The master thesis aims to design, implement, and compare algorithms that optimize processes in logistics, mainly in the planning phase. Heuristics and approximation genetic algorithms will find an near-optimal solution to NP-hard problem, such as the traveling salesman problem, with a delay less than several hours. The role of this algorithm is to plan an efficient route for garbage trucks that collect and distribute large-scale waste to waste yards in a specific city. The goal of the optimization is to minimize the shipping costs.

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This thesis describes the role of cost effective maintenance in achieving competitive advantages. It explores by means of a survey which maintenance practices are used, and how maintenance policies are selected in Swedish industries. Also, it suggests a model for selecting the most cost effective maintenance policy, and how to improve the effectiveness of condition based maintenance decision-making. Finally it discusses how to assess the impact of maintenance practices on business strategic objectives. The main results achieved in the thesis are 1) A better understanding of maintenance organisation, management, systems and maintenance status in Swedish industry. For example, it was found that about 70% of Swedish companies still consider maintenance as a cost centre. Preventive and predictive maintenance approaches are also emphasised. 2) Most Swedish firms, i.e. about 81%, use the accumulated knowledge and experience within the company as a method for maintenance selection. Besides, about 31% use a method based on modelling the time to failure and optimisation. About 10% use failure mode effect and criticality analysis (FMECA) and decision trees and only 2% use multiple criterion decision-making (MCDM). However, the most used maintenance selection method is not the one most satisfactory to its users. Furthermore, about 30% use a combination of at least two methods. 3) A practical model for selecting and improving the most cost effective maintenance policy was developed. It is characterised by incorporating all the strengths of the four methods used in industry. 4) A mechanistic model for predicting the value of vibration level was verified both at the lab and in a case study. 5) A model for identifying, assessing, monitoring and improving the economic impact of maintenance was developed and tested in a case study. Thus it was proved that maintenance is no longer a cost centre, but could be a profit-generating function. To achieve competitive advantages, companies should do the right thing, e.g. use the most cost effective maintenance policy, and they should do it right, e.g. ensure that they have the right competence. Furthermore, they should apply the never-ending improvement cycle, i.e. Plan-Do-Check-Act, which requires identifying problem areas by assessing the savings and profits generated by maintenance and monitoring the economic impact of the applied maintenance policy. Thus, they would know where investments should be allocated to eliminate the basic reasons for losses and increase savings. The major conclusion is that proper maintenance would improve the quality, efficiency and effectiveness of production systems, and hence enhances company competitiveness, i.e. productivity and value advantages, and long-term profitability.

Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2006. / The Rural Aquaculture Development Programme (RADP) of the Division of Aquaculture at Stellenbosch University (DASU) has the objective of socio-economic development of farm workers and rural communities from previously disadvantaged communities through the provision of opportunities for sustainable economic development by establishing small-scale fish-farming systems on irrigation dams. The Small-Scale Aquaculture Farming Systems (SSAFS) is an initiative of RADP. DASU intends implementing 20 new projects by the end of 2005, 100 by 2010 and at least 200 by 2020. Thus far, sites which can accommodate 25 projects have been identified. The owners of the land on which these 25 possible projects are located are being contacted to gain permission to use the sites for the SSAFS. It is possible that some of the owners of the sites will not allow them to be used so that not all 25 projects will necessarily be implemented. At present the predominant search method employed by DASU to find new sites involves two steps. Initially knowledge is acquired through word of mouth about dams that may be suitable. Personnel then drive to these dams for further inspection, to see if they are indeed suitable. This method of search is clearly both expensive and time-consuming. Geographical information systems (GIS) have as yet not been considered as an alternative and/or supplementary site selection method for the SSAFS. GIS tools and principles can reduce both expenses and time in locating sites by reducing the number of dams to be visited to only those that are optimal (i.e. they present the least risk of the SSAFS projects failing). A new site selection methodology using GIS was developed which is faster and less expensive than existing site selection methods. The GIS site selection methodology revolves around the isolation and selection of dams in the Berg and Breede River water management areas, based on their ability to fulfil certain parameters of critical variables comprising dam dimensions, environmental issues, distance measures and site security, according to a priority hierarchy, that determine a successful site for trout farming according to methods employed by the SSAFS. Once this methodology was produced, it was presented to five people who are directly involved with the SSAFS as well as four others who are not involved with the SSAFS but who might find the methodology useful, to determine whether they hold that the methodology is indeed better than existing search methodologies. Those who were presented the GIS site selection methodology felt that it is very useful and that it could be employed in future to make more informed choices when locating new sites for the SSAFS as well as other aquacultural enterprises.

A pragmatic method for selecting components and devices from a database or parameterized models is developed in this thesis. The quantitative framework presented here is sufficiently general to accommodate an entire device assembly, a component, or a sub-assembly. The details pertaining to a device or a component are classified into three sets of variables: (i) user-specifications, s (ii) device parameters, p , and (iii) device characteristics, c . Functional, practical, and performance-related attributes that a user can provide comprise user-specifications. Since, most often, a specification cannot be specified as a single number, we allow the user to enter a range with lower and upper bounds. Device parameters comprise the geometry and material properties, and device characteristics include functional requirements and performance criteria. Thus, for a device, all its functional and utility attributes are contained in the union of sets s and c , whereas the geometry and the material properties are in set p . The equations governing the physical behavior of the device are written in terms of s , p , and c . These equations may sometimes be readily available; when they are not, it may be necessary to formulate them as required. By solving the governing equations along with the inequalities that arise from the lower and upper bounds on s , we obtain feasible ranges on p and c . Then, for any pair of device characteristics, a 2D feasible map is drawn, to visually portray the consequences of user-specifications. If the feasible map is null, small, or large, it indicates that the user-specifications are infeasible, stringent, or there is much scope for design, respectively. This can be inferred even before the designs are considered. Juxtaposed on the feasible map are points or lozenges corresponding to the quantitative attributes of the entries in the database. The ones that lie inside the feasible map can be reckoned as meeting the user-specifications and thus, enabling selection. On the other hand, if there is no database or none of the devices in the database lie inside the feasible map, we can identify the feasible ranges of all the design parameters for every point inside the feasible map. This information is useful to the designer to redesign and arrive at feasible designs by using parameterized models of the device. A Graphical User Interface (GUI) is developed to facilitate the user-interaction. The utility of the selection framework is demonstrated with a variety of case-studies including miniature pumps, heat pulse-based soil-moisture sensors, springs, flywheels, compliant mechanisms, micromechanical suspensions, etc. The latter two use kineto-elastic characteristics of deformable components. The framework, when used for materials selection, can be seen as an extension of Ashby’s materials selection method. This is also illustrated with two examples.