SUNScholar - Browsing by Author "Bekker, James"

Browsing by Author "Bekker, James"

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Applying the cross-entropy method in multi-objective optimisation of dynamic stochastic systems
(Stellenbosch : Stellenbosch University, 2012-12) Bekker, James; Van Vuuren, J. H.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
ENGLISH ABSTRACT: A difficult subclass of engineering optimisation problems is the class of optimisation problems which are dynamic and stochastic. These problems are often of a non-closed form and thus studied by means of computer simulation. Simulation production runs of these problems can be time-consuming due to the computational burden implied by statistical inference principles. In multi-objective optimisation of engineering problems, large decision spaces and large objective spaces prevail, since two or more objectives are simultaneously optimised and many problems are also of a combinatorial nature. The computational burden associated with solving such problems is even larger than for most single-objective optimisation problems, and hence an e cient algorithm that searches the vast decision space is required. Many such algorithms are currently available, with researchers constantly improving these or developing more e cient algorithms. In this context, the term \e cient" means to provide near-optimised results with minimal evaluations of objective function values. Thus far research has often focused on solving speci c benchmark problems, or on adapting algorithms to solve speci c engineering problems. In this research, a multi-objective optimisation algorithm, based on the cross-entropy method for single-objective optimisation, is developed and assessed. The aim with this algorithm is to reduce the number of objective function evaluations, particularly when time-dependent (dynamic), stochastic processes, as found in Industrial Engineering, are studied. A brief overview of scholarly work in the eld of multiobjective optimisation is presented, followed by a theoretical discussion of the cross-entropy method. The new algorithm is developed, based on this information, and assessed considering continuous, deterministic problems, as well as discrete, stochastic problems. The latter include a classical single-commodity inventory problem, the well-known buffer allocation problem, and a newly designed, laboratory-sized recon gurable manufacturing system. Near multi-objective optimisation of two practical problems were also performed using the proposed algorithm. In the rst case, some design parameters of a polymer extrusion unit are estimated using the algorithm. The management of carbon monoxide gas utilisation at an ilmenite smelter is complex with many decision variables, and the application of the algorithm in that environment is presented as a second case. Quality indicator values are estimated for thirty-four test problem instances of multi-objective optimisation problems in order to quantify the quality performance of the algorithm, and it is also compared to a commercial algorithm. The algorithm is intended to interface with dynamic, stochastic simulation models of real-world problems. It is typically implemented in a programming language while the simulation model is developed in a dedicated, commercial software package. The proposed algorithm is simple to implement and proved to be efficient on test problems.
Customer super-profiling demonstrator to enable efficient targeting in marketing campaigns
(Southern African Institute for Industrial Engineering, 2017-11-22) Walters, Marisa; Bekker, James
ENGLISH ABSTRACT: Difficulties lie with identifying the right customers to engage in successful marketing campaigns. Thus far, segmentation has been a popular marketing method for selecting customer groups for targeted campaigns. However, each segment can be further exploited by performing customer profiling. In this paper, we explain the on-going development of a proposed simulator and demonstration tool that incorporates big data analytics to uncover hidden patterns within the customer dataset, thereby generating a customer super-profile. A developed toy problem and a large, realistic problem demonstrate segmentation, via clustering, and create customer profiles to enable marketers to identify appropriate marketing strategies. The proposed framework serves as the basis for enhancing customer relationship management by providing improved customer profiles for marketing campaigns.
Determining tactical operational planning policies for an auto carrier - a case study
(SAIIE, 2010) Du Plessis, A. J.; Bekker, James
ENGLISH ABSTRACT: This study was done to assist a local auto carrier company with tactical operational planning. The objective of the planning process is to maximise the number of vehicles delivered while being on time and adhering to staff and maintenance schedule constraints. We investigated the feasibility of allowing part of the fleet to roam the closed spatial network, as opposed to the traditional assignment of the complete fleet to fixed routes. We developed decision-making rules for roaming and fixed-to-route auto carriers, and evaluated the quality of these proposed rules, in combination with different fleet compositions, using discrete event simulation and four performance measures. We found that the auto carrier company should adopt a tactical operations policy where at least 50% of the fleet is allowed to roam, while roaming auto carriers pick vehicles to transport according to specific rules.
Developing a tool for project contingency estimation in a large portfolio of construction projects
(Southern African Institute for Industrial Engineering, 2014-11) Van Niekerk, Mariette; Bekker, James
To enable the management of project-related risk on a portfolio level in an owner organisation, project contingency estimation should be performed consistently and objectively. This article discusses the development of a contingency estimation tool for a large portfolio that contains similar construction projects. The purpose of developing this tool is to decrease the influence of subjectivity on contingency estimation methods throughout the project life cycle, thereby enabling consistent reflection on project risk at the portfolio level. Our research contribution is the delivery of a hybrid tool that incorporates both neural network modelling of systemic risks and expected value analysis of project-specific risks. The neural network is trained using historical project data, supported by data obtained from interviews with project managers. Expected value analysis is achieved in a risk register format employing a binomial distribution to estimate the number of risks expected. By following this approach, the contingency estimation tool can be used without expert knowledge of project risk management. In addition, this approach can provide contingency cost and duration output on a project level, and it contains both systemic and project-specific risks in a single tool.
Development and demonstration of a customer super-profiling tool to enable efficient targeting in marketing campaigns
(South African Institute for Industrial Engineering, 2018) Walters, Marisa; Bekker, James
ENGLISH ABSTRACT: Being part of a competitive generation demands having good marketing policies to attract new customers as well as to retain existing customers. This research outlines a general methodology for segmentation of customers by using the model of Recency, Frequency and Monetary (RFM) to identify types of customers, and then predict their customer profiles, based on demographic and behavioural features. A few previous studies dealt with the question using non-aggregate customer data. We, however, also address the problem by using decision trees, something which has rarely been done before. We applied and demonstrated this tool on a large customer dataset and found useful results.
Development of a data analystics-driven system for instant, temporary personalised discount offers
(South African Institute for Industrial Engineering, 2018) Els, Zandaline; Bekker, James
The innovation of targeting customers with personalised discount offers has been incorporated into business strategies in order to ensure a competitive advantage amongst peers along with ensuring customer experience. In this article, a demonstrator model was developed which provides a holistic view of an individual customer’s behaviour in retail outlets. The demonstrator creates instant, temporary personalised discount offers based on the purchasing tendencies of that customer in retail outlets. The model illustrates the utilisation of customer behavioural data and data analytics to identify unique cross-selling and upselling opportunities to ultimately improve customer experience. This article also includes the architecture of the proposed model along with the results from the demonstrator model.
Development of a demonstrator of big data analytics
(South African Institute for Industrial Engineering, 2018) Butler, Rhett Desmond; Bekker, James
Big Data Analytics is now not only being applied in the fields of science and business, but in healthcare and economic development, by organisations such as the United Nations. The research presented in this article provides a demonstration of developing a Big Data Analytics Demonstrator by integrating selected hardware and software. The components of such an analytics tool are presented, as well as the analysis of results of test data sets. Experience gained when setting up a proprietary data analytics suite is shared, and practical recommendations are made. The goal of this demonstrator is to illustrate that a system could be built to provide meaningful insights into a given dataset, by making use of free-to-use software, commodity hardware and leveraging machine learning to mine the data for these insights.
Managing customer experience using data analytics in a partnering venture
(South African Institute for Industrial Engineering, 2018) Roos, Maryke; Bekker, James
Today’s technologies are changing the physical, digital and biological worlds. This change impacts the economy and how industries operate. In the light of this, we ask how these changes can be incorporated into customer experience. To investigate this, a demonstrator was developed in which a customer’s activities are simulated on a full-scale partnering platform while the business data are captured and analysed. The focus is on the domain of travel, in which customers use several modes of transportation while engaging with various collaborating enterprises. This paper will discuss the results and findings obtained, showing how customer experience can be managed and improved with the use of data analytics in a partnering venture.
A multi-objective coal inventory management model using Monte Carlo computer simulation
(Southern African Institute for Industrial Engineering, 2016) Brits, Ryno; Bekker, James
The ability of a coal-fired power station to meet its generation targets is influenced by periods of coal shortage. In this article, we propose a multi-objective inventory model to assist with the management of coal stockpiles. The model is applicable to power utilities with a network of two or more coal-fired power stations. The aim is to determine the near-optimal amount of coal inventory to stockpile at each station in the network. A Monte Carlo coal stockpile simulator is used to incorporate stochastic uncertainty into the stockpile levels, while a metaheuristic uses the simulator as an estimator of two objective functions. The metaheuristic finds good values for the coal stockpile level at each power station in the network. The algorithm for multi-objective optimisation using the cross-entropy method is proposed as a suitable metaheuristic. A hypothetical case study is used to validate the inventory model and to showcase the optimisation results.
Multi-objective optimisation in carbon monoxide gas management at Tronox KZN Sands
(Southern African Institute for Industrial Engineering, 2014-08) Stadler, Johan; Bekker, James
Carbon monoxide (CO) is a by-product of the ilmenite smelting process from which titania slag and pig iron are produced. Prior to this project, the CO at Tronox KZN Sands in South Africa was burnt to get rid of it, producing carbon dioxide (CO2). At this plant, unprocessed materials are pre-heated using methane gas from an external supplier. The price of methane gas has increased significantly; and so this research considers the possibility of recycling CO gas and using it as an energy source to reduce methane gas demand. It is not possible to eliminate the methane gas consumption completely due to the energy demand fluctuation, and sub-plants have been assigned either CO gas or methane gas over time. Switching the gas supply between CO and methane gas involves production downtime to purge supply lines. Minimising the loss of production time while maximising the use of CO arose as a multi-objective optimisation problem (MOP) with seven decision variables, and computer simulation was used to evaluate scenarios. We applied computer simulation and the multi-objective optimisation cross-entropy method (MOO CEM) to find good solutions while evaluating the minimum number of scenarios. The proposals in this paper, which are in the process of being implemented, could save the company operational expenditure while reducing the carbon footprint of the smelter.
Real-time scheduling in a sensorised factory using cloud-based simulation with mobile device access
(Southern African Institute for Industrial Engineering, 2017) Snyman, Stephan; Bekker, James
ENGLISH ABSTRACT: Scheduling is a challenge that persists in the operational phase of the manufacturing life-cycle. The challenge can be attributed to the complex, dynamic, and stochastic nature of a manufacturing system. Computer simulation is often used to assist with scheduling, as it can sufficiently mimic complex, discrete, dynamic, stochastic processes. We propose an architecture of a real-time simulation scheduling system that incorporates the use of a sensorised-network of a job-shop, mobile devices, and cloud computing with simulation and scheduling methods. A simulation model is also created to describe the environment and operations of a job-shop.
A real-time scheduling system for a sensorised job shop using cloud-based simulation with mobile device access
(South African Institute for Industrial Engineering, 2018) Snyman, Stephan; Bekker, James; Botha, John-Pieter
Scheduling is a challenge that continues to trouble management of the operational phase of the manufacturing life cycle and can be attributed to the complex, dynamic and stochastic nature of a manufacturing system. Computer simulation is often used to assist with scheduling, as it can sufficiently mimic complex, discrete, dynamic, stochastic processes. We propose and develop a prototype real-time simulation scheduling system for a sensorised factory, which is to serve as a decision support tool for real-time rescheduling of machine steps in a job shop.
Single- and multi-objective ranking and selection procedures in simulation : a historical review
(Southern African Institute for Industrial Engineering, 2017-08-31) Yoon, Moonyoung; Bekker, James
ENGLISH ABSTRACT: Ranking and selection (R&S) procedures form an important research field in computer simulation and its applications. In simulation, one usually has to select the best from a number of scenarios or alternative designs. Often, the simulated processes have a stochastic nature, which means that, to distinguish alternatives, they must exhibit significant statistical differences. R&S procedures assist the decision-maker with the selection of the best alternative with high confidence. This paper reviews past and current R&S procedures. The review traces back to the 1950s, when the first R&S procedure was proposed, and discusses the various R&S procedures proposed since then to the present day, presenting a cursory view of the research in the area. The review includes studies in both the single-objective and the multi-objective domains. It presents the research trend, discusses specific issues, and gives recommendations for future research in both domains.
Using machine learning to predict the next purchase date for an individual retail customer
(Southern African Institute for Industrial Engineering, 2020-11-11) Droomer, Marli; Bekker, James
ENGLISH ABSTRACT: Targeted marketing has become more popular over the last few years, and knowing when a customer will require a product can be of enormous value to a company. However, predicting this is a difficult task. This paper reports on a study that investigates predicting when a customer will buy fast-moving retail products, by using machine learning techniques. This is done by analysing the purchase history of a customer at participating retailers. These predictions will be used to personalise discount offers to customers when they are about to purchase items. Such offers will be delivered on the mobile devices of participating customers and, ultimately, physical, general paper-based marketing will be reduced.

Browsing by Author "Bekker, James"

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