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With the increasing need to solve problems faster and with fewer resources, great emphasis is placed on optimisation. Many real-world problems require addressing more than one objective that are in conflict, as well as taking into consideration a number of practical restrictions or constraints. The multi-objective optimisation using the cross-entropy method (MOO CEM) algorithm is one of many algorithms that addresses the need to solve multi-objective problems effectively, but it has a number of limitations. This paper explores methods of enhancing the MOO CEM algorithm in order to improve the efficiency and increase the functionality of the algorithm, allowing for it to be applied to additional classes of problems. Three possible methods of enhancement were identified: using the beta distribution to improve sampling, adding functionality to solve constrained problems and, lastly, implementing a non-dominated sorting algorithm to solve problems with more than two objectives. The new algorithms incorporating these enhancements were developed and tested on benchmark problems. Subsequently, the results were analysed using standard performance indicators and compared to results produced by the original MOO CEM algorithm. The findings of this study indicate that using the beta distribution improves sampling and therefore algorithm efficiency. Methods of handling constraints and solving problems with an increased number of objectives were implemented successfully. Based on these results, a final algorithm implementing the enhancements is presented. [ABSTRACT FROM AUTHOR]

Sea minefields obstructing naval missions have to be breached with minimum effort and acceptable risk. With the advent of autonomous mapping, it is feasible to have high quality information of obstructing sea minefields and their environment before moving into them. The challenge remains to use this knowledge optimally to minimise risk, time and effort in crossing the sea minefield. To achieve this, two interlinked processes are required, consisting of finding the shortest route with acceptable risk or route with minimum risk through the sea minefield, and if no route of acceptable risk exists, removing the optimum combination of sea mines to create a route of acceptable risk. This paper describes the use of Dijkstra's Algorithm and a genetic algorithm to achieve practical strategies and a method in which two optimisation techniques interact to provide a safe route considering the risk of both the sea mine and the environment and making it applicable to sea mine avoidance. This methodology may readily be applied to general ship routing in risk areas and may be expanded to routing through any area where no known routes exist. [ABSTRACT FROM AUTHOR]

This paper gives an overview of a discrete-event simulation study that was performed on pallet movement at Fresh Produce Terminals in the port of Durban, South Africa. The study formed part of an extended study of the logistics infrastructure of the South African fresh fruit industry and its export supply chain. The focus in this paper is on pallet movement in the terminal and its requirement on the storage capacity of the cold store facility. Specifics pertaining to input data analysis are provided, as well as a discussion of simulation model validation and output data analysis. [ABSTRACT FROM AUTHOR]