses a critical issue facing companies. The experts also highlighted several areas of improvement and future research. These include: catering for variations in the model based on company size, company type and industry and further empirical evidence for some of the defined relationships in the model. From the case study it was concluded that iSAM provides a simple, clear, common understanding of the components involved in Innovation Strategy and innovation alignment, the implementation framework provides practical guidance and applicable tools to assist in determining innovation alignment and in following a change pathway to achieve alignment and the final recommendations provide clear instructions on what a company needs to do in order to ensure alignment is achieved. The case study also reveals several interesting challenges. These include: ensuring the entire management team engages with the model and the implementation approach and ensuring the applicability of the model for a specific company. Finally,it is the hope of the author that this dissertation initiates another small twig, which over time and through the work of other researchers in the field, contributes to the growing knowledge in the innovation domain.
Alignment of internal and external business and innovation domains
(SAIIE, 2016-05) Katz, Bernard R.; Du Preez, Niek D.; Louw, Louis
ENGLISH ABSTRACT: “Instead of being really good at doing some particular thing, companies must be really good at learning how to do new things”. This ability to do new things involves both the identification and the mastery of the next ‘new thing’ as well as the ability to align the rest of the company with this new direction. This paper focuses on the alignment of innovation activities with the overall business strategy and infrastructure. An innovation strategic alignment model is presented that supports alignment in a company by identifying the driver of change and its impact on other areas of the business.

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