Gully pots or storm drains are located at the side of roads to provide drainage for surface water. We consider gully pot maintenance as a risk-driven maintenance problem. Our simulation considers the risk impact of gully pot failure and its failure behaviour. In this paper, we focus on two factors, the issue of parked cars and up-to-date gully pots status information, that may affect the scheduling of maintenance actions. The aim is to discover potential investment directions and management policies that will improve the efficiency of maintenance. We find that the "untimely system status information" is a dominant factor that weakens the current maintenance. Low-cost sensor technique could be a good development.
Abstract. In this paper we study a complex real world workforce scheduling problem. We apply constructive search and variable neighbourhood search (VNS) metaheuristics and enhance these methods by using a variable fitness function. The variable fitness function (VFF) uses an evolutionary approach to evolve weights for each of the (multiple) objectives. The variable fitness function can potentially enhance any search based optimisation heuristic where multiple objectives can be defined through evolutionary changes in the search direction. We show that the VFF significantly improves performance of constructive and VNS approaches on training problems, and "learn" problem features which enhance the performance on unseen test problem instances.
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