Constructing operating theatre schedules using partitioned graph colouring techniques

In hospitals, scheduled operations can often be cancelled in large numbers due to the unavailability of beds for post-operation recovery. Operating theatre scheduling is known to be an (Formula presented.) -hard optimisation problem. Previous studies have shown that the correct scheduling of surgical procedures can have a positive impact on the availability of beds in hospital wards, thereby allowing a reduction in number of elective operation cancellations. This study proposes an exact technique based on the partitioned graph colouring problem for constructing optimal master surgery schedules, with the goal of minimising the number of cancellations. The resultant schedules are then simulated in order to measure how well they cope with the stochastic nature of patient arrivals. Our results show that the utilisation of post-operative beds can be increased, whilst the number of cancellations can be decreased, which may ultimately lead to greater patient throughput and reduced waiting times. A scenario-based model has also been employed to integrate the stochastic-nature associated with the bed requirements into the optimisation process. The results indicate that the proposed model can lead to more robust solutions

A generalized net with an ACO-algorithm optimization component

In the paper we describe a generalized net G ACOA realizing an arbitrary algorithms for ant colony optimization. In this sense, this net is universal for all standard algorithms for ant colony optimization, since it describes the way of functioning and results of their work. Then, we discuss the way of constructing a GN that includes the G ACOA as a subnet. In this way, we ensure the generalized net tokens’ optimal transfer with regard to the results of G ACOA . Thus, we construct a generalized net, featuring an optimization component and thus optimally functioning

New evaluations of ant colony optimization start nodes

Ant Colony Optimization (ACO) is a stochastic search method that mimics the social behavior of real ant colonies, managing to establish the shortest route to the feeding sources and back. Such algorithms have been developed to arrive at near-optimal solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. In this paper, the semi-random start procedure is applied. A new kind of evaluation of start nodes of the ants is developed and several starting strategies are prepared and combined. The idea of semi-random start is related to a better management of the ants. This new technique is tested on the Multiple Knapsack Problem (MKP). A Comparison among the strategies applied is presented in terms of quality of the results. A comparison is also carried out between the new evaluation and the existing one. Based on this comparative analysis, the performance of the algorithm is discussed. The study presents the idea that should be beneficial to both practitioners and researchers involved in solving optimization problems