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Decisions for admission scheduling in hospitals are a class of optimization problems constrained by many factors. Instead of scheduling the admission of patients directly, this paper proposes a genetic algorithm (GA) designed for the optimization of a long-term admission strategy for the ophthalmology department in hospitals. For the optimization of admission strategy, we devise a coding scheme of strategies and define the objective functions for two objectives: efficiency and fairness. The proposed algorithm utilizes historical data of the hospital for evaluation of chromosomes. Experiments are conducted on several cases, and the strategy optimized by the proposed GA is compared with the first come first serve (FCFS) strategy and the greedy strategy. Experimental results show that strategies optimized by the proposed algorithm outperform FCFS and the greedy strategy.

Real coded genetic algorithms (RCGAs) have been widely studied and applied to deal with continuous optimization problems for years. However, how to improve the degree of accuracy so as to produce high quality solutions is still one of the main difficulties that RCGAs face with. This paper proposes a novel mutation scheme for RCGAs. The mutation operator is defined as a linear map in the space of chromosomes (in RCGAs each chromosome is a floating point vector). It operates on a whole chromosome instead of several single genes to produce the new chromosome. The linear map is represented by a randomly generated mapping matrix which satisfies some predefined constraints. By this way, the constraints restrict the mutations of genes on a same chromosome as a whole. RCGA with the proposed mutation scheme is tested on 16 benchmark functions. Results demonstrate that the proposed scheme not only improves the solution accuracy that RCGA can obtain, but also presents a very fast convergence speed. The linear map-based mutation scheme has a bright future to improve RCGAs.

Project scheduling under uncertainty is a challenging field of research that has attracted an increasing attention in recent years. While most existing studies only considered the classical single-mode project scheduling problem with makespan criterion under uncertainty, this paper aims to deal with a more realistic and complicated model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF). In the model, uncertainty is sourced from activity durations and costs, which are given by random variables. The objective is to find an optimal baseline schedule so that the project's expected net present value (NPV) of cash flows is maximized. In order to solve this intractable problem, an ant colony system (ACS) algorithm is designed. The algorithm dispatches a group of ants to build baseline schedules iteratively based on pheromones and an expected discounted cost (EDC) heuristic. In addition, because it is impossible to evaluate the expected NPVs of baseline schedules directly due to the presence of random variables, the algorithm adopts Monte Carlo (MC) simulations to evaluate the performance of baseline schedules. Experimental results on 33 instances demonstrate the effectiveness of the proposed scheduling model and the ACS approach.

Research into maximizing the network lifetime is one of the most significant and challenging areas in wireless sensor networks (WSNs). By arranging sensors and sinks to realize target coverage and network connectivity respectively, an efficient schedule of sensors and sinks can prolong the network lifetime. However, the arrangements of sensors and sinks correlate with each other because each sensor needs to send its data to a sink, making the problem of finding the optimal schedule difficult. Instead of using a single process to optimize the entire schedule of sensors and sinks, this paper proposes a scheduling method which uses two separate processes to schedule operations of sensors and sinks respectively. The first process organizes sensors in the network into disjoint sets, with each set being able to fully cover the targets. Based on the arrangement of sensors, a novel genetic algorithm (GA) is adopted in the second process to allocate sinks to each set of sensors. When the number of full cover sets that ensure both connectivity of sensors to sinks and connectivity of the network composed of sinks is maximized, a schedule that maximizes the network lifetime can be obtained. The proposed method has been applied to a number of WSN cases. Results demonstrate that the method is effective and efficient in prolonging the lifetime of WSNs.