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Air traffic is continuously increasing and more efficient air transport systems are required to handle the air travel demand. The study investigates the expansion of Lanseria International Airport in Gauteng, South Africa. Expansion of Lanseria requires a study of the airport apron layout to ensure efficient passenger-aircraft flow as well as the efficient flow of aircraft to and from the airport. The candidate layout designs are based on the layout concept of the Hartsfield-Jackson Atlanta International Airport in Atlanta, USA. In the study, different airport apron layouts were compared, including the existing layout of Atlanta Airport, via a simulation model of each. Designs based mainly on passenger transfer distance between the terminal building and aircraft were evaluated. The cross-entropy method was used to develop a generic flight-to-gate assignment program that minimises passenger transfer distances.

Consumers' practices and knowledge play a significant role in ensuring meat safety and hygiene application. This is important in an environment where the slaughter and release of such meat is restricted and not always controlled and monitored. This could have been contributed by the lack of slaughter facilities; the controlled movement of live animal restrictions applicable in a typical veterinary diseases-controlled area. The aim of this investigation was to identify meat hygiene practices and general meat safety knowledge applied by consumers of central Bushbuckridge, South Africa. Structured interviews (n=81) were conducted with consumers observed purchasing meat from local meat supply centres. Data analysis was achieved by SAS Statistical software (Version 26) SAS and graphs were drawn using MS Excel 2020. The majority (84.2%, s=29.4) of the consumers indicated that meat supplied in the region were from registered facilities. The general belief amongst the consumers was that routine hygiene inspection was conducted by authorities at all meat distributing or butchery facilities (74.1%, s=39.7). None of the facilities or/and butcheries in the study area was registered and therefore, no formal regulated practices could have been conducted. Slaughter and distribution of meat within the area were seen as a good business by 78.6%, s=18.9 of the consumers, and as a result, the consumers did not have a problem with widespread slaughter and release of this meat to the public outside of those facilities. The extent of hygiene application practices at household level varied. These included the assurance that perishable products were properly handled after purchase (84.1%, s=8.3), and the handling or transportation of sourced meat did not comply with cold chain management prescripts (39.9%, s=44.9). The likelihood of consumers feeling the same about meat hygiene concerns and practices is undoubtedly low as represented by a high standard deviation (s) of the responses. These results show that consumers were relying on authorities for meat safety assurances. This ought to caution authorities on consumer practices and needed interventions such as animal movement policies enforcement. The adoption of such policies by consumers lies with the development and rollout of consumer awareness and meat safety assurances training programs. [ABSTRACT FROM AUTHOR]

Maintenance of rail infrastructure is an important element in rail operations to keep traffic moving. However, maintenance causes infrastructure to be taken out of service, which impacts traffic flow. In this study, the requirements of a maintenance possession scheduler for a South African application were investigated and a proposed solution was developed. The main objective of the scheduler was to minimise the deviation of the train service on a subset of rail infrastructure while ensuring that the required maintenance was done. An application case – the railway infrastructure between Bellville and Wellington in the Western Cape province of South Africa – was identified. A novel mixedinteger linear programming model that could do possession scheduling for 24 hours on a microscopic level was formulated for this case, and implemented in the software Cplex, after which it was validated. Finally, several experiments were conducted to investigate the model’s performance and the results. It was found that the model delivered optimal results in less than nine minutes, which makes it a feasible maintenance possession scheduler for day-to-day work in the immediate planning horizon. [ABSTRACT FROM AUTHOR]

The South African forestry supply chain is a complex system that involves both the production of trees (site preparation, re-establishment, tending, and maintenance operations) and the production and distribution of wood products from plantation stands to the pulp- and sawmills. This paper reports on a study that investigated the suitability of discrete-event simulation for modelling forestry supply chains. As part of a larger research project, two discrete-event simulation models with bi-objective optimisation of the South African pulpwood and saw-timber supply chains respectively were developed. The simulation models were developed for each supply chain by first mapping out the supply chain processes through subject-matter expert and stakeholder consultation. Thereafter simulation concept models were developed, and the required input data were identified, acquired, and verified. The simulation models were verified and validated to be realistic representations of the supply chains that they imitate. The study concluded that the simulation models demonstrated the ability to perform scenario analysis and biobjective optimisation. It was also demonstrated that the two disciplines of industrial engineering and forestry science can successfully collaborate. [ABSTRACT FROM AUTHOR]

With the increasing need to solve problems faster and with fewer resources, great emphasis is placed on optimisation. Many real-world problems require addressing more than one objective that are in conflict, as well as taking into consideration a number of practical restrictions or constraints. The multi-objective optimisation using the cross-entropy method (MOO CEM) algorithm is one of many algorithms that addresses the need to solve multi-objective problems effectively, but it has a number of limitations. This paper explores methods of enhancing the MOO CEM algorithm in order to improve the efficiency and increase the functionality of the algorithm, allowing for it to be applied to additional classes of problems. Three possible methods of enhancement were identified: using the beta distribution to improve sampling, adding functionality to solve constrained problems and, lastly, implementing a non-dominated sorting algorithm to solve problems with more than two objectives. The new algorithms incorporating these enhancements were developed and tested on benchmark problems. Subsequently, the results were analysed using standard performance indicators and compared to results produced by the original MOO CEM algorithm. The findings of this study indicate that using the beta distribution improves sampling and therefore algorithm efficiency. Methods of handling constraints and solving problems with an increased number of objectives were implemented successfully. Based on these results, a final algorithm implementing the enhancements is presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, the authors present the development of an optimisation suite and its implementation. This paper is part of an ongoing project that aims at developing a hybrid metaheuristic optimisation suite to solve multi-objective simulation optimisation problems. The suite is developed as a third-party library to solve discrete-event, stochastic simulation optimisation (SO) problems with multi-objectives, and is integrated with Tecnomatix Plant Simulation, a simulation software package developed by Siemens. Two real-life multi-objective SO problems are selected to test the suite's performance, and the test results are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]