Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems

Variable neighbourhood search for job shop scheduling problems

Variable Neighbourhood Search (VNS) is one of the most recent metaheuristics used for problem solving in which a systematic change of neighbourhood within a local search is carried out. In this paper, an investigation on implementing VNS for job shop scheduling problems is carried out tackling benchmark suites collected from OR library. The idea is to build the best local search and shake operations based on neighbourhood structure available. The results are presented and compared with the recent approaches in the literature. It is concluded that the VNS algorithm can generally find better results. © 2006 ACADEMY PUBLISHER

Maintenance planning with prognostics for systems located in various places

Predictive maintenance has been attracting researchers and industry in recent years, since maintenance and repair of assets is one of the most contributing factors of operating & support cost. Predictive maintenance proposes to maintain the assets only when necessary aiming to reduce the unnecessary repair and maintenance by monitoring the health of the assets. The expected time of the failure is estimated by analyzing the monitored signals and remaining useful life of the asset before failure is used to plan, get prepared and perform the maintenance. When one team is responsible for maintenance of systems that are located in various places, the travel time between these systems should also be incorporated in the maintenance planning. Off shore wind farms and railway switches are two examples of these systems. This paper presents formulation of the problem that incorporates travel times between systems and prognostics information obtained from each system.This research was supported by the The Scientific and Technological Research Council of Turkey (TUBITAK) under Project 108M275