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Economic transformation and escalating market competitiveness have prompted manufacturers to adopt zero-defect manufacturing principles to lower production costs and maximise product quality. The key enabler of zero-defect manufacturing is the adoption of data-driven techniques that harness the wealth of information offered by digitalised manufacturing systems in order to predict errors. Multi-stage manufacturing systems, however, introduce additional complexity owing to the cascade effects associated with stage interactions. A generic modular framework is proposed for facilitating the tasks associated with preparing data emanating from multi-stage manufacturing systems, building predictive models, and interpreting these models' results. In particular, cascade quality prediction methods are employed to harness the benefit of invoking a stage-wise modelling approach. The working of the framework is demonstrated in a practical case study involving data from a multistage semiconductor production process. [ABSTRACT FROM AUTHOR]

Fantasy Premiere League (FPL) is a popular online sports prediction game based on the well-known (soccer) English Premiere League (EPL). In FPL, each participant forms a series of imaginary teams over the course of a season of the EPL, each composed of real-world soccer players. Points are then awarded to participants based on the real-world performances of the players in the EPL. The goal is to accumulate as many points as possible during the season. FPL participants are allocated a budget for team selection which gives rise to the constrained team selection optimisation problem of filling playing positions in the team for each game week of the FPL season. This team selection problem is further complicated by the facts that player performance is not known in advance with certainty and that participants may only make limited changes to their team compositions in the form of transfers during any game week. In this paper we adopt a combinatorial optimisation approach towards participating in FPL, in which future player performances are forecast by statistical and machine learning techniques. We demonstrate retrospectively that our approach would have placed within the top 4% of players worldwide during the 2020/2021 FPL season. [ABSTRACT FROM AUTHOR]

Fitness landscape analysis has found numerous applications in industrial engineering, such as estimating optimisation problem complexity, predicting metaheuristic performance, and automating algorithm selection. In these applications, relationships between properties of the fitness landscape and metaheuristic algorithmic appropriateness are often analysed. The ability of a metaheuristic to traverse diverse areas of the feasible region is, however, typically overlooked when analysing algorithmic performance by invoking traditional measures of fitness landscape characteristics. In this paper, we propose three novel fitness landscape measures that are tailored to analyse the structure and degree of connectedness of the feasible region. These measures are related to the degree of neighbourhood feasibility, the size of the feasible region relative to that of the entire search space, and the tightness of the constraints. The significance of these measures is demonstrated in a suite of fitness-landscape analyses. When incorporated into a metaheuristic configuration machine-learning model, the measures yield accuracy improvements up to 6.4%. [ABSTRACT FROM AUTHOR]

Since the inception of algorithmic trading during the mid-1970s, considerable resources and time have been committed by the financial sector to the development of trading algorithms in the hope of obtaining a competitive advantage over human contenders. A plethora of trading algorithms has been proposed in the literature; each algorithm is unique in its design, but little emphasis has been placed on heterogeneous trading strategy ensembling. In this paper, we propose a trading strategy ensemble method for combining three different domain-specific trading strategies: a deterministic strategy, a probabilistic strategy, and a machine-learning strategy. The objective of the trading strategy ensemble is to find an appropriate trade-off between the levels of return and the risk exposure of a trader. We implement our strategy across different historical forex currency pair data in a bid to validate the trading strategy ensemble, and we analyse the results by invoking appropriate return and risk performance measures. [ABSTRACT FROM AUTHOR]

Anomaly detection has a wide variety of applications, ranging from intrusion detection in cybersecurity to fraud detection in finance. Among the most prominent applications is predictive maintenance in manufacturing, which involves performing maintenance only when truly necessary, based on the condition of relevant equipment instead of following a fixed maintenance schedule. When implemented correctly, predictive maintenance can lead to more significant cost savings than other preventative maintenance approaches. Unfortunately, the unique challenges present in anomaly detection (including the very broad definition of an anomalous instance) make it particularly difficult to choose an appropriate algorithm, since each algorithm's performance is so dependent on the use case. In this paper we present an up-to-date taxonomy of univariate anomaly detection approaches to predictive maintenance, which is aimed at aiding practitioners to design effective predictive maintenance models for their specific use cases, based on numerical benchmark tests. [ABSTRACT FROM AUTHOR]

Explicitly incorporating individual vehicle acceleration into a traffic simulation model is not a trivial task, and typically results in a considerable increase in model complexity. For this reason, alternative implicit techniques have been introduced in the literature to compensate for the delay times associated with acceleration. In this paper, the claim is investigated that these implicit modelling techniques adequately account for the time delays due to vehicle acceleration; the modelling techniques are implemented in a simulated environment, and compared with models in which vehicle acceleration has been incorporated explicitly for a number of traffic network topologies and traffic densities. It is found that considerable discrepancies may result between the two approaches.

In the generator maintenance scheduling (GMS) problem, a schedule is sought for the planned maintenance outages of generating units in a power system. The GMS model considered in this paper is formulated as a mixed-integer quadratic program assuming a reliability objective, subject to certain constraints. A generic GMS solution methodology is derived, capable of solving general GMS problem instances that conform to this model structure, and implemented in a computerised decision support system. The decision support system is then used to solve a real-life case study in the South African national power system.

Little is known about those who are widowed while raising dependent children. This study aimed to explore the factors which influence adjustment to partner death. Seven fathers and five mothers were interviewed, and constructivist grounded theory was used. Three interrelated themes were identified: Interpersonal influences, Intrapersonal influences, and Contextual influences. Dependent children meant sole responsibility and increased demands, yet ultimately provided widowed parents a purpose. Participants highlighted the need for increased awareness of young widowhood at a systemic and cultural level, to improve communication around death and young widowhood. Implications included social, financial and therapeutic interventions. [ABSTRACT FROM AUTHOR]

Models for the design of public bus transit networks are usually based on the historically observed origin-demand travel patterns of potential passengers. The option of a passenger choosing to walk is, however, not usually incorporated into the design models for such networks. We argue that the absence of this model feature might lead to significant suboptimality, especially in the design of small public bus transit networks. A novel approach to including this feature in the bus frequency setting aspect of public bus transit network design models is demonstrated in this paper, in which passenger transit modal choices are modelled on the basis of an existing optimal strategy transit assignment model. The newly proposed biobjective route design and frequency setting models are applied to a realistic case study involving the establishment of a public bus transit system for students on a university campus; and this model implementation is used to gain insight into passenger route choice behaviour, based on individual preferences between walking or using buses. It is shown in the context of this case study that including the option of a passenger walking in the design models for public bus transit networks can affect the efficacy of the final network, especially if the network is small. [ABSTRACT FROM AUTHOR]

One of the most challenging decisions that has to be made routinely by dispatch managers at distribution centres of warehousing and distribution businesses in the retail sector involves the assignment of delivery vehicles to service customers exhibiting demand for retail goods and the subsequent routing of these delivery vehicles to the various customers and back again. Perhaps surprisingly, these dispatch managers do not always use vehicle routing software to schedule goods deliveries to customers, instead often relying on teams of human schedulers who perform this task manually. The reason for not using such software is usually a perception that it may be difficult to integrate the software with existing enterprise resource planning systems already in use. In such cases, estimates of potential costs savings that may be brought about by appropriate software is often required before the dispatch department will risk the significant step towards investing in vehicle routing planning software. Dispatch managers may then employ these cost savings estimates in cost-benefit trade-off analyses. This paper contains a practical case study in which the potential cost savings of a vehicle routing optimisation approach are quantified for a large retail distribution centre in the South African Western Cape in a bid to support its decision as to whether or not to invest in vehicle routing planning software. [ABSTRACT FROM AUTHOR]

With the explosive growth of the Internet and social media, the communication model between an organisation and its customers has become increasingly complex. A problem arises due to the sheer volume of unstructured data that has to be processed for the purposes of studying and addressing customer feedback. This calls for the development of automated methods. Important objectives of such methods include the detection of the underlying sentiment of customer feedback, as well as the synthesis and presentation of this sentiment in meaningful clusters such as topics and geographical locations. In this paper, a case study is conducted in which unstructured customer reviews related to products and services of a South African retail bank are evaluated by means of sentiment analysis. After suitable preprocessing techniques are applied to the reviews, the process of developing suitable models (primarily within the realm of machine learning) for detecting sentiment with a high level of performance is described. Subsequently, model results are analysed, synthesised and visualised in order to extract valuable insight from the data. The findings of the study show that custom learning-based models significantly outperform both pre-trained and commercial tools in sentiment classification. Furthermore, the analysis approach is shown to yield actionable information that may inform decision making. [ABSTRACT FROM AUTHOR]

Traffic signal optimisation may lead to the alleviation, to some extent, of urban traffic congestion, particularly by using real-time data rather than expected traffic flow data. Recent advances in radar technology have made it possible to observe detailed traffic flow data in and around roadway intersections in real time. The notion of self-organisation has relatively recently been proposed as a promising alternative to improve the effective allocation of green time, particularly under lighter traffic conditions. A fixed-time control strategy and seven self-organising algorithms are compared in a microscopic traffic simulation model of a provincial road in the Western Cape province of South Africa. Actual arrival rates are used as input for the model, while the algorithms are compared using six performance measure indicators, under both light and moderate traffic conditions. The results are used to make a case for the adoption of self-organising traffic signal control algorithms, especially under conditions of light to moderate traffic densities, since this can lead to significant improvements in traffic flow in terms of delay time, vehicle stops, and time spent travelling at unacceptably slow speeds through the road network. [ABSTRACT FROM AUTHOR]

Fixed-time control and vehicle-actuated control are two distinct types of traffic signal control. The latter control method involves switching traffic signals based on detected traffic ows and thus offers more exibility (appropriate for lighter traffic conditions) than the former, which relies solely on cyclic, predetermined signal phases that are better suited for heavier traffic conditions. The notion of self-organisation has relatively recently been proposed as an alternative approach towards improving traffic signal control, particularly under light traffic conditions, due to its exible nature and its potential to result in emergent behaviour. The effectiveness of five existing self-organising traffic signal control strategies from the literature and a fixed-control strategy are compared in this paper within a newly designed agent-based, microscopic traffic simulation model. Various shortcomings of three of these algorithms are identified and algorithmic improvements are suggested to remedy these deficiencies. The relative performance improvements resulting from these algorithmic modifications are then quantified by their implementation in the aforementioned traffic simulation model. Finally, a new self-organising algorithm is proposed that is particularly effective under lighter traffic conditions. [ABSTRACT FROM AUTHOR]

The retail problem of slotting refers to the assignment of stock keeping units (SKUs) to the available storage locations in a distribution centre (DC). Generally, the expected total distance travelled by stock pickers during an order consolidation and the resulting level of congestion experienced within aisle racking are common considerations when making these assignments. These criteria give rise to a bi-objective optimisation model with the aim of identifying multiple stock setups that achieve acceptable tradeoffs between minimising the criteria on expectation. A mathematical framework is established in this paper, based on these two criteria, for evaluating the effectiveness of a given stock setup. In the framework, a stock picker's movement between various storage locations is modelled as a Markov chain in order to quantify his or her expected travel distance, while a closed queuing network model is used to devise a suitable measure of congestion. This optimisation model framework forms the basis of a flexible decision support system (DSS) for the purpose of discovering highquality stock assignment trade-off solutions for inventory managers. The DSS is applied to a special case study involving data from a real DC, and the desirability of the recommended stock configurations is compared with that currently implemented within the DC. [ABSTRACT FROM AUTHOR]

Abstract: Eldana saccharina Walker (Lepidoptera: Pyralidae) is a stalk borer pest plaguing the sugar producing industry in South Africa . The pest feeds on the internal tissue of sugarcane stalks, causing yield losses in sucrose. Various control methods have been proposed in an attempt to suppress the pest. These control methods are, however, often difficult and costly to test, implement, and develop further in an iterative manner. In an attempt to better understand the behavior and population dynamics of E. saccharina, an agent‐based simulation model is presented in this paper which simulates the stalk borer's biology, feeding habits, mating behavior, dispersal patterns, and various other characteristics as accurately as possible. The flexibility and validity of the model are then ascertained by subjecting the model to expert opinion and parameter variation analysis. A simplified control measure is then implemented in order to demonstrate the possibility for using the agent‐based model in the future for developing and testing integrated pest management strategies against E. saccharina. Recommendations for Resource Managers: The use of agent‐based simulation modeling is investigated as a possible means for accurate capturing of the behavior of E. saccharina. The ability to accurately recreate pest behavior poses the opportunity for the detailed testing and development of control strategies to combat this sugarcane pest. By employing simulation modeling, control strategies may be tested to assess their anticipated success in eradicating the pest. The speed with which long‐term analyses may be conducted, as well as the absence of the associated cost of infield testing, means that various scenarios may be investigated over a number of iterations in an attempt to optimize the process and achieve maximum confidence prior to infield implementation of the control strategies. The successful implementation of this science may provide a platform to extend this type of modeling to include other pests and/or plant hosts which may benefit from pest suppression. [ABSTRACT FROM AUTHOR]

The availability of irrigation water greatly impacts on the profitability of the agricultural sector in South Africa and is largely determined by prudent decisions related to water release strategies at open-air irrigation reservoirs. The selection of such release strategies is difficult, since the objectives that should be pursued are not generally agreed upon and unpredictable weather patterns cause reservoir inflows to vary substantially between hydrological years. In this paper, a decision support system is proposed for the selection of suitable water release strategies. The system is based on a mathematical model which generates a probability distribution of the reservoir volume at the end of a hydrological year based on historical reservoir inflows. A release strategy is then computed which centres the expected hydrological year-end reservoir volume on some user-specified target value subject to user-specified weight factors representing demand satisfaction importance during the various decision periods of the hydrological year. The probability of water shortage for a given year-end transition volume may be determined by the decision support system, which allows for the computation of acceptable trade-off decisions between the fulfilment of current demand and the future repeatability of a release strategy. The system is implemented as a computerised concept demonstrator which is validated in a special case study involving Keerom Dam, an open-air reservoir in the Nuy agricultural district near Worcester in the South African Western Cape. The system's strategy suggestions are compared to historically employed strategies and the suggested strategies are found to fare better in maintaining reservoir storage levels whilst still fulfilling irrigation demands. [ABSTRACT FROM AUTHOR]

The resource constrained scheduling problem involves the scheduling of a number of activities over time, where each activity consumes one or more resources per time period. For a feasible solution to exist, the total resource consumption per time period must not exceed the available resources. In addition, the order in which activities may be scheduled is determined by a precedence graph. In this paper, valid inequalities proposed for the resource flow-based formulation in previous studies are investigated to determine what effect they may have on computing times. It is shown empirically that improved computing times may be obtained if these valid inequalities are, in fact, omitted from the resource ow-based formulation. In addition, a heuristic is proposed for the generation of initial starting solutions and for estimating the extent of the scheduling horizon which, in turn, is required to calculate the latest starting times of activities. The computational results are based on well-known problem test instances as well as new randomly generated problem instances. [ABSTRACT FROM AUTHOR]

Multiple factors have to be taken into account when mobile telecommunication network providers make decisions about radio transmitter placement. Generally, area coverage and the average signal level provided are of prime importance in these decisions. These criteria give rise to a bi-objective problem of facility location, with the goal of achieving an acceptable trade-off between maximising the total area coverage and maximising the average signal level provided to the demand region by a network of radio transmitters. This paper establishes a mathematical modelling framework, based on these two placement criteria, for evaluating the effectiveness of a given set of radio transmitter locations. In the framework, coverage is measured according to the degree of obstruction of the so-called ‘Fresnel zone’ that is formed between handset and base station, while signal strength is modelled taking radio wave propagation loss into account. This framework is used to formulate a novel bi-objective facility location model that may form the basis for decision support aimed at identifying high-quality transmitter location trade-off solutions for mobile telecommunication network providers. But it may also find application in various other contexts (such as radar, watchtower, or surveillance camera placement optimisation). [ABSTRACT FROM AUTHOR]

A scheduling problem in the colour printing industry is considered in this paper. The problem is to find an optimal assignment of print jobs to each of a set of colour printers, as well as an optimal processing sequence for the set of jobs assigned to each printer. The objective is to minimise the makespan of the schedule to achieve a suitable balance between the workloads of the printers and the efficiencies of the job sequences assigned to the printers. A novel aspect of the problem is the way in which the printer set-up times associated with the jobs are job sequence-dependent — it is possible to exploit commonalities between the colours required for successive jobs on each machine. We solve this problem approximately by using a simple heuristic and three well-known metaheuristics. Besides colour printing, the scheduling problem considered here admits many other applications. Some of these alternative applications are also briefly described. [ABSTRACT FROM AUTHOR]