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We present a multi-objective fuzzy-stochastic inventory model for the military.The model determines the minimum stock level and order quantity for a single item.We test the reliability of the model through simulation.The reliabilities of the model and two well known inventory models are compared.The comparison shows that the presented is model more reliable in extreme scenarios. Inventory management under uncertainty is a widely investigated field and many different types of inventory models have been used to address inventory problems in practice. However, a look at the literature reveals that few papers are devoted to inventory planning and management in environments characterised by uncertainty resulting from extreme events. In this paper a fuzzy-stochastic multi-objective modelling approach is used to address the problem of managing inventory in an environment characterised by uncertainty. The model is applied specifically to the military environment and determines the required stock level for a single item, based on three different scenarios. A numerical example is provided for the sake of illustration and the reliability of the model tested through simulation. The results are compared with those obtained from the well known ( r , Q ) and ( s , S ) inventory models in the literature. This comparison showed that the hybrid model presented in this paper is more reliable in extreme scenarios.

Environmental Diversity (ED) has been proposed as a potential biodiversity surrogate for use in spatial biodiversity planning. The concept is based on the premise that differences in species composition between areas are correlated with differences in environmental conditions. If this is true then sites selected to represent the full range of environmental conditions in an area should provide good coverage of species diversity. This is potentially significant for conservation planning because environmental data are more easily available while species distribution data are often incomplete, but empirical tests of ED have been inconclusive. We explored the value of ED as a biodiversity surrogate using species distribution data from South Africa, focusing on how implementation affects its performance. We used empirical distribution datasets for birds, butterflies, frogs, and reptiles as well as artificial species distributions and modeled distributions for butterflies. Our results show that the form of the underlying optimization model and the number of environmental variables have a significant effect on the performance of ED while the choice of heuristic procedures for selecting sites has no significant effect. We conclude that ED can perform well as a biodiversity surrogacy strategy if it is implemented correctly.

As transport modellers we are interested in capturing the behaviour of freight vehicles that includes the locations at which vehicles perform their activities, the duration of activities, how often these locations are visited, and the sequence in which they are visited. With disaggregated freight behaviour data being scarce, transport modellers have identified vehicle tracking and fleet management companies as ideal third party sources for GPS travel data. GPS data does not provide us with behavioural information, but allows us to infer and extract behavioural knowledge using a variety of processing techniques. Many researchers remain sceptical as specific human intervention, referred to as ‘expert knowledge’, is often required during the processing phase: each GPS data set has unique characteristics and requires unique processing techniques and validation to extract the necessary behavioural information. Although much of the GPS data processing is automated through algorithms, human scrutiny is required to decide what algorithmic parameters as considered ‘best’, or at least ‘good’. In this paper we investigate the repeatability and reproducibility (R&R) of a method that entails variable human intervention in processing GPS data. More specifically, the judgement made by an observer with domain expertise on what clustering parameters applied to GPS data best identify the facilities where commercial vehicles perform their activities. By studying repeatability we want to answer the question ‘if the same expert analyses the GPS data more than once, how similar are the outcomes?’, and with reproducibility we want to answer the question ‘if different experts analyse the same GPS data, how similar are their outcomes?’ We follow two approaches to quantify the R&R and conclude in both cases that the measurement system is accurate. The use of GPS data and the associated expert judgements can hence be applied with confidence in freight transport models.

After private cars, minibus taxis are the most common transport mode in South Africa. Especially for low-income citizens living in townships, minibus services are often the only possibility for mobility. Despite the great importance of the mode, there is very little knowledge of routes, fares, and the number of minibuses. Hence, it is difficult to simulate and to understand the influence of this mode on other modes and on transport planning in general. This article presents the development of the first ``close-to-reality'' minibus supply model based on demand and street network only. The approach adopts the survival-of-the-fittest principle, using a co-evolutionary algorithm that is integrated into a microscopic multi-agent simulation framework. The successful application of the approach to a large-scale, real-world scenario in the Nelson Mandela Bay Area Municipality in South Africa shows that it is able to identify the main minibus corridors as well as to find robust service coverage in lower-demand areas. The resulting minibus supply model can then be used for planning purposes (e.g., to investigate aspects of strategic, operational, or regulatory changes).

The paper describes the use of Global Positioning System (GPS) data obtained from commercial and project-specific sources to examine the travel behavior and fuel consumption patterns of drivers over a 3-day period in Gauteng Province, South Africa. Data for commercial (truck and light delivery vehicle) traffic were obtained from a commercial fleet management provider that continuously tracked the movements of 42,000 vehicles. Data for private car users came from a panel of 720 drivers, whose multiday driving activity was tracked by mobile passive GPS loggers. The driving behavior of the two driver populations was analyzed and compared in terms of the total distance traveled: spatial patterns (e.g., the amount of travel on different types of road) and temporal variations (e.g., variations between times of day and between multiple days). The detailed nature of the GPS data permitted the estimation of fuel consumption at a very disaggregate level (by link and time of day) and the identification of differences between user groups; these factors have significant implications for transport and energy policy. A new indicator, the recovery ratio, was introduced to assess the relationship between fuel use and distance traveled on different classes of road and to help identify equity distortions between user groups. The paper also discusses research needs related to the collection and integration of GPS data from multiple sources for model calibration and program evaluation.

We introduce complex network analysis and use a commercial vehicle’s observed trip as a proxy for a business relation between two facilities in its activity chain. We extract facility locations by applying density-based clustering to GPS data of commercial vehicle activities. The network among the facilities is then extracted by analysing the activity chains of more than 25,000 commercial vehicles. Centrality metrics prove useful and novel in identifying and locating key logistics players. Transport planners and decision makers can benefit from such an approach as it allows them to design more targeted initiatives and policy interventions.

The most essential and alluring characteristic of a security estate is the estate's ability to provide 24-h security to its residents, of which the continual patrolling of roads and paths is vital. The objective of this paper is to address the lack of sufficient patrol route design procedures by presenting a tabu search algorithm capable of generating multiple patrol routes for an estate's security guards. The paper shows that the problem of designing these routes can be modelled as an Arc Routing Problem, specifically as min–max k postmen problems. The algorithm is illustrated with a real problem instance from an estate in Gauteng, South Africa. The patrol routes generated by the algorithm provide a significant improvement in the even patrolling of the road network, and a more balanced work distribution among guards. The algorithm is also tested on several benchmark problems from literature.

To address the underreporting of freight from a transport geography point of view, we present a novel analysis of the time and spatial characteristics of disaggregated commercial vehicle activities. The activities were extracted from raw global positioning system (GPS) data collected in South Africa over a six-month period for more than 30,000 commercial vehicles. The analyses of the activity chains provide useful characteristics such as activity and chain durations, number of activities per chain, and the spatial extent of the activity chains. Key results indicate that about 60% of activity chains have between 5 and 15 activities per chain while 25% of the chains have 4 or less; 89% of the chains have a duration of 24 hours or less; and approximately 75% of all activities start between 08:00 and 17:00. The paper’s contribution is twofold: it firstly demonstrates a methodology to extract and evaluate vehicle activities and activity chains from raw GPS data. Novel results and characteristics about transport geographies in Gauteng, the economic centre of South Africa, are presented. We also report on the sensitivity of the analyses to certain parameters. Secondly, we introduce new metrics to evaluate a geographical area’s economic productivity based on commercial activity.

This paper is on fuzzy stochastic optimisation, an area that is quickly coming to the forefront of mathematical programming under uncertainty. An even stronger motivating factor for the growing interest in this area can be found in the ubiquitous nature of decision problems involving hybrid imprecision. More precisely, we consider a range of situations in which random factors and fuzzy information co-occur in an optimisation setting. Related hybrid optimisation models are discussed and converted into deterministic terms through appropriate tools like probabilistic set, uncertain probability, and fuzzy random variable, making good use of uncertainty principles. We also discuss ways to deal with the resulting problems. Numerical examples carried out using class optimisation software demonstrate the efficiency of the proposed approaches. We shall end this article by pointing out some of the challenges that currently occupy researchers in this emerging field.

The main thrust of this study is the operational scheduling of the continuous coal handling and blending processes when considering multiple, and sometimes conflicting, objectives. A widely applicable generic goal programming model is proposed. Furthermore, assumptions regarding the certainty of demand during different periods are challenged, endeavoring to provide more robust schedules in a largely stochastic environment. As the study aims to provide scheduling solutions to any coal handling facility, the Simulated Annealing metaheuristic is proposed to ensure that acceptably good solutions for large instances of the generic model can be found in reasonable computational time. The generic approach and its suggested application will be valuable not only in the coal handling environment, but also in the continuous product manufacturing/blending or continuous material handling environment.

South Africa is nearly 20 years into its democracy, yet the legacy of apartheid remains evident and the level of inequality steadily rising. Gauteng province, the economic heart of the country that includes Johannesburg and Tshwane, the capital, carries the burden of many of its formerly relocated citizens still living in poverty on the periphery of the large metropolitan areas. Whereas sprawl is commonly associated with low-density, more affluent development on the periphery, Gauteng finds itself with having to provide basic services and mobility to high density, low-income people on its outskirts. The mobility culture in Gauteng is heavily influenced by the socioeconomic disparity. In this chapter we revisit the legislative context that gave rise to racial segregation, and concern ourselves with the impact it had in the evolution of the now-dominant paratransit mode that accounts for more than two thirds of all commuter trips in Gauteng. Although often cited in literature and the media for its violent sectarian conflicts, the minibus taxis, as it is commonly referred to, is much more than a mere mode of transport. Outsiders often perceive the taxi industry to be a chaotic system, but it has evolved into a powerful economic industry with a unique mobility culture, most notably the hand signals—a silent gestural semiotic language in its own right—used by commuters to communicate their desired destinations to passing taxis. Aware of the rising inequality, and not fully understanding or appreciating the exibility it provides, government often perceives paratransit as a necessary nuisance that should be formalised. Improving mobility and accessibility, however, requires both settlement location and transport to be revisited. As such, paratransit may prove to a valuable solution, and not the mere nuisance it is often made out to be.

Reusable packaging often accumulates downstream in supply chains, resulting in nonrevenue-earning relocations. This paper presents a reliable optimisation model under supply and demand uncertainty. Instead of using deterministic forecasted values, we propose a fixed recourse stochastic program that embeds the approximated uncertainties within the decision model. Results do indeed prove to be more reliable, and thus useful to industry. We also investigate the model's sensitivity to the difference in weighting of inventory versus stock-out penalties. The valuable role of Operations Research in industry – and of optimisation, specifically – is affirmed. Though uncertainties cannot be eradicated, planned relocations can be done more reliably. OPSOMMING Herbruikbare verpakking bou gereeld aan die onderpunt van voerkanaalnetwerke op met nie-verdienste-genererende herskuiwings tot gevolg. Hierdie artikel bied ’n betroubare optimeringsmodel aan wat vraag- en aanbodonsekerheid aanspreek. In plaas van deterministiese vooruitskattings, stel ons ’n vaste verhaling stogastiese program voor waar die benaderde onsekerheid in die model self vasgevang word. Resultate is inderdaad meer betroubaar en dus bruikbaar vir industrie. Ons beskou ook die sensitiwiteit van voorraadteenoor vooraadlose strafgewigte. Die waardevolle rol van Operasionele Navorsing – en meer spesifiek, optimering – word bevestig. Alhoewel onsekerheid nie uitgewis kan word nie, kan meer betroubare herskuiwings beplan word.

Transportation Research Record, 2168 (1), ISSN:0361-1981, ISSN:2169-4052, ISBN:978-0-309-16037-7

ENGLISH ABSTRACT: It is reasonable to expect that preparation time is needed to ready a repair facility before a repair can be carried out. A three-unit system with a `preparation time’ for the repair facility is studied in this paper. The steady-state availability of such a system is obtained. The asymptotic confidence limits of the steady state availability are obtained numerically.AFRIKAANSE OPSOMMING: Redelikerwys kan verwag word dat ‘n herstelproses voorafgegaan word deur ‘n voorbereidingsproses voordat herstel ‘n aanvang kan neem. ‘n Sisteem wat bestaan uit drie eenhede waar ‘n sodanige voorbereidingsproses voor herstelwerk moet plaasvind word ondersoek. Die gestadigde stelselbeskikbaarheid met asimptotiese vertrouegrense word numeries blootgestel.

Past political regimes, poor urban planning, and extreme socio-economic imbalances have resulted in segregated disparate public transportation systems in the City of Tshwane. Public transport only captures 50% of the passenger transport market with modes competing due to its fragmented nature and the deregulated planning functions. The paper contributes to the public transport body of knowledge as it introduces South African particularities, and proposes innovative interventions to overcome transport planning challenges.

The integration of multiple constraints of the Vehicle Routing Problem (VRP) variants is computationally expensive. Although vehicle routing problems have been well researched, variants are typically treated in isolation, whereas industry requires integrated solutions. Solution algorithms are also tested using benchmark data that are questionable, and that do not represent typical applications. The paper proposes an approach that solves a problem by analyzing its environment through cluster analysis, chooses an appropriate solution strategy, and tests the results in an attempt to learn for the purposes of improved future decisions.

Modelling large-scale traffic flow systems at a disaggregate level can be data intensive as it requires extensive knowledge about the activities and activity chains of vehicles. This paper focuses on activity chain generation for commercial vehicles. We use a large sample of GPS records to extract a complex network and sample chain characteristics from. The paper makes a valuable contribution in both its methodology, and in its focus on intra and inter-provincial vehicle populations simultaneously. The simulated chains are validated in terms of vehicle kilometrekilometres travelled and its spatiotemporal accuracy, comparing favorably in both.