One of the most challenging decisions that has to 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 such 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.
{"title":"Vehicle routing decision support for a local retailer","authors":"J. V. Vuuren, Aaron Shuttleworth","doi":"10.5784/37-1-691","DOIUrl":"https://doi.org/10.5784/37-1-691","url":null,"abstract":"One of the most challenging decisions that has to 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 such 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.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81732881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the automotive industry, a Body in White (BIW) refers to the first step, the basic structure, in the production of a vehicle. Once a BIW production line has been built, the (maximum) capacity is fixed and throughput is therefore limited by the equipment specified during the design phase. The main metric used to inform the production line design is the Net Ideal Cycle Time (NICT). Unfortunately, the state of practice to estimate the NICT is a basic heuristic that does not account for production variation. In this paper, we challenge the current estimation approach by proposing an alternative that assumes actual production to follow a Weibull distribution. The proposed model is derived and estimated from empirical data. The results suggest that BIW production lines have traditionally been designed with too low a capacity, resulting in planned throughput rarely being achieved. On the other hand, increasing the design capacity implies a higher initial investment. In this paper it is demonstrated that the higher investment required is offset by reduced losses, resulting in more reliable planning and returns.
在汽车工业中，白车身(Body In White，简称BIW)指的是汽车生产的第一步，即基本结构。一旦白车身生产线建成，(最大)产能是固定的，因此产量受到设计阶段指定设备的限制。用于通知生产线设计的主要指标是净理想周期时间(NICT)。不幸的是，估计NICT的实践状态是一种基本的启发式方法，不能解释生产变化。在本文中，我们通过提出一种替代方法来挑战当前的估计方法，该方法假设实际生产遵循威布尔分布。所提出的模型是根据经验数据推导和估计的。结果表明，传统的白车身生产线设计的产能太低，导致计划的产量很少实现。另一方面，增加设计能力意味着更高的初始投资。本文证明了较高的投资需要被减少的损失所抵消，从而产生更可靠的规划和回报。
{"title":"Estimating net ideal cycle time for body-in-white production lines","authors":"W. Grobler, D. Kotzé, J. Joubert","doi":"10.5784/37-1-683","DOIUrl":"https://doi.org/10.5784/37-1-683","url":null,"abstract":"In the automotive industry, a Body in White (BIW) refers to the first step, the basic structure, in the production of a vehicle. Once a BIW production line has been built, the (maximum) capacity is fixed and throughput is therefore limited by the equipment specified during the design phase. The main metric used to inform the production line design is the Net Ideal Cycle Time (NICT). Unfortunately, the state of practice to estimate the NICT is a basic heuristic that does not account for production variation. In this paper, we challenge the current estimation approach by proposing an alternative that assumes actual production to follow a Weibull distribution. The proposed model is derived and estimated from empirical data. The results suggest that BIW production lines have traditionally been designed with too low a capacity, resulting in planned throughput rarely being achieved. On the other hand, increasing the design capacity implies a higher initial investment. In this paper it is demonstrated that the higher investment required is offset by reduced losses, resulting in more reliable planning and returns.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80566919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Survival analysis has become a popular technique to more accurately model the probability of default in the credit risk environment with the ultimate goal of finding the optimal price for credit. In this paper we present an overview of some of the basic concepts of survival analysis. The focus is specifically on the Cox Proportional Hazards (CPH) model and the mixture cure model, which is a general alternative to the CPH model. A detailed algorithm that can be used to simulate survival times (default times) from a mixture cure model is provided. A parametric CPH and mixture cure model are fitted to a simulated data set and the benefits of fitting the latter model are illustrated.
{"title":"An overview of survival analysis with an application in the credit risk environment","authors":"M. Smuts, J. Allison","doi":"10.5784/36-2-690","DOIUrl":"https://doi.org/10.5784/36-2-690","url":null,"abstract":"Survival analysis has become a popular technique to more accurately model the probability of default in the credit risk environment with the ultimate goal of finding the optimal price for credit. In this paper we present an overview of some of the basic concepts of survival analysis. The focus is specifically on the Cox Proportional Hazards (CPH) model and the mixture cure model, which is a general alternative to the CPH model. A detailed algorithm that can be used to simulate survival times (default times) from a mixture cure model is provided. A parametric CPH and mixture cure model are fitted to a simulated data set and the benefits of fitting the latter model are illustrated.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"13 1","pages":"89-110"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78382343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with strip packing metaheuristic algorithm selection using data mining techniques. Given a set of solved strip packing problem instances, the relationship between the instance characteristics and algorithm performance is learned and is used to predict the best algorithms to solve a new set of unseen problem instances. A framework capable of modelling this relationship for an automated packing algorithm selection is proposed. The effectiveness of the proposed framework is evaluated in the context of a large set of strip packing problem instances and the state-of-the-art strip packing algorithms. The results suggest a 91% accuracy in correctly identifying the best algorithm for a given instance.
{"title":"A machine learning approach for automated strip packing algorithm selection","authors":"Rosephine G. Rakotonirainy","doi":"10.5784/36-2-686","DOIUrl":"https://doi.org/10.5784/36-2-686","url":null,"abstract":"This paper deals with strip packing metaheuristic algorithm selection using data mining techniques. Given a set of solved strip packing problem instances, the relationship between the instance characteristics and algorithm performance is learned and is used to predict the best algorithms to solve a new set of unseen problem instances. A framework capable of modelling this relationship for an automated packing algorithm selection is proposed. The effectiveness of the proposed framework is evaluated in the context of a large set of strip packing problem instances and the state-of-the-art strip packing algorithms. The results suggest a 91% accuracy in correctly identifying the best algorithm for a given instance.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"27 1","pages":"73-88"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78330962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In production lines, buffers function as a means to decouple stations, which reduce the effect that station failures and varying process times have on the complete line’s throughput. However, adding larger buffers can be costly, for example, in the automotive industry where it results in increased working capital. This manuscript addresses the buffer allocation problem (BAP), seeking the smallest total buffer size while meeting a prescribed throughput by employing a simulation-based optimisation approach. A Tabu Search algorithm searches the solution space for the optimal buffer configuration while a discrete event simulation model evaluates each configuration, accounting for the machine (un)reliability. Since the multiple simulations add a sizeable computational burden, our approach introduces a novel neighbourhood search mechanism, which borrows from the Theory of Constrains. Solving test sets available in the literature suggest that this approach is 18 times faster than prior Adaptive Tabu Search approaches for small problems, and more than five times faster for medium-sized problems.
{"title":"Solving the buffer allocation problem using simulation-based optimisation","authors":"J. Joubert, D. Kotzé","doi":"10.5784/36-2-684","DOIUrl":"https://doi.org/10.5784/36-2-684","url":null,"abstract":"In production lines, buffers function as a means to decouple stations, which reduce the effect that station failures and varying process times have on the complete line’s throughput. However, adding larger buffers can be costly, for example, in the automotive industry where it results in increased working capital. This manuscript addresses the buffer allocation problem (BAP), seeking the smallest total buffer size while meeting a prescribed throughput by employing a simulation-based optimisation approach. A Tabu Search algorithm searches the solution space for the optimal buffer configuration while a discrete event simulation model evaluates each configuration, accounting for the machine (un)reliability. Since the multiple simulations add a sizeable computational burden, our approach introduces a novel neighbourhood search mechanism, which borrows from the Theory of Constrains. Solving test sets available in the literature suggest that this approach is 18 times faster than prior Adaptive Tabu Search approaches for small problems, and more than five times faster for medium-sized problems.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80379856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scorecards are models used in credit risk modelling. These models segments a population into various so-called risk buckets" based on the risk characteristics of the individual clients. Once a scorecard has been developed, the credit provider typically prefers to keep this model in use for an extended period. As a result, it is important to test whether or not the model still ts the population. To this end, the hypothesis of population stability is tested; this hypothesis speci es that the current proportions of the population in the various risk buckets are the same as was the case at the point in time at which the scorecard was developed. In practice, this assumption is usually tested using a measure known as the population stability index (which corresponds to the asymmetric Kullback-Leibler discrepancy between discrete distributions) together with a well-known rule of thumb. This paper considers the statistical motivation for the use of the population stability index. Numerical examples are provided in order to demonstrate the e ect of the rule of thumb as well as other critical values. Although previous numerical studies relating to this statistic are available, the sample sizes are not realistic for the South African credit market. The paper demonstrates that the population stability index has little statisticalmerit as either a goodness-of- t statistic to test the hypothesis of population stability or as an intuitive discrepancy measure. As a result, a novel methodology for testing the mentioned hypothesis is proposed. This methodology includes a restatement of the hypothesis to specify a range of acceptable" deviations from the speci ed model. An alternative test statistic is also employed as discrepancy measure; this measure has the advantage of having a simple heuristic interpretation in the context of credit risk modelling. Key words: Goodness-of- t testing, hypothesis testing, population stability, risk analysis.
{"title":"On testing the hypothesis of population stability for credit risk scorecards","authors":"J. D. Pisanie, I. Visagie","doi":"10.5784/36-1-678","DOIUrl":"https://doi.org/10.5784/36-1-678","url":null,"abstract":"Scorecards are models used in credit risk modelling. These models segments a population into various so-called risk buckets\" based on the risk characteristics of the individual clients. Once a scorecard has been developed, the credit provider typically prefers to keep this model in use for an extended period. As a result, it is important to test whether or not the model still ts the population. To this end, the hypothesis of population stability is tested; this hypothesis speci es that the current proportions of the population in the various risk buckets are the same as was the case at the point in time at which the scorecard was developed. In practice, this assumption is usually tested using a measure known as the population stability index (which corresponds to the asymmetric Kullback-Leibler discrepancy between discrete distributions) together with a well-known rule of thumb. This paper considers the statistical motivation for the use of the population stability index. Numerical examples are provided in order to demonstrate the e\u000bect of the rule of thumb as well as other critical values. Although previous numerical studies relating to this statistic are available, the sample sizes are not realistic for the South African credit market. The paper demonstrates that the population stability index has little statisticalmerit as either a goodness-of- t statistic to test the hypothesis of population stability or as an intuitive discrepancy measure. As a result, a novel methodology for testing the mentioned hypothesis is proposed. This methodology includes a restatement of the hypothesis to specify a range of acceptable\" deviations from the speci ed model. An alternative test statistic is also employed as discrepancy measure; this measure has the advantage of having a simple heuristic interpretation in the context of credit risk modelling. \u0000Key words: Goodness-of- t testing, hypothesis testing, population stability, risk analysis.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78480034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 performanceis described. Subsequently, model results are analysed, synthesised and visualised in order to extract valuable insight from the data. The ndings of the study show that custom learning-based models signi cantly outperform both pre-trained and commercial tools in sentiment classi cation. Furthermore, the analysis approach is shown to yield actionable information that may inform decision making. Key words: Data mining, decision support systems, neural networks, sentiment analysis.
{"title":"Sentiment analysis of unstructured customer feedback for a retail bank","authors":"J. Kazmaier, JH van Vuuren","doi":"10.5784/36-1-668","DOIUrl":"https://doi.org/10.5784/36-1-668","url":null,"abstract":"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 performanceis described. Subsequently, model results are analysed, synthesised and visualised in order to extract valuable insight from the data. The ndings of the study show that custom learning-based models signi cantly outperform both pre-trained and commercial tools in sentiment classi cation. Furthermore, the analysis approach is shown to yield actionable information that may inform decision making. \u0000Key words: Data mining, decision support systems, neural networks, sentiment analysis.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"107 1","pages":"35-71"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80808697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, di erent univariate GARCH option pricing models are applied to the FTSE/JSE Top 40 index to determine the best performing model when modelling the implied South African Volatility Index (SAVI). Three di erent GARCH models (one symmetric and two asymmetric) are considered and three di erent log-likelihood functions are used in the model parameter estimation. Furthermore, the accuracy of each model is tested by comparing the GARCH implied SAVI to the historical SAVI. In addition, the pricing performance of each model is tested by comparing the GARCH implied price to market option prices. The empirical results indicate that the models incorporating asymmetric e ects outperform competing models in terms of pricing performance. Key words: Econometrics, nancial markets, pricing, stochastic processes.
{"title":"GARCH option pricing models in a South African equity context","authors":"PJ Venter, E. Maré","doi":"10.5784/36-1-676","DOIUrl":"https://doi.org/10.5784/36-1-676","url":null,"abstract":"In this paper, di\u000berent univariate GARCH option pricing models are applied to the FTSE/JSE Top 40 index to determine the best performing model when modelling the implied South African Volatility Index (SAVI). Three di\u000berent GARCH models (one symmetric and two asymmetric) are considered and three di\u000berent log-likelihood functions are used in the model parameter estimation. Furthermore, the accuracy of each model is tested by comparing the GARCH implied SAVI to the historical SAVI. In addition, the pricing performance of each model is tested by comparing the GARCH implied price to market option prices. The empirical results indicate that the models incorporating asymmetric e\u000bects outperform competing models in terms of pricing performance. \u0000Key words: Econometrics, nancial markets, pricing, stochastic processes.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"203 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81369210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Every day national power system networks provide thousands of MW of electric power from generating units to consumers, requiring different operations and planning to ensure secure systems. Where demand exceeds supply, load shedding - controlled, enforced reduction in supply - is necessary to prevent system collapse. Should load shedding need to be implemented, a planned schedule is necessary to allocate geographic areas on the required period of shedding. The problem of how to construct a schedule that fairly allocates load shedding responsibilities over geographic areas with minimum economic impacts is addressed in this paper. Two programming models are proposed. The first model consists of a linear integer programming model in which the objective is to minimise the economic cost subject to different fairness allocation constraints, while the second model involves formulation of the problem as a goal programming model in which different conflicting goals are simultaneously optimised. Several case studies are conducted in the context of a realistic, but hypothetical, scenario to explore the possible solutions that the proposed models provide. Results show that a fair schedule requires a high cost whereas lower cost can only be achieved with some sacrifices to the fairness of the schedule.
{"title":"Considering fairness in the load shedding scheduling problem","authors":"RG Rakotonirainy, I. Durbach, J. Nyirenda","doi":"10.5784/35-2-648","DOIUrl":"https://doi.org/10.5784/35-2-648","url":null,"abstract":"Every day national power system networks provide thousands of MW of electric power from generating units to consumers, requiring different operations and planning to ensure secure systems. Where demand exceeds supply, load shedding - controlled, enforced reduction in supply - is necessary to prevent system collapse. Should load shedding need to be implemented, a planned schedule is necessary to allocate geographic areas on the required period of shedding. The problem of how to construct a schedule that fairly allocates load shedding responsibilities over geographic areas with minimum economic impacts is addressed in this paper. Two programming models are proposed. The first model consists of a linear integer programming model in which the objective is to minimise the economic cost subject to different fairness allocation constraints, while the second model involves formulation of the problem as a goal programming model in which different conflicting goals are simultaneously optimised. Several case studies are conducted in the context of a realistic, but hypothetical, scenario to explore the possible solutions that the proposed models provide. Results show that a fair schedule requires a high cost whereas lower cost can only be achieved with some sacrifices to the fairness of the schedule.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87710488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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