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Operations Research Society of South Africa (ORSSA)en-USORiON2224-0004<p>The following license applies:</p><p><strong> Attribution CC BY</strong></p><p>This <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">license</a> lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation.</p>Editorial to Volume 35(2)
https://orion.journals.ac.za/pub/article/view/669
-SE Terblanche
Copyright (c) 2019 ORiON
2019-12-202019-12-20352iii10.5784/35-2-669Application of stochastic programming to electricity generation planning in South Africa
https://orion.journals.ac.za/pub/article/view/651
A two-stage stochastic programming model is used to solve the electricity generation planning problem in South Africa for the period 2013 to 2050, in an attempt to minimise expected cost. Costs considered are capital and running costs. Unknown future electricity demand is the source of uncertainty represented by four scenarios with equal probabilities. The results show that the main contributors for new capacity are coal, wind, hydro and gas/diesel. The minimum costs obtained by solving the two-stage stochastic programming models range from R2 201 billion to R3 094 billion.M BasheM Shuma-IwisiMA van Wyk
Copyright (c) 2019 ORiON
2019-12-202019-12-203528812510.5784/35-2-651Considering fairness in the load shedding scheduling problem
https://orion.journals.ac.za/pub/article/view/648
<p>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.</p>RG RakotonirainyI DurbachJ Nyirenda
Copyright (c) 2019 ORiON
2019-12-202019-12-2035212714410.5784/35-2-648New goodness-of-fit test for exponentiality based on a conditional moment characterisation
https://orion.journals.ac.za/pub/article/view/661
<p>The exponential distribution plays a key role in the practical application of reliability theory, survival analysis, engineering and queuing theory. These applications often rely on the underlying assumption that the observed data originate from an exponential distribution. In this paper, two new tests for exponentiality are proposed, which are based on a conditional second moment characterisation. The proposed tests are compared to various established tests for exponentiality by means of a simulation study where it is found that the new tests perform favourably relative to the existing tests. The tests are also applied to real-world data sets with independent and identically distributed data as well as to simulated data from a Cox proportional hazards model, to determine whether the residuals obtained from the fitted model follow a standard exponential distribution.</p>M SmutsJS AllisonL Santana
Copyright (c) 2019 ORiON
2019-12-202019-12-2035214516010.5784/35-2-661Picking location metrics for order batching on a unidirectional cyclical picking line
https://orion.journals.ac.za/pub/article/view/646
In this paper order batching is extended to a picking system with the layout of a unidirectional cyclical picking line. The objective is to minimise the walking distance of pickers in the picking line. The setup of the picking system under consideration is related to unidirectional carousel systems. Three order-to-route closeness metrics are introduced to approximate walking distance, since the orders will be batched before the pickers are routed. All metrics are based on the picking location describing when a picker has to stop at a location to collect the items for an order. These metrics comprise a number of stops, a number of non-identical stops and a stops ratio measurement. Besides exact solution approaches, four greedy heuristics as well as six metaheuristics are applied to combine similar orders in batches. All metrics are tested using real life data of 50 sample picking lines in a distribution centre of a prominent South African retailer. The capacity of the picking device is restricted, thus the maximum batch size of two orders per batch is allowed. The best combination of metric and solution approach is identified. A regression analysis supports the idea that the introduced metrics can be used to approximate walking distance. The combination of stops ratio metric and the greedy random heuristic generate the best results in terms of minimum number of total cycles traversed as well as computational time to find the solution.FM HofmannSE Visagie
Copyright (c) 2019 ORiON
2019-12-202019-12-2035216118610.5784/35-2-646